High-quality multilingual text-to-speech model by Inworld AI with 130+ preset voices across 15 languages. Supports voice cloning, word-level timestamps, and streaming. Optimized for natural, expressive speech with <250ms time-to-first-audio.

inworld-tts-1.5-max

Fast multilingual text-to-speech model by Inworld AI with 130+ preset voices across 15 languages. Supports voice cloning, word-level timestamps, and streaming. Optimized for low-latency applications with <130ms time-to-first-audio.

inworld-tts-1.5-mini

NVIDIA Nemotron 3 Super is a hybrid Mixture-of-Experts (MoE) model engineered for highest compute efficiency and accuracy in multi-agent applications and specialized agentic systems. It is optimized to run many collaborating agents per application on a single GPU, delivering high accuracy for reasoning, tool use, and instruction following.

NVIDIA-Nemotron-3-Super-120B-A12B

GLM-5 is an advanced, open-source large language model designed for developers tackling the toughest challenges. It excels at long-context reasoning, multi-step tool orchestration, and complex systems engineering, making it the ideal choice for powering sophisticated agents and applications that require high-level cognitive tasks.

GLM-5

  Qwen3-TTS is an advanced text-to-speech model by Alibaba's Qwen team, delivering stable, expressive, and low-latency speech generation across 10 languages.                                                                                                                                                                                                                                                                                                                                           Key capabilities:                                                                                                                                                                                                                                  - 9 preset voices — Vivian, Serena, Uncle_Fu, Dylan, Eric, Ryan, Aiden, Ono_Anna, Sohee — covering diverse genders, ages, and accents                                                                                                              - Voice cloning — clone any voice from a short (~3s) audio sample via the voice_id parameter   - Instruction control — adjust tone, emotion, and speaking style with natural language (e.g. "speak slowly and calmly", "excited tone")   - 10 languages — English, Chinese, Japanese, Korean, German, French, Russian, Spanish, Italian, Portuguese   - Streaming support — real-time PCM streaming with ~97ms first-byte latency   - Multiple output formats — WAV, MP3, FLAC, PCM    Built on a 1.7B parameter architecture using discrete multi-codebook language modeling for end-to-end speech synthesis without cascading errors. Uses a custom 12Hz acoustic tokenizer that preserves paralinguistic information and   environmental audio details.

Qwen3-TTS

● Qwen3-TTS-VoiceDesign is a voice design variant of Qwen3-TTS by Alibaba's Qwen team. Instead of selecting from preset voices, you describe the voice you want in natural language — and the model generates speech in that voice.                                                                                                                                                                                                                                                                     Key capabilities:                                                                                                                                                                                                                                  - Natural language voice control — describe any voice with free text (e.g. "a deep male voice with a calm, authoritative presence", "a young cheerful female with a warm and friendly tone")   - 10 languages — English, Chinese, Japanese, Korean, German, French, Russian, Spanish, Italian, Portuguese                                                                                                                                         - Streaming support — real-time PCM streaming   - Multiple output formats — WAV, MP3, FLAC, PCM    Built on the same 1.7B parameter architecture as Qwen3-TTS, using discrete multi-codebook language modeling and a custom 12Hz acoustic tokenizer for high-quality end-to-end speech synthesis.

Qwen3-TTS-VoiceDesign

MiniMax M2.5 is SOTA in coding, agentic tool use and search, office work, and a range of other economically valuable tasks, boasting scores of 80.2% in SWE-Bench Verified, 51.3% in Multi-SWE-Bench, and 76.3% in BrowseComp (with context management).

MiniMax-M2.5

The latest flagship model in the Qwen family. State-of-the-art results across a comprehensive suite of benchmarks — including knowledge, reasoning, coding, instruction following, human preference alignment, agent tasks, and multilingual understanding.

Qwen3-Max

The latest flagship reasoning model in the Qwen3 family. Further enhanced by multiple innovations like adaptive tool-use and advanced test-time scaling techniques

Qwen3-Max-Thinking

Kimi K2.5 is an open-source, native multimodal agentic model built through continual pretraining on approximately 15 trillion mixed visual and text tokens atop Kimi-K2-Base. It seamlessly integrates vision and language understanding with advanced agentic capabilities, instant and thinking modes, as well as conversational and agentic paradigms.

Kimi-K2.5

GLM-4.7-Flash is a 30B-A3B MoE model. As the strongest model in the 30B class, GLM-4.7-Flash offers a new option for lightweight deployment that balances performance and efficiency.

GLM-4.7-Flash

NVIDIA Nemotron 3 Nano is an open small reasoning model optimized for fast, cost-efficient inference in agentic and production workloads. Built with a hybrid Mixture-of-Experts (MoE) and Mamba-Transformer architecture, it delivers strong multi-step reasoning, high token throughput, stable latency with predictable cost, and efficient deployment for agent-based systems. Designed for real-world AI systems where reasoning can generate significantly more tokens per prompt, Nemotron Nano reduces compute cost while maintaining strong reasoning quality.

Nemotron-3-Nano-30B-A3B

DeepSeek-V3.2 is a large language model designed to harmonize high computational efficiency with strong reasoning and agentic tool-use performance. It introduces DeepSeek Sparse Attention (DSA), a fine-grained sparse attention mechanism that reduces training and inference cost while preserving quality in long-context scenarios. A scalable reinforcement learning post-training framework further improves reasoning, with reported performance in the GPT-5 class, and the model has demonstrated gold-medal results on the 2025 IMO and IOI. V3.2 also uses a large-scale agentic task synthesis pipeline to better integrate reasoning into tool-use settings, boosting compliance and generalization in interactive environments.

DeepSeek-V3.2

Chatterbox is a family of three state-of-the-art, open-source text-to-speech models by Resemble AI.  We are excited to introduce Chatterbox-Turbo, our most efficient model yet. Built on a streamlined 350M parameter architecture, Turbo delivers high-quality speech with less compute and VRAM than our previous models. We have also distilled the speech-token-to-mel decoder, previously a bottleneck, reducing generation from 10 steps to just one, while retaining high-fidelity audio output.  Paralinguistic tags are now native to the Turbo model, allowing you to use [cough], [laugh], [chuckle], and more to add distinct realism. While Turbo was built primarily for low-latency voice agents, it excels at narration and creative workflows.  If you like the model but need to scale or tune it for higher accuracy, check out our competitively priced TTS service (link).

chatterbox-turbo

The fastest model of the Flux 2 family. Frontier visual intelligence — state-of-the-art image generation and editing from Black Forest Labs

FLUX-2-klein-4b

The best quality-to-latency ratio, production apps model of the Flux 2 family. Frontier visual intelligence — state-of-the-art image generation and editing from Black Forest Labs

FLUX-2-klein-9b

Developed by Anthropic, Claude is a family of highly performant, trustworthy AI models built for complex reasoning, advanced coding, and nuanced language understanding. The latest Claude 4 generation delivers breakthrough capabilities in analytical thinking, with Claude 4 Opus setting new standards for intelligence and Claude 4 Sonnet providing exceptional performance with remarkable efficiency.

Claude models excel at understanding context, following complex instructions, and maintaining coherent conversations across extended interactions. With advanced features like extended thinking for deeper reasoning, prompt caching that reduces costs by up to 90%, vision capabilities for image analysis, and robust safety measures, Claude is designed for enterprise applications that demand both sophistication and reliability.

