Mistral Small 3 vs Mixtral 8x22B
Head-to-head specifications
| Metric | Mistral Small 3 | Mixtral 8x22B | Difference |
|---|---|---|---|
| MMLU (general capability) | 72.0% | 77.8% | -5.8% |
| Context window | 32K tokens | 64K tokens | — |
| Price (input / output per 1M) | Open weights | Open weights | — |
| Access | Open weights | Open weights | — |
- Mixtral 8x22B leads general capability (MMLU 77.8% vs 72.0%).
- Mixtral 8x22B offers the larger context window, useful for long documents and codebases.
Verdict: Mistral Small 3 or Mixtral 8x22B?
Mistral Small 3 advantages
- No decisive advantage on the tracked metrics.
Mixtral 8x22B advantages
- General capability (+7%)
- Context window (+50%)
Which should you choose?
- Choose the Mixtral 8x22B if you need the strongest reasoning and accuracy.
Value for money
Both are open-weight models you can self-host, so running cost depends on your own infrastructure rather than API pricing.
Mistral Small 3 vs Mixtral 8x22B: which should you choose?
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.
Mixtral 8x22B — Mistral AI large language model (2024) with a 64K-token context window and an MMLU score of 77.8%, released with open weights.
Mistral Small 3 vs Mixtral 8x22B: Mixtral 8x22B scores higher on the MMLU benchmark. Mixtral 8x22B leads general capability (MMLU 77.8% vs 72.0%). Mixtral 8x22B 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 Mixtral 8x22B scores 77.8% 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 Mixtral 8x22B handles up to 64K 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
Mistral Small 3 is open weights and Mixtral 8x22B 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 Mistral Small 3 better than the Mixtral 8x22B?
Mixtral 8x22B is the clearly stronger overall choice, winning most of the dimensions that matter. Mixtral 8x22B leads general capability (MMLU 77.8% vs 72.0%).
What is the main difference between the Mistral Small 3 and the Mixtral 8x22B?
Mixtral 8x22B leads general capability (MMLU 77.8% vs 72.0%). Mixtral 8x22B offers the larger context window, useful for long documents and codebases.
Which is better value?
Both are open-weight models you can self-host, so running cost depends on your own infrastructure rather than API pricing.
Which should I choose?
Choose the Mixtral 8x22B if you need the strongest reasoning and accuracy.
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.