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 | 120B |
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. | 64k | 1M |
| 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 | $0.10Crusoe |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $6.00Mistral | $0.40Crusoe |
| $700Mistral · zdr | $40.00Crusoe · bf16 | |
| 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. | — | 51.78% |
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. | — | 71.1% |
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. | — | 67.72% |
LiveCodeBenchCompetitive coding — Coding problems published so recently the AI can't have seen them in training — a contamination-free test of raw programming skill. Higher is better. | — | 88.22% |
Terminal-Bench 2.1Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? Higher is better. | — | 66.7% |
BrowseCompWeb browsing — Can the AI browse the web and track down hard-to-find answers? Higher is better. | — | 78% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 89.52% |
IFBenchInstruction following — Tests whether the AI can follow detailed instructions and satisfy multiple constraints at once. Higher is better. | — | 70.86% |
| Overview | ||
| Company | Mistral | NVIDIA |
| Release date | Apr 10 2024 | Oct 15 2026 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
Mixtral 8×22BvsClaude Haiku 5.5Nemotron 3.5 Super VLvsClaude Haiku 5.5Mixtral 8×22BvsGPT-6.1 SolNemotron 3.5 Super VLvsGPT-6.1 SolMixtral 8×22BvsGemini 4 ArgonNemotron 3.5 Super VLvsGemini 4 ArgonMixtral 8×22BvsMuse Spark OpenNemotron 3.5 Super VLvsMuse Spark OpenMixtral 8×22BvsGrok 4.7Nemotron 3.5 Super VLvsGrok 4.7Mixtral 8×22BvsDeepSeek-V4.1-Flash-1017Nemotron 3.5 Super VLvsDeepSeek-V4.1-Flash-1017Frequently asked questions
Mixtral 8×22B and Nemotron 3.5 Super VL don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Mixtral 8×22B shipped 918 days before Nemotron 3.5 Super VL, so benchmark comparisons should account for the intervening progress.
Mixtral 8×22B has 141B parameters, while Nemotron 3.5 Super VL has 120B. Context windows are 64k (Mixtral 8×22B) vs 1M (Nemotron 3.5 Super VL).
Direct benchmark comparisons are unavailable — Mixtral 8×22B and Nemotron 3.5 Super VL don't publish scores on any of the same benchmarks.