Available with comprehensive API features including streaming responses, batch processing for 50% cost savings, multilingual support across dozens of languages, and flexible context windows up to 200K tokens (1M in beta), Claude is perfect for building intelligent applications like customer support agents, content analysis systems, coding assistants, and complex reasoning workflows that require both accuracy and trustworthiness.

Claude AI family: Claude 4 Opus for complex reasoning, Claude 4 Sonnet for balanced performance, plus advanced capabilities like extended thinking, prompt caching, vision analysis, and enterprise-grade safety APIs.

Claude AI APIs via DeepInfra

claude

Developed by Anthropic, Claude is a family of highly performant, trustworthy AI models built for complex reasoning, advanced coding, and nuanced language understanding

DeepInfra provides access to Anthropic's latest Claude models, featuring the most advanced reasoning capabilities and balanced performance options, all with enterprise-grade safety and reliability.

Claude

DeepSeek develops advanced foundation models optimized for computational efficiency and strong generalization across diverse tasks. The architecture incorporates recent advances in transformer-based systems, delivering robust performance in both zero-shot and fine-tuned scenarios. Models are pretrained on rigorously filtered multilingual corpora with specialized optimizations for mathematical reasoning and algorithmic tasks. The inference stack achieves competitive throughput while maintaining low latency, making it suitable for production deployment. Researchers and engineers can leverage these models for tasks ranging from natural language processing to complex analytical problem-solving.

deepseek

DeepSeek's models are a suite of advanced AI systems that prioritize efficiency, scalability, and real-world applicability.

DeepSeek

Developed by Black Forest Labs (the original creators behind Stable Diffusion), Flux is a family of state-of-the-art image generation and editing models that deliver exceptional visual quality with breakthrough prompt accuracy and photorealism. Built on advanced 12 billion parameter architecture, Flux models excel at understanding exactly what you want to create or modify.
FLUX 2 represents the next generation of the Flux family, introducing significantly improved image quality, faster generation, and more precise prompt following. The lineup includes Max for ultimate quality, Pro for production workflows, and Klein variants (9B and 4B) optimized for speed and efficiency — making high-quality image generation accessible at every scale.
Flux offers specialized variants for every need: from open-source to commercial licensing, Flux is perfect for developers building creative applications, product visualization tools, and next-generation image editing experiences.

Flux AI image generation family: FLUX.1 Kontext for in-context editing, FLUX.1 Pro/Dev for text-to-image synthesis, plus comprehensive editing tools and state-of-the-art visual generation APIs.

Flux Image Generation APIs via DeepInfra

flux

Developed by Black Forest Labs, Flux is a family of state-of-the-art image generation and editing models that deliver exceptional visual quality with breakthrough prompt accuracy and photorealism.

DeepInfra provides access to Black Forest Labs' complete Flux ecosystem, offering everything from lightning-fast generation to sophisticated in-context editing capabilities with industry-leading prompt adherence and visual quality.

Flux

Developed by Google DeepMind, Gemini is a family of state-of-the-art thinking models with native multimodal capabilities, designed for advanced reasoning, complex problem-solving, and comprehensive understanding across text, audio, video, and images. Built with revolutionary thinking architecture, Gemini models reason through problems step-by-step before responding, delivering enhanced accuracy and performance for sophisticated applications.

Gemini 2.5 Pro sets new standards for complex reasoning and coding excellence, while Gemini 2.5 Flash provides optimal price-performance for high-volume tasks. With massive context windows up to 1 million tokens, native multimodal processing that handles hours of video and audio, and transparent reasoning capabilities that show step-by-step thinking processes, Gemini excels at document analysis, code generation, scientific research, and agentic workflows.

Perfect for building intelligent applications that require deep reasoning, multimodal understanding, long-context processing, and transparent AI decision-making with Google's enterprise-grade reliability and performance.


Gemini AI family: Advanced thinking models with native multimodal processing for text, audio, video, and image understanding APIs

Gemini AI Model APIs via DeepInfra

gemini

Developed by Google DeepMind, Gemini is a family of state-of-the-art thinking models with native multimodal capabilities

DeepInfra provides access to Google's latest Gemini models, featuring advanced thinking capabilities, native multimodal processing, and industry-leading performance for complex reasoning and development tasks.

Gemini

Developed by Meta, Llama (Large Language Model Meta AI) is a family of state-of-the-art open-weight models designed for efficiency and performance. The latest versions feature Mixture-of-Experts (MoE) architectures, enabling cost-effective inference by dynamically activating subsets of parameters. With support for multimodal inputs (text + images) and extended context windows (up to 10M tokens), Llama excels in tasks like code generation, multilingual understanding, and long-form reasoning. The models support FP8 quantization and batch inference, ensuring low-latency, high-throughput performance for production workloads. With permissive licensing and robust tooling (e.g., Llama Guard for safety), Llama is ideal for developers seeking powerful, customizable AI with minimal overhead.

llama

The Llama 4 collection of models are natively multimodal AI models that enable text and multimodal experiences.

Llama 4

Meta Llama 3 are a collection of pretrained and instruction tuned generative text models in 8B, 70B and 405B sizes.

Llama 3

Llama

Developed by Mistral AI, a leading French research lab, Mistral is a family of open-source AI models built for multilingual excellence, advanced reasoning, and cost-effective performance. These models excel at complex reasoning, mathematics, coding, and specialized tasks while offering complete transparency and deployment freedom through open-source licensing.

Mistral Small 3.2 delivers breakthrough efficiency with native fluency in European languages, while specialized variants handle specific needs: Devstral for coding, Voxtral for audio processing, and Mixtral for high-performance tasks. With Apache 2.0 licensing, extensive context windows up to 128K tokens, and comprehensive customization options, Mistral provides enterprise-grade capabilities without vendor lock-in.

Perfect for building multilingual applications, coding assistants, and reasoning systems where you need both powerful performance and complete control over your AI deployment.

Mistral AI model family: Mistral Small 3.2 for efficient performance, Mixtral for specialized tasks, Devstral for coding, plus multilingual reasoning, mathematics, and open-source flexibility APIs.

Mistral AI Model APIs via DeepInfra

mistral

Developed by Mistral AI, a leading French research lab, Mistral is a family of open-source AI models built for multilingual excellence, advanced reasoning, and cost-effective performance

DeepInfra provides access to Mistral AI's comprehensive open-source model ecosystem, from efficient small models to specialized coding and audio processing variants, all with complete Apache 2.0 licensing freedom.

Mistral

Voxtral is a family of audio models with state-of-the-art speech to text capabilities.

Voxtral

[NVIDIA Nemotron<sup style='font-size:0.6em'>TM</sup>](https://www.nvidia.com/en-us/ai-data-science/foundation-models/nemotron/) is a family of open models, datasets, and training recipes engineered for high performance, efficiency and customization. Nemotron models support synthetic data workflows and supervised fine-tuning — and are equally optimized for real-time inference, reasoning agents, and production AI systems.

nemotron

NVIDIA Nemotron is a family of open models customized for efficiency, accuracy, and specialized workloads.

