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thinkingmachines/
Inkling-Small
$0.58
in
$1.44
out
$0.116
cached
/ 1M tokens
Inkling-Small is a Mixture-of-Experts transformer with 276B total parameters, 12B active, trained on NVIDIA GB300 NVL72 systems. Like Inkling, it features native reasoning over audio and images, variable thinking effort
Inkling-Small
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license: apache-2.0 license_link: https://www.apache.org/licenses/LICENSE-2.0 pipeline_tag: image-text-to-text tags:
- conversational
- image-text-to-text
- audio-text-to-text
- moe library_name: transformers
Inkling
1. General Information
Inkling-Small is a general-purpose multimodal model that accepts text, image and audio inputs and generates text outputs. It is intended for use in English and other languages, and across multiple coding languages. The model is designed to be used by developers building AI-powered applications, including agentic and tool-use systems, coding assistants, chatbots, and retrieval-augmented generation systems, and is suitable for general-purpose conversational use, instruction-following, and other natural language and multimodal tasks. It is released with open weights to support research, fine-tuning and integration into third-party products by downstream developers.
Languages: English, with general multilingual capabilities across other languages.
2. Model Properties
Model type
Multimodal autoregressive transformer
Architecture type
A 42-layer decoder-only transformer with a sparse Mixture-of-Experts (MoE) feed-forward backbone: each token is routed to 6 of 256 experts, plus 2 shared experts active on every token. Attention is a hybrid of local and global layers. The model is natively multimodal — images are encoded via a hierarchical patch encoder, and audio via discrete token encoding — with all modalities projected into a shared hidden space and processed jointly by the decoder.
Parameters
276B total, 12B active
Numerics support
BF16 and NVFP4
Input modalities
Inkling-Small accepts text, image, and audio inputs:
- Text: UTF-8 encoded text
- Image: Any pixel-based image input. For optimal performance, each image dimension should be between 40px to 4096px.
- Audio: WAV format, sampled at 16kHz. For optimal performance, audio length should ideally be under 2 mins.
Output modalities
Inkling-Small generates output as UTF-8 encoded text.
3. Evaluations
| Open weights | Closed weights | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Inkling-Small | Qwen3.5 397B-A17B | MiMo V2.5 | Minimax M2.7 | DeepSeek V4 Flash | Nemotron 3 Ultra | Inkling | Claude 4.5 Haiku | Gemini 3.5 Flash-Lite | GPT 5.6 Luna | |
| Model Info | ||||||||||
| AA Index (v4.1) | 40.0% | 34.0% | 37.0% | 38.0% | 40.0% | 38.0% | 41.0% | 30.0% | 36.0% | 49.0% |
| Params (B) (activated / total) | 12 / 276 | 17 / 397 | 15 / 310 | 10 / 230 | 13 / 284 | 55 / 550 | 41 / 975 | – | – | – |
| Agentic (coding) | ||||||||||
| SWEBench Verified | 80.2% | 76.4% | 71.0% | 79.9% | 79.0% | 70.7% | 77.6% | 73.3% | 75.0% | 93.0% |
| SWEBench Pro (public) | 55.9% | 50.9% | 56.1% | 56.2% | 52.6% | 46.4% | 54.3% | 39.5% | 54.2% | 62.7% |
| Terminal Bench 2.1 (best harness) | 64.7% | 51.3% | 63.7% | 55.4% | 61.8% | 56.4% | 63.8% | 44.2% | 54.0% | 82.5% |
| SciCode | 48.7% | 42.0% | 43.1% | 47.0% | 44.9% | 39.9% | 46.1% | 43.3% | 40.9% | 50.0% |
| Agentic (general) | ||||||||||
| GDPval-AA v2 | 1269 | 962 | 1145 | 1159 | 1189 | 1164 | 1238 | 911 | 1139 | 1530 |
| MCP Atlas (public / all) | 79.6/79.2% | 74.2%/– | – | 49.4%/– | 69.0%/– | 47.4/44.7% | 78.8/76.0% | 41.2/40.2% | 79.8/76.8% | 77.0/75.0% |
