Gemini 1.5 Pro vs Mistral Large 2
Head-to-head specifications
| Metric | Gemini 1.5 Pro | Mistral Large 2 | Difference |
|---|---|---|---|
| MMLU (general capability) | 85.9% | 84.0% | +1.9% |
| Context window | 2M tokens | 128K tokens | — |
| Price (input / output per 1M) | $1.25 / $5 | $2 / $6 | — |
| Access | Proprietary API | Proprietary API | — |
- Gemini 1.5 Pro leads general capability (MMLU 85.9% vs 84.0%).
- Gemini 1.5 Pro offers the larger context window, useful for long documents and codebases.
Verdict: Gemini 1.5 Pro or Mistral Large 2?
Gemini 1.5 Pro advantages
- Context window (+94%)
- Input cost (+38%)
- Output cost (+17%)
Mistral Large 2 advantages
- No decisive advantage on the tracked metrics.
Which should you choose?
- Choose the Gemini 1.5 Pro if you work with long documents or large codebases.
- Choose the Gemini 1.5 Pro if you process large volumes of input and want the lowest cost.
Value for money
Gemini 1.5 Pro offers more capability per dollar — a better value pick for high-volume use, delivering 1.31× the MMLU-per-cost of the alternative.
Gemini 1.5 Pro vs Mistral Large 2: which should you choose?
Gemini 1.5 Pro — Google large language model (2024) with a 2M-token context window and an MMLU score of 85.9%.
Mistral Large 2 — Mistral AI large language model (2024) with a 128K-token context window and an MMLU score of 84.0%.
Gemini 1.5 Pro vs Mistral Large 2: Gemini 1.5 Pro scores higher on the MMLU benchmark. Gemini 1.5 Pro leads general capability (MMLU 85.9% vs 84.0%). 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 84.0%. 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 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
Gemini 1.5 Pro is proprietary api and Mistral Large 2 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 Gemini 1.5 Pro better than the Mistral Large 2?
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 84.0%).
What is the main difference between the Gemini 1.5 Pro and the Mistral Large 2?
Gemini 1.5 Pro leads general capability (MMLU 85.9% vs 84.0%). Gemini 1.5 Pro offers the larger context window, useful for long documents and codebases.
Which is better value?
Gemini 1.5 Pro offers more capability per dollar — a better value pick for high-volume use, delivering 1.31× the MMLU-per-cost of the alternative.
Which should I choose?
Choose the Gemini 1.5 Pro if you work with long documents or large codebases. Choose the Gemini 1.5 Pro if you process large volumes of input and want the lowest cost.
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.