The Nemotron family spans Nano, Super, and specialized instruct variants, enabling you to balance accuracy, reasoning depth, latency, and cost for your specific workload.
- **Nano** for maximum efficiency and stable inference
- **Super** for multi-agent systems and advance reasoning
- **Instruct variants** for instruction-following and conversational workloads

Nemotron

Developed by Alibaba Group's Qwen Team, Qwen is a family of state-of-the-art large language and multimodal models designed for comprehensive AI capabilities and multilingual performance. The latest Qwen3 generation features balanced model architectures including reintroduced Mixture-of-Experts (MoE) variants (Qwen3-30B-A3B and Qwen3-235B-A22B) alongside dense models up to 32B parameters, enabling efficient resource utilization through dynamic parameter activation. 

With support for 119 languages and dialects, hybrid thinking modes that seamlessly alternate between reasoning and instruction-following without model switching, and extended context windows (up to 1M tokens in Qwen3-2507), Qwen excels in tasks like multilingual understanding, code generation, agentic workflows, and complex problem-solving. The models utilize advanced Byte-level Byte Pair Encoding with a 151,646-token vocabulary, structured ChatML formatting for conversational interactions, and robust tool calling capabilities with parallel execution support. 

Available in both proprietary and open-weight versions with flexible licensing, comprehensive model variants (Base, Instruct, Thinking, and hybrid modes), and enhanced Model Context Protocol support, Qwen is ideal for developers seeking powerful, multilingual AI systems with sophisticated reasoning capabilities and minimal deployment complexity.

Qwen AI model family: Qwen3 language models, specialized coding & reasoning models, plus state-of-the-art embedding & reranking APIs for search and RAG applications.

Qwen Model APIs via DeepInfra

qwen

Qwen series offers a comprehensive suite of dense and mixture-of-experts models.

DeepInfra provides access to Qwen's latest generation of large language models, offering both specialized coding models and general-purpose AI systems with advanced reasoning capabilities.

Qwen

Meet Qwen3-VL — the most powerful vision-language model in the Qwen series to date.  This generation delivers comprehensive upgrades across the board: superior text understanding & generation, deeper visual perception & reasoning, extended context length, enhanced spatial and video dynamics comprehension, and stronger agent interaction capabilities.

Bria Expand expands images beyond their borders in high quality. Resizing the image by generating new pixels to expand to the desired aspect ratio. Trained exclusively on licensed data for safe and risk-free commercial use.

Llama-3.1-Nemotron-70B-Instruct is a large language model customized by NVIDIA to improve the helpfulness of LLM generated responses to user queries. This model reaches Arena Hard of 85.0, AlpacaEval 2 LC of 57.6 and GPT-4-Turbo MT-Bench of 8.98, which are known to be predictive of LMSys Chatbot Arena Elo.  As of 16th Oct 2024, this model is #1 on all three automatic alignment benchmarks (verified tab for AlpacaEval 2 LC), edging out strong frontier models such as GPT-4o and Claude 3.5 Sonnet.

L3.3-70B-Euryale-v2.3 is a model focused on creative roleplay from Sao10k

olmOCR is a specialized AI tool that converts PDF documents into clean, structured text while preserving important formatting and layout information. What makes olmOCR particularly valuable for developers is its ability to handle challenging PDFs that traditional OCR tools struggle with—including complex layouts, poor-quality scans, handwritten text, and documents with mixed content types. Built on a fine-tuned 7B vision-language model, olmOCR provides enterprise-grade PDF processing at a fraction of the cost of proprietary solutions.

We introduce StarCoder2-15B-Instruct-v0.1, the very first entirely self-aligned code Large Language Model (LLM) trained with a fully permissive and transparent pipeline. Our open-source pipeline uses StarCoder2-15B to generate thousands of instruction-response pairs, which are then used to fine-tune StarCoder-15B itself without any human annotations or distilled data from huge and proprietary LLMs.

A sentence transformation model that has been trained on a wide range of datasets, including but not limited to S2ORC, WikiAnwers, PAQ, Stack Exchange, and Yahoo! Answers. Our model can be used for various NLP tasks such as clustering, sentiment analysis, and question answering.

ClarityAI/flux integrates the Flux AI model into the upscaling process, enabling high-resolution enhancements with superior face preservation and support for LoRAs to apply specific styles or identities.

Text Embeddings by Weakly-Supervised Contrastive Pre-training. Model has 24 layers and 1024 out dim. 

Euryale 3.1 - 70B v2.2 is a model focused on creative roleplay from Sao10k

Kimi K2 Thinking is the latest, most capable version of open-source thinking model developed by MoonshotAI

Bria Blur Background softens and de-emphasizes image backgrounds while keeping the subject sharp and clear for professional-quality results. Trained fully on licensed data, it delivers safe, natural, and commercial-ready outputs.

Automatically identify and segment foreground objects across video frames and generate a mask. No prompts, just a video.

Turn any prompt into a smooth video. Intelligent shot scheduling supports multi-shot storytelling, generating multi-shot narrative videos with consistent subjects, scenes, and atmosphere

The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. Building upon the dense foundational models of the Qwen3 series, it provides a comprehensive range of text embeddings and reranking models in various sizes (0.6B, 4B, and 8B).

Llama Guard 3 is a Llama-3.1-8B pretrained model, fine-tuned for content safety classification. Similar to previous versions, it can be used to classify content in both LLM inputs (prompt classification) and in LLM responses (response classification). It acts as an LLM – it generates text in its output that indicates whether a given prompt or response is safe or unsafe, and if unsafe, it also lists the content categories violated.

Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support

Bria 3.2 is the next-generation commercial-ready text-to-image model. With just 4 billion parameters, it provides exceptional aesthetics and text rendering, evaluated to be on par to leading open-source models, and outperforming other licensed models.

LLaMa 2 is a collections of LLMs trained by Meta. This is the 70B chat optimized version. This endpoint has per token pricing.

Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 7B fine-tuned model, optimized for dialogue use cases and converted for the Hugging Face Transformers format. 

The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. Building upon the dense foundational models of the Qwen3 series, it provides a comprehensive range of text embeddings and reranking models in various sizes (0.6B, 4B, and 8B)

ChatGPT said:  EmbeddingGemma is a 300M parameter multilingual open embedding model from Google DeepMind, designed for efficient deployment even on low-resource devices, producing high-quality text vector representations for tasks such as search, classification, clustering, and semantic similarity.

NVIDIA-Nemotron-Nano-9B-v2 is a large language model (LLM) trained from scratch by NVIDIA, and designed as a unified model for both reasoning and non-reasoning tasks. It responds to user queries and tasks by first generating a reasoning trace and then concluding with a final response.  The model's reasoning capabilities can be controlled via a system prompt. If the user prefers the model to provide its final answer without intermediate reasoning traces, it can be configured to do so.

FLUX.1 Redux [dev] is an image variation generation adapter for all FLUX.1 base models. It enables users to refine images with slight variations and supports text-based restyling via API. Integrated with FLUX1.1 [pro] Ultra, it allows for high-quality 4-megapixel outputs. The model can be used with Diffusers in Python for efficient image generation. While powerful, it has ethical and factual limitations and is governed by a non-commercial license.

LLaMA2-13B-Tiefighter is a highly creative and versatile language model, fine-tuned for storytelling, adventure, and conversational dialogue. It combines the strengths of multiple models and datasets, including retro-rodeo and choose-your-own-adventure, to generate engaging and imaginative content. With its ability to improvise and adapt to different styles and formats, Tiefighter is perfect for writers, creators, and anyone looking to spark their imagination.

