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 | — |
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 | 400k |
| 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. | — | 39% |
| Overview | ||
| Company | Mistral | OpenAI |
| Release date | Jul 16 2024 | Sep 15 2025 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
Mathstral 7BvsClaude Haiku 5.5GPT-5-CodexvsClaude Haiku 5.5Mathstral 7BvsGemini 4 ArgonGPT-5-CodexvsGemini 4 ArgonMathstral 7BvsMuse Spark OpenGPT-5-CodexvsMuse Spark OpenMathstral 7BvsGrok 4.7GPT-5-CodexvsGrok 4.7Mathstral 7BvsDeepSeek-V4.1-Flash-1017GPT-5-CodexvsDeepSeek-V4.1-Flash-1017Mathstral 7BvsKimi K3GPT-5-CodexvsKimi K3Frequently asked questions
Mathstral 7B and GPT-5-Codex don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Mathstral 7B shipped 426 days before GPT-5-Codex, so benchmark comparisons should account for the intervening progress.
Context windows are 32k (Mathstral 7B) vs 400k (GPT-5-Codex). Mathstral 7B is open weight, while GPT-5-Codex is closed.
Direct benchmark comparisons are unavailable — Mathstral 7B and GPT-5-Codex don't publish scores on any of the same benchmarks.