Gemini 2.5 Flash vs Mistral Large
Pricing, benchmarks, and use case comparison
Quick take
- •Gemini 2.5 Flash is meaningfully stronger at cost efficiency (92 vs 66 on our capability index).
- •Gemini 2.5 Flash is 85% cheaper on input tokens, which compounds fast on high-volume or agentic workloads.
- •Mistral Large has a 128000 context window vs 1,048,576 tokens (1M) input; up to 65,535 output - better for whole-repo or long-document work.
Specs comparison
| Gemini 2.5 Flash | Mistral Large | |
|---|---|---|
| Provider | Google DeepMind | Mistral AI |
| Type | Closed source | Closed source |
| Context window | 1,048,576 tokens (1M) input; up to 65,535 output | ✓128000 |
| Input / 1M tokens | ✓$0.30 | 2.00 |
| Output / 1M tokens | $2.50 | 6.00 |
| Release date | 2025-06 | 2024-02 |
Benchmarks
| Benchmark | Gemini 2.5 Flash | Mistral Large |
|---|---|---|
| Context window | 1M tokens | - |
| Input price | $0.30/1M | - |
| MMLU | - | 84.0% |
| HumanEval | - | 92.0% |
Scores sourced from official provider release posts and independent benchmark aggregators.
Which should you choose?
Choose Gemini 2.5 Flash if...
- →High-volume, latency-sensitive production workloads
- →Chatbots, extraction, classification, and summarization at scale
- →You need decent reasoning but must control costs
Choose Mistral Large if...
- →You need a strong European-built flagship with open weights
- →Your work is multilingual or requires nuanced reasoning
- →You want structured/JSON output and solid coding ability
- →You need the option to self-host for data sovereignty