DeepSeek-V3-0324, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token, an improved iteration over DeepSeek-V3.

Janus-Pro is a novel autoregressive framework that unifies multimodal understanding and generation. It addresses the limitations of previous approaches by decoupling visual encoding into separate pathways, while still utilizing a single, unified transformer architecture for processing. The decoupling not only alleviates the conflict between the visual encoder’s roles in understanding and generation, but also enhances the framework’s flexibility. Janus-Pro surpasses previous unified model and matches or exceeds the performance of task-specific models. The simplicity, high flexibility, and effectiveness of Janus-Pro make it a strong candidate for next-generation unified multimodal models.

A drop-in replacement for Flux [Dev] that delivers sharper details, richer colors, and enhanced realism, while instantly boosting LoRAs and LyCORIS with full compatibility.

DeepSeek R1 Distill Qwen 32B is a distilled large language model based on Qwen 2.5 32B, using outputs from DeepSeek R1. It outperforms OpenAI's o1-mini across various benchmarks, achieving new state-of-the-art results for dense models.  Other benchmark results include:  AIME 2024: 72.6 | MATH-500: 94.3 | CodeForces Rating: 1691.

Enhanced industrial design and geometric reasoning, improved character consistency, reduced offset issues, and integrated LoRA capabilities

Zonos-v0.1 is a leading open-weight text-to-speech model trained on more than 200k hours of varied multilingual speech, delivering expressiveness and quality on par with—or even surpassing—top TTS providers.  Our model enables highly natural speech generation from text prompts when given a speaker embedding or audio prefix, and can accurately perform speech cloning when given a reference clip spanning just a few seconds. The conditioning setup also allows for fine control over speaking rate, pitch variation, audio quality, and emotions such as happiness, fear, sadness, and anger. The model outputs speech natively at 44kHz.

The GLM-4.5 series models are foundation models designed for intelligent agents. GLM-4.5 has 355 billion total parameters with 32 billion active parameters, while GLM-4.5-Air adopts a more compact design with 106 billion total parameters and 12 billion active parameters. GLM-4.5 models unify reasoning, coding, and intelligent agent capabilities to meet the complex demands of intelligent agent applications.

Gemma 3 introduces multimodality, supporting vision-language input and text outputs. It handles context windows up to 128k tokens, understands over 140 languages, and offers improved math, reasoning, and chat capabilities, including structured outputs and function calling. Gemma 3-12B is Google's latest open source model, successor to Gemma 2

Voxtral Mini is an enhancement of Ministral 3B, incorporating state-of-the-art audio input capabilities while retaining best-in-class text performance. It excels at speech transcription, translation and audio understanding.

DeepSeek-V3.1 Terminus is an update to DeepSeek V3.1 that maintains the model's original capabilities while addressing issues reported by users, including language consistency and agent capabilities, further optimizing the model's performance in coding and search agents. It is a large hybrid reasoning model (671B parameters, 37B active) that supports both thinking and non-thinking modes. It extends the DeepSeek-V3 base with a two-phase long-context training process. Users can control the reasoning behaviour with the reasoning enabled boolean. Learn more in our docs  The model improves tool use, code generation, and reasoning efficiency, achieving performance comparable to DeepSeek-R1 on difficult benchmarks while responding more quickly. It supports structured tool calling, code agents, and search agents, making it suitable for research, coding, and agentic workflows.

The 1080p high-fidelity mode in PixVerse renders videos with significantly enhanced sharpness and visual clarity, capturing intricate details and providing a crisp, professional-grade quality suitable for more polished projects.

Kimi K2 0905 is the September update of Kimi K2 0711. It is a large-scale Mixture-of-Experts (MoE) language model developed by Moonshot AI, featuring 1 trillion total parameters with 32 billion active per forward pass. It supports long-context inference up to 256k tokens, extended from the previous 128k.  This update improves agentic coding with higher accuracy and better generalization across scaffolds, and enhances frontend coding with more aesthetic and functional outputs for web, 3D, and related tasks. Kimi K2 is optimized for agentic capabilities, including advanced tool use, reasoning, and code synthesis. It excels across coding (LiveCodeBench, SWE-bench), reasoning (ZebraLogic, GPQA), and tool-use (Tau2, AceBench) benchmarks. The model is trained with a novel stack incorporating the MuonClip optimizer for stable large-scale MoE training.

Phi-4-reasoning-plus is a state-of-the-art open-weight reasoning model finetuned from Phi-4 using supervised fine-tuning on a dataset of chain-of-thought traces and reinforcement learning. The supervised fine-tuning dataset includes a blend of synthetic prompts and high-quality filtered data from public domain websites, focused on math, science, and coding skills as well as alignment data for safety and Responsible AI. The goal of this approach was to ensure that small capable models were trained with data focused on high quality and advanced reasoning. Phi-4-reasoning-plus has been trained additionally with Reinforcement Learning, hence, it has higher accuracy but generates on average 50% more tokens, thus having higher latency.

Stable Diffusion is a latent text-to-image diffusion model. Generate realistic images given text description

Qwen-Image-Edit is a next-generation image editing model built on top of Qwen-Image, designed for both semantic and appearance-level edits. It excels at tasks like precise text modifications, style transfers, viewpoint transformations, and element adjustments while preserving overall visual consistency.

Dolphin 2.9.1, a fine-tuned Llama-3-70b model. The new model, trained on filtered data, is more compliant but uncensored. It demonstrates improvements in instruction, conversation, coding, and function calling abilities.

We present a sentence transformation model that generates semantically similar sentences. Our model is based on the Sentence-Transformers architecture and was trained on a large dataset of sentence pairs. We evaluate the effectiveness of our model by measuring its ability to generate similar sentences that are close to the original sentence in meaning.

The Multilingual-E5 models, initialized from XLM-RoBERTa, support up to 512 tokens per input — any longer text will be silently truncated. To ensure optimal performance, always prefix inputs with “query:” or “passage:”, as the model was explicitly trained with this format.

This model is a multilingual version of the OpenAI CLIP-ViT-B32 model, which maps text and images to a common dense vector space. It includes a text embedding model that works for 50+ languages and an image encoder from CLIP. The model was trained using Multilingual Knowledge Distillation, where a multilingual DistilBERT model was trained as a student model to align the vector space of the original CLIP image encoder across many languages.

This is the instruction fine-tuned version of Mixtral-8x22B - the latest and largest mixture of experts large language model (LLM) from Mistral AI. This state of the art machine learning model uses a mixture 8 of experts (MoE) 22b models. During inference 2 experts are selected. This architecture allows large models to be fast and cheap at inference.

The 72 billion parameter Qwen2 excels in language understanding, multilingual capabilities, coding, mathematics, and reasoning.

Whisper is a state-of-the-art model for automatic speech recognition (ASR) and speech translation, proposed in the paper "Robust Speech Recognition via Large-Scale Weak Supervision" by Alec Radford  et al. from OpenAI. Trained on >5M hours of labeled data, Whisper demonstrates a strong ability to generalise to many datasets and domains in a zero-shot setting. Whisper large-v3-turbo is a finetuned version of a pruned Whisper large-v3. In other words, it's the exact same model, except that the number of decoding layers have reduced from 32 to 4. As a result, the model is way faster, at the expense of a minor quality degradation.

Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on 680k hours of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains without fine-tuning. The primary intended users of these models are AI researchers studying robustness, generalisation, and capabilities of the current model.

This offers the imaginative writing style of chronos while still retaining coherency and being capable. Outputs are long and utilize exceptional prose. Supports a maxium context length of 4096. The model follows the Alpaca prompt format.

🥳 For a limited time, Fibo Edit is free on DeepInfra 🥳  YOUR AI, YOUR RULES. Visual Generation for Production-Grade. FIBO Edit. An open-source image editing model with native masking and a lightweight 8B architecture.

WizardLM-2 8x22B is Microsoft AI's most advanced Wizard model. It demonstrates highly competitive performance compared to those leading proprietary models.

Llama 3.3-70B is a multilingual LLM trained on a massive dataset of 15 trillion tokens, fine-tuned for instruction-following and conversational dialogue. The model is designed to be helpful, safe, and flexible, with a focus on responsible deployment and mitigating potential risks such as bias, toxicity, and misinformation. It achieves state-of-the-art performance on various benchmarks, including conversational tasks, language translation, and text generation.

The Meta Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out).

The DeepSeek R1 model has undergone a minor version upgrade, with the current version being DeepSeek-R1-0528.

The CLIP model was developed by OpenAI to investigate the robustness of computer vision models. It uses a Vision Transformer architecture and was trained on a large dataset of image-caption pairs. The model shows promise in various computer vision tasks but also has limitations, including difficulties with fine-grained classification and potential biases in certain applications.

Anthropic’s most powerful model yet and the state-of-the-art coding model. It delivers sustained performance on long-running tasks that require focused effort and thousands of steps, significantly expanding what AI agents can solve. Claude Opus 4 is ideal for powering frontier agent products and features.

OpenChat is a library of open-source language models that have been fine-tuned with C-RLFT, a strategy inspired by offline reinforcement learning. These models can learn from mixed-quality data without preference labels and have achieved exceptional performance comparable to ChatGPT. The developers of OpenChat are dedicated to creating a high-performance, commercially viable, open-source large language model and are continuously making progress towards this goal.

The latest image model, delivering better editing consistency, improved multi-image fusion, finer detail control, natural small text and faces, and harmonious, aesthetic visuals.

Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. It was trained on 680k hours of labelled data and demonstrated a strong ability to generalise to many datasets and domains without fine-tuning. Whisper checks pens are available in five configurations of varying model sizes, including a smallest configuration trained on English-only data and a largest configuration trained on multilingual data. This one is English-only.

Gemma 3 introduces multimodality, supporting vision-language input and text outputs. It handles context windows up to 128k tokens, understands over 140 languages, and offers improved math, reasoning, and chat capabilities, including structured outputs and function calling. Gemma 3 27B is Google's latest open source model, successor to Gemma 2

Gemini 2.5 Flash is Google's latest thinking model, designed to tackle increasingly complex problems. It's capable of reasoning through their thoughts before responding, resulting in enhanced performance and improved accuracy.  Gemini 2.5 Flash: best for balancing reasoning and speed.

CodeGemma is a collection of lightweight open code models built on top of Gemma. CodeGemma models are text-to-text and text-to-code decoder-only models and are available as a 7 billion pretrained variant that specializes in code completion and code generation tasks, a 7 billion parameter instruction-tuned variant for code chat and instruction following and a 2 billion parameter pretrained variant for fast code completion.

The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer three different sizes of models, including GTE-large, GTE-base, and GTE-small. The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including information retrieval, semantic textual similarity, text reranking, etc.

Openchat 3.6 is a LLama-3-8b fine tune that outperforms it on multiple benchmarks.

Gemma is a family of lightweight, state-of-the-art open models from Google. The 9B Gemma 2 model delivers class-leading performance, outperforming Llama 3 8B and other open models in its size category.

Llama Guard 4 is a natively multimodal safety classifier with 12 billion parameters trained jointly on text and multiple images. Llama Guard 4 is a dense architecture pruned from the Llama 4 Scout pre-trained model and fine-tuned for content safety classification. Similar to previous versions, it can be used to classify content in both LLM inputs (prompt classification) and in LLM responses (response classification). It itself acts as an LLM: it generates text in its output that indicates whether a given prompt or response is safe or unsafe, and if unsafe, it also lists the content categories violated.

Qwen2.5 is a model pretrained on a large-scale dataset of up to 18 trillion tokens, offering significant improvements in knowledge, coding, mathematics, and instruction following compared to its predecessor Qwen2. The model also features enhanced capabilities in generating long texts, understanding structured data, and generating structured outputs, while supporting multilingual capabilities for over 29 languages.

Meta developed and released the Meta Llama 3.1 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8B, 70B and 405B sizes

PaddleOCR-VL is a SOTA and resource-efficient model tailored for document parsing. Its core component is PaddleOCR-VL-0.9B, a compact yet powerful vision-language model (VLM) that integrates a NaViT-style dynamic resolution visual encoder with the ERNIE-4.5-0.3B language model to enable accurate element recognition. This innovative model efficiently supports 109 languages and excels in recognizing complex elements (e.g., text, tables, formulas, and charts), while maintaining minimal resource consumption. Through comprehensive evaluations on widely used public benchmarks and in-house benchmarks, PaddleOCR-VL achieves SOTA performance in both page-level document parsing and element-level recognition. It significantly outperforms existing solutions, exhibits strong competitiveness against top-tier VLMs, and delivers fast inference speeds. These strengths make it highly suitable for practical deployment in real-world scenarios.

QVQ-72B-Preview is an experimental research model developed by the Qwen team, focusing on enhancing visual reasoning capabilities. QVQ-72B-Preview has achieved remarkable performance on various benchmarks. It scored a remarkable 70.3% on the Multimodal Massive Multi-task Understanding (MMMU) benchmark

The Meta Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out)

Bria RMBG 2.0 enables seamless removal of backgrounds from images, ideal for professional editing tasks. Trained exclusively on licensed data for safe and risk-free commercial use.

Hermes 3 is a generalist language model with many improvements over Hermes 2, including advanced agentic capabilities, much better roleplaying, reasoning, multi-turn conversation, long context coherence, and improvements across the board.

We present a sentence similarity model based on the Sentence Transformers architecture, which maps sentences to a 384-dimensional dense vector space. The model uses a pre-trained BERT encoder and applies mean pooling on top of the contextualized word embeddings to obtain sentence embeddings. We evaluate the model on the Sentence Embeddings Benchmark.

Euryale 70B v2.1 is a model focused on creative roleplay from Sao10k

Bria Enhance improves overall image quality by sharpening details, balancing colors, and boosting clarity for crisp, professional visuals. Trained only on licensed data, it’s safe, reliable, and ready for commercial use.

Veo 3.1 is the latest text-to-video model from Google that generates high-fidelity, cinematic videos with synchronized audio from a simple text prompt. It excels at creating realistic and imaginative scenes with a deep understanding of natural language and visual dynamics.

Bria GenFill enables high-quality object addition or visual transformation. Trained exclusively on licensed data for safe and risk-free commercial use.

