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. | 7B | 117B |
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 | 128k |
| 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. | — | $0.03AkashML |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $0.15Near AI |
| — | $13.50Venice · int4 | |
| 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. | — | 12% |
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. | — | 62.4% |
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. | — | 14.9% |
Humanity's Last Exam · with toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects. “With tools” means the AI is allowed to search the web or run code while answering. Higher is better. | — | 19% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 80.1% |
MMLUGeneral knowledge — A 57-subject multiple-choice exam — history, law, medicine, maths — that was the standard measure of how much a model knows from 2020 until roughly 2024, when frontier scores crowded into the high 80s and labs moved on to harder tests. The scores here were published years apart under different testing setups, so read them as a historical record rather than a like-for-like ranking. Higher is better. | — | 90% |
| Overview | ||
| Company | Mistral | OpenAI |
| Release date | Jul 16 2024 | Aug 5 2025 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
Mathstral 7BvsClaude Haiku 5.5gpt-oss-120bvsClaude Haiku 5.5Mathstral 7BvsGemini 4 Argongpt-oss-120bvsGemini 4 ArgonMathstral 7BvsMuse Spark Opengpt-oss-120bvsMuse Spark OpenMathstral 7BvsGrok 4.7gpt-oss-120bvsGrok 4.7Mathstral 7BvsDeepSeek-V4.1-Flash-1017gpt-oss-120bvsDeepSeek-V4.1-Flash-1017Mathstral 7BvsKimi K3gpt-oss-120bvsKimi K3Frequently asked questions
Mathstral 7B and gpt-oss-120b don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Mathstral 7B shipped 385 days before gpt-oss-120b, so benchmark comparisons should account for the intervening progress.
Mathstral 7B has 7B parameters, while gpt-oss-120b has 117B. Context windows are 32k (Mathstral 7B) vs 128k (gpt-oss-120b).
Direct benchmark comparisons are unavailable — Mathstral 7B and gpt-oss-120b don't publish scores on any of the same benchmarks.