Compare AI models
| Specifications | ||
ParametersThe learned values that shape how a model responds. B means billion and T means trillion. For mixture-of-experts models, the total count is larger than the number active for each token. Parameter count alone does not measure quality. | — | 35B |
Context windowHow much information the model can work with in one request, measured in tokens — pieces of words or other data. This includes your question, attachments, conversation and reply. Limits can vary by model variant and provider. | 32k | 256k |
| API pricingUSD per 1M tokens · lower wins | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | — | $0.375 |
Output priceWhat you pay for the text the model writes back. It is normally the dearer half: producing an answer costs more than reading one. | — | $2.25 |
Cheapest inputLowest input rate across every provider on OpenRouter, the lab included. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | $2.00Mistral | $0.05Darkbloom |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $6.00Mistral | $0.70Darkbloom |
| $700Mistral | $45.00Darkbloom · fp4 | |
| These models have no shared benchmark scores. | ||
SWE-Bench ProAgentic coding — Can the AI fix real bugs in real software? It's handed actual problems from open-source projects and has to write code that genuinely solves them. Higher is better. | — | 49.5% |
SWE-Bench VerifiedCoding — Real coding tasks pulled from open-source projects — the AI has to find and fix actual bugs. A human-checked version of the original SWE-Bench. Higher is better. | — | 73.4% |
SWE-Bench MultilingualMultilingual coding — Like SWE-Bench, but the coding problems span many programming languages, not just one. Tests how broadly the AI can code. Higher is better. | — | 67.2% |
Terminal-Bench 2.0Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? (Version 2.0 of the test.) Higher is better. | — | 51.5% |
Humanity's Last Exam · no toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects, written so you can't just look up the answer. “No tools” means the AI answers on its own. Higher is better. | — | 21.4% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 86% |
CharXiv ReasoningChart reasoning — Can the AI read and reason about complex charts and figures, not just text? Higher is better. | — | 78% |
MMMU-ProMultimodal reasoning — A tougher version of MMMU — college-level questions that mix images, diagrams, and text together. Higher is better. | — | 75.3% |
MMMUMultimodal — Tests the AI on understanding images and text together across many college subjects. Higher is better. | — | 81.7% |
| Overview | ||
| Company | Mistral | Qwen |
| Release date | Feb 26 2024 | Apr 16 2026 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Mistral LargevsClaude Haiku 5.5Qwen3.6vsClaude Haiku 5.5Mistral LargevsGPT-6.1 SolQwen3.6vsGPT-6.1 SolMistral LargevsGemini 4 ArgonQwen3.6vsGemini 4 ArgonMistral LargevsMuse Spark OpenQwen3.6vsMuse Spark OpenMistral LargevsGrok 4.7Qwen3.6vsGrok 4.7Mistral LargevsDeepSeek-V4.1-Flash-1017Qwen3.6vsDeepSeek-V4.1-Flash-1017Frequently asked questions
Mistral Large and Qwen3.6 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only Qwen3.6 has a verified first-party API price: $0.375 per million input tokens and $2.25 per million output tokens. No pay-as-you-go API rate is tracked for Mistral Large. Mistral Large shipped 780 days before Qwen3.6, so benchmark comparisons should account for the intervening progress.
Context windows are 32k (Mistral Large) vs 256k (Qwen3.6). Mistral Large is closed, while Qwen3.6 is open weight.
Direct benchmark comparisons are unavailable — Mistral Large and Qwen3.6 don't publish scores on any of the same benchmarks.