Brand-new Flux2 Dev introduces a faster, more modular architecture for next-generation image generation pipelines. It delivers improved performance, cleaner control APIs, and a significantly more flexible development workflow for custom inference setups.

You can use cURL or any other http client to run inferences:

```bash
curl -X POST \
    -d '{"text": "The quick brown fox jumps over the lazy dog"}'  \
    -H "Authorization: bearer $DEEPINFRA_TOKEN"  \
    -H 'Content-Type: application/json'  \
    'https://api.deepinfra.com/v1/inference/inworld-ai/inworld-tts-1.5-max'
```

which will give you back something similar to:

```json
{
  "audio": null,
  "input_character_length": 0,
  "output_format": "",
  "words": [
    {
      "end": 1.0,
      "start": 0.0,
      "text": "Hello"
    },
    {
      "end": 5.0,
      "start": 4.0,
      "text": "World"
    }
  ],
  "request_id": null,
  "inference_status": {
    "status": "unknown",
    "runtime_ms": 0,
    "cost": 0.0,
    "tokens_generated": 0,
    "tokens_input": 0,
    "output_length": 0
  }
}

```


You can use our command-line tool [deepctl](/docs/advanced/deepctl) to run
inferences:

```bash
deepctl infer \
    -m 'inworld-ai/inworld-tts-1.5-max'  \
    -i 'text=The quick brown fox jumps over the lazy dog'
```

which will give you back something similar to:

```json
{
  "audio": null,
  "input_character_length": 0,
  "output_format": "",
  "words": [
    {
      "end": 1.0,
      "start": 0.0,
      "text": "Hello"
    },
    {
      "end": 5.0,
      "start": 4.0,
      "text": "World"
    }
  ],
  "request_id": null,
  "inference_status": {
    "status": "unknown",
    "runtime_ms": 0,
    "cost": 0.0,
    "tokens_generated": 0,
    "tokens_input": 0,
    "output_length": 0
  }
}

```


DeepInfra supports custom voices.

## Create voice

The following creates a voice using the `curl` command.

```bash
curl -X POST "https://api.deepinfra.com/v1/voices/add" \
  -H "Content-Type: multipart/form-data" \
  -H "Authorization: Bearer $DEEPINFRA_TOKEN" \
  -F "audio=@hello.wav" \
  -F "name=John Doe" \
  -F "description=John Doe's voice"
```

which will return something similar to

```json
{
  "user_id": "gh:10000000", 
  "voice_id": "abcd1234abcd1234abcd",
  "name": "John Doe",
  "description": "John Doe's voice",
  "created_at": 1723851387,
  "updated_at": 1723851387
}
```


We try to be eleventlabs python library compatible. Please reach out to feedback@deepinfra.com if you encounter any issues.
```python
from elevenlabs import ElevenLabs, play

# Initialize the ElevenLabs client with overridden api_key and base_url
client = ElevenLabs(api_key="$DEEPINFRA_TOKEN", base_url="https://api.deepinfra.com")

# Define the voice data
voice_name = "John Doe"
voice_description = "John Doe's voice"
audio_file_path = "test_audio.wav"

# Create the voice by cloning using the ElevenLabs client
cloned_voice = client.clone(
    name=voice_name,
    description=voice_description,
    files=[audio_file_path],
    labels="",
)

# Use the voice_id to generate speech
audio = client.generate(
    text="Hello, how are you?",
    voice=cloned_voice.voice_id,
    model="deepinfra/tts",
    output_format="wav",
)

play(audio)
```


The following creates a voice using the `axios` library in JavaScript.

```javascript
const axios = require('axios');
const FormData = require('form-data');
const fs = require('fs');

// Define the API endpoint
const url = "https://api.deepinfra.com/v1/voices/add";

// Create a FormData instance
const formData = new FormData();

// Append the audio file, name, and description to the form data
formData.append('files', fs.createReadStream('test_audio.wav'));
formData.append('name', 'John Doe');
formData.append('description', "John Doe's voice");

// Set the headers, including authorization and content type
const headers = {
    "Authorization": "Bearer $DEEPINFRA_TOKEN",
    ...formData.getHeaders()
};

// Send the POST request
axios.post(url, formData, { headers: headers })
    .then(response => {
        console.log("Voice created successfully!");
        console.log("Response:", response.data);
    })
    .catch(error => {
        console.error("Failed to create voice.");
        console.error("Status Code:", error.response.status);
        console.error("Response:", error.response.data);
    });
```

## Read voice

The following reads a voice using the `curl` command.

```bash
curl -X GET "https://api.deepinfra.com/v1/voices/abcd1234abcd1234abcd" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $DEEPINFRA_TOKEN"
```

which will return something similar to

```json
{
  "user_id": "gh:10000000", 
  "voice_id": "abcd1234abcd1234abcd",
  "name": "John Doe",
  "description": "John Doe's voice",
  "created_at": 1723851387,
  "updated_at": 1723851387
}
```


## Read voice

The following reads a voice using the `elevenlabs` library in Python. If you encounter any issues, please contact us at feedback@deepinfra.com.
```python
from elevenlabs import ElevenLabsClient

client = ElevenLabsClient(api_key="$DEEPINFRA_TOKEN", base_url="https://api.deepinfra.com")

voice = client.voices.get(voice_id="abcd1234abcd1234abcd")
print(voice)
```


## Read voice

The following reads a voice using JavaScript with the `fetch` API.

```javascript
const url = "https://api.deepinfra.com/v1/voices/abcd1234abcd1234abcd";
const headers = {
  "Content-Type": "application/json",
  "Authorization": "Bearer $DEEPINFRA_TOKEN"
};

fetch(url, { method: "GET", headers: headers })
  .then(response => response.json())
  .then(data => console.log(data))
  .catch(error => console.error('Error:', error));
```

## Update voice

The following updates a voice using the `curl` command.

```bash
curl -X POST "https://api.deepinfra.com/v1/voices/abcd1234abcd1234abcd/edit" \
  -H "Authorization: Bearer $DEEPINFRA_TOKEN" \
  -F "name=Jane Doe" \
  -F "description=Jane Doe's voice"
```

which will return something similar to

```json
{
  "user_id": "gh:10000000", 
  "voice_id": "abcd1234abcd1234abcd",
  "name": "Jane Doe",
  "description": "Jane Doe's voice",
  "created_at": 1723851387,
  "updated_at": 1723851387
}
```


## Update voice
We support elevenlabs client for python. If you encounter any issues, please contact us at feedback@deepinfra.com.
```python
from elevenlabs import ElevenLabsClient

client = ElevenLabsClient(api_key="$DEEPINFRA_TOKEN", base_url="https://api.deepinfra.com")

client.voices.edit(voice_id="abcd1234abcd1234abcd", name="Jane Doe", description="Jane Doe's voice")
```


## Update voice

Update a voice using the `axios` library in JavaScript.

```javascript
const axios = require('axios');
const FormData = require('form-data');

const url = "https://api.deepinfra.com/v1/voices/abcd1234abcd1234abcd/edit";
const formData = new FormData();
formData.append('name', 'John Doe');
formData.append('description', "John Doe's voice");

const headers = {
    "Authorization": "Bearer $DEEPINFRA_TOKEN",
    ...formData.getHeaders()
};

axios.post(url, formData, { headers: headers })
    .then(response => {
        console.log("Voice updated successfully!");
        console.log("Response:", response.data);
    })
    .catch(error => {
        console.error("Failed to update voice.");
        console.error("Status Code:", error.response.status);
        console.error("Response:", error.response.data);
    });
```


## Delete voice

The following deletes a voice using the `curl` command.

```bash
curl -X DELETE "https://api.deepinfra.com/v1/voices/abcd1234abcd1234abcd" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $DEEPINFRA_TOKEN"
```

which will return 200 OK status code.


