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. | — | 141B |
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. | 1M | 64k |
| API pricingUSD per 1M tokens · lower wins | ||
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 |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $6.00Mistral |
| — | $700Mistral · zdr | |
| These models have no shared benchmark scores. | ||
BullshitBench v2Nonsense detection — Given a confidently-worded but nonsensical prompt, does the AI spot that it makes no sense and push back — instead of playing along and inventing an answer? The score is how often it clearly called out the nonsense. Higher is better. | 48% | — |
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. | 76.2% | — |
Next.js EvalsNext.js coding — Vercel's open eval of how well AI coding agents build and migrate real Next.js apps — measured as the share of tasks the agent completes successfully. Higher is better. | 52% | — |
ARC-AGI-2Abstract reasoning — Puzzle-style tests of abstract reasoning and pattern-finding — the kind of thing people find easy but AIs often struggle with. Higher is better. | 31.1% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 91.9% | — |
MMMUMultimodal — Tests the AI on understanding images and text together across many college subjects. Higher is better. | 81% | — |
| Overview | ||
| Company | Mistral | |
| Release date | Nov 18 2025 | Apr 10 2024 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Gemini 3.0 ProvsClaude Haiku 5.5Mixtral 8×22BvsClaude Haiku 5.5Gemini 3.0 ProvsGPT-6.1 SolMixtral 8×22BvsGPT-6.1 SolGemini 3.0 ProvsMuse Spark OpenMixtral 8×22BvsMuse Spark OpenGemini 3.0 ProvsGrok 4.7Mixtral 8×22BvsGrok 4.7Gemini 3.0 ProvsDeepSeek-V4.1-Flash-1017Mixtral 8×22BvsDeepSeek-V4.1-Flash-1017Gemini 3.0 ProvsKimi K3Mixtral 8×22BvsKimi K3Frequently asked questions
Gemini 3.0 Pro and Mixtral 8×22B don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Mixtral 8×22B shipped 587 days before Gemini 3.0 Pro, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Gemini 3.0 Pro) vs 64k (Mixtral 8×22B). Gemini 3.0 Pro is closed, while Mixtral 8×22B is open weight.
Direct benchmark comparisons are unavailable — Gemini 3.0 Pro and Mixtral 8×22B don't publish scores on any of the same benchmarks.