Inkling

1. General Information

Inkling 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 66-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 and video 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

975B total, 41B active

Numerics support

BF16 and NVFP4

Input modalities

Inkling accepts text input in UTF-8 encoding, image input in any pixel-based format (with each dimension ideally between 40px and 4096px for optimal performance), and audio input in WAV format sampled at 16kHz (ideally under 20 minutes in length for optimal performance).

Output modalities

Inkling generates output as UTF-8 encoded text.

3. Evaluations

Inkling results are reported at effort=0.99. Comparison scores are generated Jul 14, 2026. Nemotron 3 Ultra, Kimi K2.5, Kimi K2.6, GLM 5.2, and DeepSeek V4 Pro are open weights models; Gemini 3.1 Pro, Claude Fable 5, and GPT 5.6 Sol are closed weights models.

		Inkling	Nemotron 3 Ultra	Kimi K2.5	Kimi K2.6	GLM 5.2	DeepSeek V4 Pro	Gemini 3.1 Pro (high)	Claude Fable 5 (max)	GPT 5.6 Sol (xhigh)
Reasoning
	HLE (text only)	29.7%	26.6%	29.4%	35.9%	40.1%	35.9%	44.7%	53.3%	47.2%
	HLE (with tools)	46.0%	37.4%	50.2%	54.0%	54.7%	48.2%	51.4%	64.5%	55.0%
	AIME 2026	97.1%	94.2%	95.8%	96.4%	99.2%	96.7%	98.3%	–	99.9%
	GPQA Diamond	87.2%	86.7%	87.9%	91.1%	89.5%	88.8%	94.1%	92.6%	94.1%
Agentic (coding)
	SWEBench Verified	77.6%	70.7%	76.8%	80.2%	–	80.6%	80.6%	95.0%	–
	SWEBench Pro (Public)	54.3%	46.4%	50.7%	58.6%	62.1%	55.4%	54.2%	80.0%	64.6%
	Terminal Bench 2.1 (Best Harness)	63.8	56.4	51.3	71.3	82.7	64	73.8	84.6	89.5
	GDPVal-AA v2	1233	1164	1009	1190	1514	1307	962	1760	1748
Agentic (general)
	MCP Atlas	74.1%	44.7%	64.0%	68.1%	77.8%	73.2%	78.2%	83.3%	81.8%
	Tau 3 Banking	23.7%	13.8%	13.2%	20.6%	26.8%	25.8%	16.5%	26.8%	33.0%
Factuality
	BrowseComp (w/ Ctx)	77.1%	–	74.9%	83.2%	–	83.4%	85.9%	88.0%	89.4%
	SimpleQA Verified	43.9%	32.4%	36.9%	38.7%	38.1%	57.0%	77.3%	68.3%	71.6%
	AA Omniscience	1.0%	-1.0%	-8.0%	6.0%	4.0%	-10.0%	33.0%	40.0%	22.0%
Chat
	IFBench	79.8%	81.4%	70.2%	76.0%	73.3%	76.5%	77.1%	63.5%	72.7%
	Global-MMLU-Lite	88.7%	85.6%	84.0%	88.4%	89.2%	89.3%	92.7%	93.3%	91.8%
Vision
	MMMU Pro (Standard 10)	73.5%	–	75.0%	79.0%	–	–	82.0%	84.2%	83.0%
	Charxiv RQ	78.1%	–	77.5%	80.4%	–	–	80.2%	86.5%	84.7%
	Charxiv RQ (with python)	82.0%	–	78.7%	86.7%	–	–	89.9%	89.4%	87.8%
Audio
	Audio MC	56.6%	–	–	–	–	–	66.8%	–	–
	MMAU	77.2%	–	–	–	–	–	82.5%	–	–
	VoiceBench	91.4%	–	–	–	–	–	94.3%	–	–
Safety
	FORTRESS (Adversarial)	78.0%	77.6%	54.1%	65.6%	71.3%	36.0%	65.2%	96.0%	82.4%
	FORTRESS (Benign)	95.9%	90.5%	98.3%	97.2%	90.0%	98.5%	98.0%	55.1%	98.1%
	StrongREJECT	98.6%	98.7%	99.5%	99.8%	98.5%	98.6%	98.0%	98.7%	98.5%