## Delete voice


```python
from elevenlabs import ElevenLabsClient

client = ElevenLabsClient(api_key="$DEEPINFRA_TOKEN", base_url="https://api.deepinfra.com")

client.voices.delete(voice_id="abcd1234abcd1234abcd")
```


## Delete voice

The following deletes a voice using the `fetch` API in JavaScript.

```javascript
const url = "https://api.deepinfra.com/v1/voices/abcd1234abcd1234abcd";
const headers = {
  "Content-Type": "application/json",
  "Authorization": `Bearer $DEEPINFRA_TOKEN`
};

fetch(url, {
  method: "DELETE",
  headers: headers
})
.then(response => {
  if (response.ok) {
    console.log("Voice deleted successfully.");
  } else {
    throw new Error(`Failed to delete voice: ${response.status}`);
  }
})
.catch(error => console.error(error));
```


## List voices
The following lists voices using the `curl` command.

```bash
curl -X GET "https://api.deepinfra.com/v1/voices" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $DEEPINFRA_TOKEN" \
```

which will return something similar to

```json
{
  "voices": [
    {
      "user_id": "gh:10000000", 
      "voice_id": "abcd1234abcd1234abcd",
      "name": "John Doe",
      "description": "John Doe's voice",
      "created_at": 1723851387,
      "updated_at": 1723851387
    },
    {
      "user_id": "gh:10000000",
      "voice_id":"abcd1234abcd1234abc1",
      "name": "Jane Doe",
      "description": "Jane Doe's voice",
      "created_at": 1723680057,
      "updated_at": 1723680057
    }
  ]
}
```


## List voices

The following lists voices using the `elevenlabs` client library in Python. If you encounter any issues, please reach out to feedback@deepinfra.com.

```python
from elevenlabs import ElevenLabs

client = ElevenLabs(api_key="$DEEPINFRA_TOKEN", base_url="https://api.deepinfra.com")

response = client.voices.get_all()

for voice in response.voices:
    print(voice.voice_id)
```


## List voices

The following lists voices using the `fetch` API in JavaScript.

```javascript
const url = "https://api.deepinfra.com/v1/voices";
const headers = {
  "Content-Type": "application/json",
  "Authorization": `Bearer $DEEPINFRA_TOKEN`
};

fetch(url, {
  method: "GET",
  headers: headers
})
.then(response => {
  if (response.ok) {
    return response.json();
  } else {
    throw new Error(`Failed to list voices: ${response.status}`);
  }
})
.then(data => console.log(data))
.catch(error => console.error(error));
```


The DeepInfra OpenAI-compatible Speech API endpoint enables users to effortlessly convert text into speech audio. This document outlines how to integrate and utilize this endpoint to quickly create speech from text inputs, leveraging various audio output formats.

## Create speech

Use the following example of pythong code to generate an audio file from your text input:

```python
from pathlib import Path
from openai import OpenAI

client = OpenAI(base_url="https://api.deepinfra.com/v1/openai",
                api_key="$DEEPINFRA_TOKEN")

speech_file_path = Path(__file__).parent / "speech.mp3"
with client.audio.speech.with_streaming_response.create(
  model="inworld-ai/inworld-tts-1.5-max",
  voice="Ashley",
  input="The quick brown fox jumped over the lazy dog.",
  response_format="mp3",
) as response:
  response.stream_to_file(speech_file_path)
```

The API returns the generated audio file in the requested format (e.g., `mp3`, `pcm`). The example above saves the audio output directly as `speech.mp3`.

### Supported Audio Formats

| Format | Description                           | Recommended Usage                          |
|--------|---------------------------------------|--------------------------------------------|
| pcm    | Raw, uncompressed audio               | *Best for lowest latency streaming*        |
| mp3    | Compressed audio suitable for storage | General use and file distribution          |
| opus   | Compressed audio ideal for streaming  | Efficient streaming and real-time playback |
| flac   | Lossless audio compression            | High-quality archival storage              |
| wav    | Uncompressed audio                    | High-quality audio processing tasks        |

### Performance Recommendations

- **For lowest latency (fastest initial audio chunk):**
  - Use the `pcm` output format.

- **For general purposes:**
  - Use the `mp3` or `opus` formats for optimal balance between quality and file size.



The DeepInfra OpenAI-compatible Speech API endpoint enables users to effortlessly convert text into speech audio. This document outlines how to integrate and utilize this endpoint to quickly create speech from text inputs, leveraging various audio output formats.

## Create speech

Use the following example of js code to generate an audio file from your text input:

```javascript
import fs from "fs";
import path from "path";
import OpenAI from "openai";

const openai = new OpenAI(base_url="https://api.deepinfra.com/v1/openai",
                          api_key="$DEEPINFRA_TOKEN");

const speechFile = path.resolve("./speech.mp3");

async function main() {
  const mp3 = await openai.audio.speech.create({
    model: "inworld-ai/inworld-tts-1.5-max",
    voice: "Ashley",
    input: "The quick brown fox jumped over the lazy dog.",
    response_format: "mp3",
  });
  console.log(speechFile);
  const buffer = Buffer.from(await mp3.arrayBuffer());
  await fs.promises.writeFile(speechFile, buffer);
}
main();
```

The API returns the generated audio file in the requested format (e.g., `mp3`, `pcm`). The example above saves the audio output directly as `speech.mp3`.

### Supported Audio Formats

| Format | Description                           | Recommended Usage                          |
|--------|---------------------------------------|--------------------------------------------|
| pcm    | Raw, uncompressed audio               | *Best for lowest latency streaming*        |
| mp3    | Compressed audio suitable for storage | General use and file distribution          |
| opus   | Compressed audio ideal for streaming  | Efficient streaming and real-time playback |
| flac   | Lossless audio compression            | High-quality archival storage              |
| wav    | Uncompressed audio                    | High-quality audio processing tasks        |

### Performance Recommendations

- **For lowest latency (fastest initial audio chunk):**
  - Use the `pcm` output format.

- **For general purposes:**
  - Use the `mp3` or `opus` formats for optimal balance between quality and file size.



The DeepInfra OpenAI-compatible Speech API endpoint enables users to effortlessly convert text into speech audio. This document outlines how to integrate and utilize this endpoint to quickly create speech from text inputs, leveraging various audio output formats.

## Create speech

Use the following example `curl` request to generate an audio file from your text input:

```bash
curl https://api.deepinfra.com/v1/openai/audio/speech \
  -H "Authorization: Bearer $DEEPINFRA_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "inworld-ai/inworld-tts-1.5-max",
    "input": "The quick brown fox jumped over the lazy dog.",
    "voice": "Ashley",
    "response_format": "mp3"
  }' \
  --output speech.mp3
```

The API returns the generated audio file in the requested format (e.g., `mp3`, `pcm`). The example above saves the audio output directly as `speech.mp3`.