| Tau 3 Banking | 15.5% | 13.4% | 6.6% | 8.9% | 22.9% | 13.8% | 23.7% | 9.1% | 16.5% | 24.3% |
| BrowseComp (with context management) | 77.4% | 78.6% | – | 76.3% | 73.2% | 63.0% | 77.1% | – | – | 84.0% |
| Toolathlon Verified | 54.4% | 40.7% | 49.1% | 47.5% | 50.9% | 34.3% | 45.5% | 26.9% | 57.1% | 67.9% |
| AA-Briefcase | 917 | – | – | – | 833 | 870 | 839 | 612 | – | – |
| Reasoning (general) | ||||||||||
| GPQA Diamond | 89.5% | 89.3% | 84.9% | 87.4% | 89.4% | 86.7% | 87.2% | 67.2% | 83.8% | 89.5% |
| HLE (text only) | 31.6% | 27.3% | 25.2% | 28.1% | 32.1% | 26.6% | 29.7% | 9.7% | 17.5% | 35.6% |
| HLE (with tools) | 47.8% | 48.3% | 40.0% | 40.3% | 45.1% | 37.4% | 46.0% | 17.8% | 42.5% | 48.9% |
| AIME 2026 | 95.5% | 93.3% | 93.6% | 87.7% | 95.8% | 94.2% | 97.1% | 85.1% | 82.2% | 97.6% |
| HMMT Feb 2026 | 90.2% | 87.9% | 82.6% | 71.2% | 93.9% | 78.8% | 86.3% | 66.7% | 63.6% | 98.5% |
| CritPt | 8.3% | 1.7% | 3.7% | 0.6% | 7.1% | 3.1% | 5.4% | 0.0% | 0.0% | 20.6% |
| Reasoning (abstract) | ||||||||||
| ARC-AGI-1 | 84.0% | – | – | – | – | – | 79.5% | 47.7% | – | 87.7% |
| ARC-AGI-2 | 40.1% | – | – | – | – | – | 36.5% | 4.0% | – | 47.6% |
| Factuality | ||||||||||
| SimpleQA Verified | 20.6% | 26.0% | 16.1% | 13.5% | 34.1% | 32.4% | 43.9% | 5.9% | 44.1% | 41.7% |
| AA Omniscience (index) | -9.0 | -29.8 | -9.3 | 0.7 | -22.9 | -1.0 | 2.1 | -4.2 | 6.9 | -11.6 |
| Chat | ||||||||||
| IFBench | 82.2% | 78.8% | 67.1% | 75.7% | 79.2% | 81.4% | 79.8% | 54.3% | 78.6% | 67.3% |
| Global-MMLU-Lite | 86.7% | 90.0% | 83.5% | 83.9% | 88.4% | 85.6% | 88.7% | 83.4% | 89.4% | 88.7% |
| Safety | ||||||||||
| StrongREJECT | 98.4% | 99.4% | 99.3% | 99.4% | 97.4% | 98.7% | 98.6% | 98.6% | 97.6% | 98.7% |
| FORTRESS (adversarial) | 71.6% | 77.3% | 64.8% | 86.3% | 32.0% | 77.6% | 78.0% | 91.3% | 70.7% | 83.8% |
| FORTRESS (benign) | 96.9% | 95.4% | 94.6% | 90.1% | 99.2% | 90.6% | 95.9% | 94.1% | 95.5% | 97.8% |
| Vision | ||||||||||
| MMMU Pro (Standard 10) | 74.0% | 77.3% | 75.4% | – | – | – | 73.5% | 58.6% | 79.0% | 78.6% |
| Charxiv RQ (original / with python) | 77.4/81.3% | 80.8%/– | 81.0%/– | – | – | – | 78.1/82.0% | 57.4%/– | 70.0%/– | 81.4%/– |
| Audio | ||||||||||
| Audio MC | 54.9% | – | 30.4% | – | – | – | 56.6% | – | 33.6% | – |
| MMAU | 77.0% | – | 73.6% | – | – | – | 77.2% | – | 75.2% | – |
| VoiceBench | 90.1% | – | 86.4% | – | – | – | 91.4% | – | 85.9% | – |
- Inkling-Small against open-and closed-weights models across the full eval suite. Activated and total parameters are given for scale; a dash means the score was not available at the time of writing.
- SWEBench Verified: Inkling and Inkling-Small’s numbers are reported using a bash-only harness. We use self-reported numbers for external models.
- Terminal Bench 2.1: Inkling and Inkling-Small’s numbers are reported using an internal coding harness. A small number of solutions were found to be contaminated from web search and were assigned a score of 0. We use self-reported numbers for external models where available. Otherwise, we report performance using our internal harness.
- Audio MC: Other models were evaluated internally since they are not on the official leaderboard.
- VoiceBench: VoiceBench uses rule-based, hard-coded string matching for grading, making the evaluation sensitive to output-formatting differences. We therefore added a system message instructing models to follow the expected answer format.
- HLE with tools: We benchmarked Minimax M2.7, Claude 4.5 Haiku, Gemini 3.5 Flash-Lite, and GPT 5.6 Luna using our internal harness.
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