GPT-4o vs Mistral Small 3
Head-to-head specifications
| Metric | GPT-4o | Mistral Small 3 | Difference |
|---|---|---|---|
| MMLU (general capability) | 88.7% | 72.0% | +16.7% |
| Context window | 128K tokens | 32K tokens | — |
| Price (input / output per 1M) | $2.5 / $10 | Open weights | — |
| Access | Proprietary API | Open weights | — |
- GPT-4o leads general capability (MMLU 88.7% vs 72.0%).
- GPT-4o offers the larger context window, useful for long documents and codebases.
Verdict: GPT-4o or Mistral Small 3?
GPT-4o advantages
- General capability (+19%)
- Context window (+75%)
Mistral Small 3 advantages
- No decisive advantage on the tracked metrics.
Which should you choose?
- Choose the GPT-4o if you need the strongest reasoning and accuracy.
- Choose the GPT-4o if you work with long documents or large codebases.
Value for money
Mistral Small 3 is open-weight and can be self-hosted, which can dramatically lower cost at scale versus a per-token API.
GPT-4o vs Mistral Small 3: which should you choose?
GPT-4o — OpenAI large language model (2024) with a 128K-token context window and an MMLU score of 88.7%.
Mistral Small 3 — Mistral AI large language model (2025) with a 32K-token context window and an MMLU score of 72.0%, released with open weights.
GPT-4o vs Mistral Small 3: GPT-4o scores higher on the MMLU benchmark. GPT-4o leads general capability (MMLU 88.7% vs 72.0%). GPT-4o offers the larger context window, useful for long documents and codebases.
Capability and reasoning
On MMLU — a 57-subject benchmark of general knowledge and reasoning — the GPT-4o scores 88.7% versus 72.0%. MMLU is a useful proxy for raw knowledge but does not capture instruction-following, coding, tool use, latency or safety, so treat it as one signal among several.
Context window
The GPT-4o handles up to 128K tokens per request, which sets how much documentation, transcript or code it can reason over at once — decisive for retrieval-augmented and long-document workflows.
Pricing and access
GPT-4o is proprietary api and Mistral Small 3 is open weights. Proprietary models bill per token via API; open-weight models can be self-hosted, trading per-call cost for infrastructure you manage. For production, weigh throughput, rate limits and data-residency needs alongside headline price.
The verdict
Both are credible choices in the ai model comparison space; the specification table above lays out every metric so you can weigh the trade-offs that matter to you. Pick the one whose strengths line up with how you will actually use it.
Frequently asked questions
Is the GPT-4o better than the Mistral Small 3?
GPT-4o is the clearly stronger overall choice, winning most of the dimensions that matter. GPT-4o leads general capability (MMLU 88.7% vs 72.0%).
What is the main difference between the GPT-4o and the Mistral Small 3?
GPT-4o leads general capability (MMLU 88.7% vs 72.0%). GPT-4o offers the larger context window, useful for long documents and codebases.
Which is better value?
Mistral Small 3 is open-weight and can be self-hosted, which can dramatically lower cost at scale versus a per-token API.
Which should I choose?
Choose the GPT-4o if you need the strongest reasoning and accuracy. Choose the GPT-4o if you work with long documents or large codebases.
Methodology
Large language models are compared on the MMLU benchmark (a widely-cited 57-subject test of general knowledge and reasoning, reported as a percentage), maximum context window, and published API pricing per million input and output tokens. Open-weight models can also be self-hosted. Benchmarks capture only part of real-world quality, which also depends on tool use, latency, safety and task fit.