### Supported Audio Formats

| Format | Description                           | Recommended Usage                          |
|--------|---------------------------------------|--------------------------------------------|
| pcm    | Raw, uncompressed audio               | *Best for lowest latency streaming*        |
| mp3    | Compressed audio suitable for storage | General use and file distribution          |
| opus   | Compressed audio ideal for streaming  | Efficient streaming and real-time playback |
| flac   | Lossless audio compression            | High-quality archival storage              |
| wav    | Uncompressed audio                    | High-quality audio processing tasks        |

### Performance Recommendations

- **For lowest latency (fastest initial audio chunk):**
  - Use the `pcm` output format.

- **For general purposes:**
  - Use the `mp3` or `opus` formats for optimal balance between quality and file size.



The DeepInfra ElevenLabs-compatible Speech API endpoint allows users to seamlessly convert text inputs into high-quality speech audio. This document provides guidance on integrating and using this endpoint to generate realistic audio files efficiently.

Use the following examples of `py` request to generate an audio file from your text input:

## Create Speech (non-streaming)

```bash
from elevenlabs import ElevenLabs

client = ElevenLabs(
    api_key="$DEEPINFRA_TOKEN",
    base_url="https://api.deepinfra.com/",
)
client.text_to_speech.convert(
    voice_id="Ashley",
    output_format="mp3",
    text="The quick brown fox jumped over the lazy dog.",
    model_id="inworld-ai/inworld-tts-1.5-max",
)
```

## Create Speech with Streaming

```bash
from elevenlabs import ElevenLabs

client = ElevenLabs(
    api_key="$DEEPINFRA_TOKEN",
)
client.text_to_speech.convert_as_stream(
    voice_id="Ashley",
    output_format="pcm",
    text="The quick brown fox jumped over the lazy dog.",
    model_id="inworld-ai/inworld-tts-1.5-max",
)
```

The API returns the generated audio in the requested format, such as `mp3` or `pcm`. The example above saves the audio directly as `speech.mp3`.

### Supported Audio Formats

| Format | Description                           | Recommended Usage                          |
|--------|---------------------------------------|--------------------------------------------|
| pcm    | Raw, uncompressed audio               | *Lowest latency streaming scenarios*       |
| mp3    | Compressed audio suitable for storage | General use and easy file sharing          |
| opus   | Compressed audio ideal for streaming  | Real-time audio streaming applications     |
| flac   | Lossless audio compression            | Archival storage and high-fidelity needs   |
| wav    | Uncompressed audio                    | Audio processing and editing tasks         |

### Performance Recommendations

- **Lowest latency:** Using the `pcm` output format for streaming applications is **HIGHLY RECOMMENDED**.
- **General use:** Use `mp3` or `opus` formats for the best trade-off between quality and file size.



The DeepInfra ElevenLabs-compatible Speech API endpoint allows users to seamlessly convert text inputs into high-quality speech audio. This document provides guidance on integrating and using this endpoint to generate realistic audio files efficiently.

Use the following examples of `js` request to generate an audio file from your text input:

## Create Speech (non-streaming)

```bash
import { ElevenLabsClient } from "elevenlabs";

const client = new ElevenLabsClient({ apiKey: "$DEEPINFRA_TOKEN", base_url: "https://api.deepinfra.com/" });
await client.textToSpeech.convert("Ashley", {
    output_format: "mp3",
    text: "The quick brown fox jumped over the lazy dog.",
    model_id: "inworld-ai/inworld-tts-1.5-max"
});
```

## Create Speech with Streaming

```bash
import { ElevenLabsClient } from "elevenlabs";

const client = new ElevenLabsClient({ apiKey: "$DEEPINFRA_TOKEN" });
await client.textToSpeech.convert("Ashley", {
    output_format: "pcm",
    text: "The quick brown fox jumped over the lazy dog.",
    model_id: "inworld-ai/inworld-tts-1.5-max"
});
```

The API returns the generated audio in the requested format, such as `mp3` or `pcm`. The example above saves the audio directly as `speech.mp3`.

### Supported Audio Formats

| Format | Description                           | Recommended Usage                          |
|--------|---------------------------------------|--------------------------------------------|
| pcm    | Raw, uncompressed audio               | *Lowest latency streaming scenarios*       |
| mp3    | Compressed audio suitable for storage | General use and easy file sharing          |
| opus   | Compressed audio ideal for streaming  | Real-time audio streaming applications     |
| flac   | Lossless audio compression            | Archival storage and high-fidelity needs   |
| wav    | Uncompressed audio                    | Audio processing and editing tasks         |

### Performance Recommendations

- **Lowest latency:** Using the `pcm` output format for streaming applications is **HIGHLY RECOMMENDED**.
- **General use:** Use `mp3` or `opus` formats for the best trade-off between quality and file size.



The DeepInfra ElevenLabs-compatible Speech API endpoint allows users to seamlessly convert text inputs into high-quality speech audio. This document provides guidance on integrating and using this endpoint to generate realistic audio files efficiently.

Use the following examples of `curl` request to generate an audio file from your text input:

## Create Speech (non-streaming)

```bash
curl -X POST "https://api.deepinfra.com/v1/text-to-speech/Ashley" \
     -H "xi-api-key: $DEEPINFRA_TOKEN" \
     -H "Content-Type: application/json" \
     -d '{
  "text": "The quick brown fox jumped over the lazy dog.",
  "model_id": "inworld-ai/inworld-tts-1.5-max",
  "output_format": "mp3",
}' --output speech.mp3
```

## Create Speech with Streaming

```bash
curl -X POST "https://api.deepinfra.com/v1/text-to-speech/Ashley/stream" \
     -H "xi-api-key: $DEEPINFRA_TOKEN" \
     -H "Content-Type: application/json" \
     -d '{
  "text": "The quick brown fox jumped over the lazy dog.",
  "model_id": "inworld-ai/inworld-tts-1.5-max",
  "output_format": "pcm",
}' --output speech.pcm
```

The API returns the generated audio in the requested format, such as `mp3` or `pcm`. The example above saves the audio directly as `speech.mp3`.

### Supported Audio Formats

| Format | Description                           | Recommended Usage                          |
|--------|---------------------------------------|--------------------------------------------|
| pcm    | Raw, uncompressed audio               | *Lowest latency streaming scenarios*       |
| mp3    | Compressed audio suitable for storage | General use and easy file sharing          |
| opus   | Compressed audio ideal for streaming  | Real-time audio streaming applications     |
| flac   | Lossless audio compression            | Archival storage and high-fidelity needs   |
| wav    | Uncompressed audio                    | Audio processing and editing tasks         |

### Performance Recommendations

- **Lowest latency:** Using the `pcm` output format for streaming applications is **HIGHLY RECOMMENDED**.
- **General use:** Use `mp3` or `opus` formats for the best trade-off between quality and file size.



The service tier used for processing the request. When set to 'priority', the request will be processed with higher priority (only applies to models that support it).

service_tier

text

Preset voice name (Ashley, Diego, etc.) or a voice_id from /v1/voices/add for voice cloning.

voice

output_format

speaking_rate

Temperature controls variability of the speech

temperature

sample_rate

return_timestamps

stream

The webhook to call when inference is done, by default you will get the output in the response of your inference request

inworld-tts-1.5-max

Add New Voice