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 | 128k |
| Overview | ||
| Company | Mistral | OpenAI |
| Release date | Jul 16 2024 | Feb 12 2026 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
Mathstral 7BvsClaude Haiku 5.5GPT-5.3-Codex-SparkvsClaude Haiku 5.5Mathstral 7BvsGemini 4 ArgonGPT-5.3-Codex-SparkvsGemini 4 ArgonMathstral 7BvsMuse Spark OpenGPT-5.3-Codex-SparkvsMuse Spark OpenMathstral 7BvsGrok 4.7GPT-5.3-Codex-SparkvsGrok 4.7Mathstral 7BvsDeepSeek-V4.1-Flash-1017GPT-5.3-Codex-SparkvsDeepSeek-V4.1-Flash-1017Mathstral 7BvsKimi K3GPT-5.3-Codex-SparkvsKimi K3Frequently asked questions
Mathstral 7B and GPT-5.3-Codex-Spark don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Mathstral 7B shipped 576 days before GPT-5.3-Codex-Spark, so benchmark comparisons should account for the intervening progress.
Context windows are 32k (Mathstral 7B) vs 128k (GPT-5.3-Codex-Spark). Mathstral 7B is open weight, while GPT-5.3-Codex-Spark is closed.
Direct benchmark comparisons are unavailable — Mathstral 7B and GPT-5.3-Codex-Spark don't publish scores on any of the same benchmarks.