Mixtral 8x22B vs Gemini 1.5 Pro
Head-to-head specifications
| Metric | Mixtral 8x22B | Gemini 1.5 Pro | Difference |
|---|---|---|---|
| MMLU (general capability) | 77.8% | 85.9% | -8.1% |
| Context window | 64K tokens | 2M tokens | — |
| Price (input / output per 1M) | Open weights | $1.25 / $5 | — |
| Access | Open weights | Proprietary API | — |
- Gemini 1.5 Pro leads general capability (MMLU 85.9% vs 77.8%).
- Gemini 1.5 Pro offers the larger context window, useful for long documents and codebases.
Verdict: Mixtral 8x22B or Gemini 1.5 Pro?
Mixtral 8x22B advantages
- No decisive advantage on the tracked metrics.
Gemini 1.5 Pro advantages
- General capability (+9%)
- Context window (+97%)
Which should you choose?
- Choose the Gemini 1.5 Pro if you need the strongest reasoning and accuracy.
Value for money
Mixtral 8x22B is open-weight and can be self-hosted, which can dramatically lower cost at scale versus a per-token API.
Mixtral 8x22B vs Gemini 1.5 Pro: which should you choose?
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.
Gemini 1.5 Pro — Google large language model (2024) with a 2M-token context window and an MMLU score of 85.9%.
Mixtral 8x22B vs Gemini 1.5 Pro: Gemini 1.5 Pro scores higher on the MMLU benchmark. Gemini 1.5 Pro leads general capability (MMLU 85.9% vs 77.8%). Gemini 1.5 Pro 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 Gemini 1.5 Pro scores 85.9% versus 77.8%. 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 Gemini 1.5 Pro handles up to 2 million 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
Mixtral 8x22B is open weights and Gemini 1.5 Pro is proprietary api. 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 Mixtral 8x22B better than the Gemini 1.5 Pro?
Gemini 1.5 Pro is the clearly stronger overall choice, winning most of the dimensions that matter. Gemini 1.5 Pro leads general capability (MMLU 85.9% vs 77.8%).
What is the main difference between the Mixtral 8x22B and the Gemini 1.5 Pro?
Gemini 1.5 Pro leads general capability (MMLU 85.9% vs 77.8%). Gemini 1.5 Pro offers the larger context window, useful for long documents and codebases.
Which is better value?
Mixtral 8x22B 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 Gemini 1.5 Pro 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.