The first open-weights model that can fine-tune itself. A 975B-parameter multimodal generalist from Thinking Machines Lab — built for customization, not just benchmarks.
Inkling isn't the strongest single-benchmark model. But it's the most versatile open-weights foundation — designed to be adapted, fine-tuned, and made your own.
Trained from scratch on text, images, audio, and video. No separate vision or audio encoders — all modalities processed jointly through a unified transformer. Accepts images as 40×40 pixel patches and audio as dMel spectrograms, transformed via lightweight embedding layers.
Text · Image · Audio · VideoBalance performance with token efficiency. Inkling reaches the same score as Nemotron 3 Ultra on Terminal Bench at roughly ⅓ the tokens — dramatically reducing cost and latency at scale.
Effort: 0.2 → 0.99Trained with randomized tool sets and schemas to reduce harness sensitivity. Ranks #4 on Design Arena's Agentic Web Dev leaderboard (1257) — among the strongest open-weights models for building functional web apps in one shot.
77.6% SWEBench VerifiedTrained for calibrated confidence, instruction following, and censorship resistance. Scores 61.1 on ForecastBench (matching Gemini 3.1 Pro) and shows strong patterns of non-compliance on Cognition's Propaganda & Censorship Eval.
Calibrated · HonestStrongest built-in safeguards of any open-weights model on FORTRESS (78.0% adversarial, 95.9% benign). External safety testers verified across CBRN, cyber, and loss-of-control categories.
98.6% StrongREJECTThe full weights are available for fine-tuning on Tinker, Thinking Machines' training platform. Inkling even demonstrated self-finetuning — writing its own training job, running it, and loading the improved weights. Apache 2.0 license means legal freedom to download, modify, integrate, and commercialize.
Apache 2.0 · Tinker · Self-FinetuningAll evals at effort=0.99, temperature=1.0. Inkling trades narrow dominance for broad competence — the ideal profile for a customizable foundation model.
| Benchmark | Inkling | Nemotron 3 Ultra | Kimi K2.6 | GLM 5.2 | GPT 5.6 Sol | Claude Fable 5 |
|---|---|---|---|---|---|---|
| HLE (with tools) | 46.0% | 37.4% | 54.0% | 54.7% | 55.0% | 64.5% |
| AIME 2026 | 97.1% | 94.2% | 96.4% | 99.2% | 99.9% | 99.9% |
| GPQA Diamond | 87.2% | 86.7% | 91.1% | 89.5% | 94.1% | 92.6% |
| SWEBench Verified | 77.6% | 70.7% | 80.2% | 80.0% | 82.2% | 95.0% |
| Terminal Bench 2.1 | 63.8% | 56.4% | 71.3% | 82.7% | 89.5% | 84.6% |
| MCP Atlas | 74.1% | 44.7% | 68.1% | 77.8% | 81.8% | 83.3% |
| Benchmark | Inkling | Qwen3-Omni | Kimi K2.6 | Qwen3.5 Omni+ | Gemini 3.1 Pro |
|---|---|---|---|---|---|
| Audio MC | 56.6% | 24.3% | – | 37.6% | 66.8% |
| MMAU | 77.2% | 77.5% | – | 81.1% | 82.5% |
| VoiceBench | 91.4% | 88.8% | – | 92.4% | 94.3% |
| MMMU Pro (Standard 10) | 73.5% | 60.0% | 79.0% | 71.0% | 82.0% |
| CharXiv RQ (with Python) | 82.0% | – | 86.7% | – | 89.9% |
Terminal Bench 2.1 — Inkling reaches the same score at ~⅓ the tokens of Nemotron 3 Ultra
A Mixture-of-Experts transformer with departures from the DeepSeek-V3 recipe, each chosen for long-context performance and training stability.
We asked Inkling to fine-tune itself to be a lipogram model — never using the letter "e". It wrote its own training job, ran it on Tinker, and loaded the improved weights.
A 276B-parameter MoE model with only 12B active parameters. Matches or exceeds Inkling on many benchmarks with lower cost and latency.
Strongest built-in safeguards of any open-weights model on FORTRESS. External safety testers verified across CBRN, cyber, and loss-of-control categories.
Inkling is just the start — the first release in a model family from Thinking Machines Lab. Download the weights, fine-tune on Tinker, or try it in the playground.