Gemini 3.5 vs Mistral Large
Pricing, benchmarks, and use case comparison
Quick take
- •Gemini 3.5 is meaningfully stronger at multimodal (90 vs 65 on our capability index).
- •Gemini 3.5 is 25% 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 (Gemini 3.5 Flash; Pro variant not yet released) - better for whole-repo or long-document work.
Specs comparison
| Gemini 3.5 | Mistral Large | |
|---|---|---|
| Provider | Google DeepMind | Mistral AI |
| Type | Closed source | Closed source |
| Context window | 1,048,576 tokens (Gemini 3.5 Flash; Pro variant not yet released) | ✓128000 |
| Input / 1M tokens | ✓$1.50 | 2.00 |
| Output / 1M tokens | $9.00 | 6.00 |
| Release date | 2026-05 | 2024-02 |
Benchmarks
| Benchmark | Gemini 3.5 | Mistral Large |
|---|---|---|
| Terminal-Bench 2.1 (coding) | 76.2% | - |
| MCP Atlas (tool use) | 83.6% | - |
| CharXiv Reasoning (multimodal) | 84.2% | - |
| MMLU | - | 84.0% |
| HumanEval | - | 92.0% |
Scores sourced from official provider release posts and independent benchmark aggregators.
Which should you choose?
Choose Gemini 3.5 if...
- →You need frontier agent/coding performance without frontier prices
- →Building autonomous agents that make many tool calls
- →High-throughput production workloads that were previously too costly on a Pro model
- →You want a strong default multimodal model with a 1M-token context window
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