AI model comparison
Mistral Large 2407 vs Mistral Large
Pricing, context window, benchmarks, and features compared side by side. Or skip the guesswork and send one prompt to both.
Mistral
Mistral Large 2407
This is Mistral AI's flagship model, Mistral Large 2 (version mistral-large-2407). It's a proprietary weights-available model and excels at reasoning, code, JSON, chat, and more.
- Input / 1M
- $2
- Output / 1M
- $6
- Context
- 131K
Mistral
Mistral Large
This is Mistral AI's flagship model, Mistral Large 2 (version `mistral-large-2407`). It's a proprietary weights-available model and excels at reasoning, code, JSON, chat, and more.
- Input / 1M
- $2
- Output / 1M
- $6
- Context
- 128K
At a glance
Quick verdict
How the two models stack up on the things people ask about most.
Lower price
Tie
Same price for a typical chat
Higher intelligence score
Mistral Large 2407
7.6 vs 5.8 on Artificial Analysis
Larger context window
Mistral Large 2407
131K vs 128K tokens
Newer release
Mistral Large 2407
Released November 19, 2024
Benchmarks
Benchmark scores
Independent scores from Artificial Analysis. Higher is better.
Intelligence index
Coding index
Agentic index
Pricing
Mistral Large 2407 vs Mistral Large API pricing
Per-token API rates. Cheaper option highlighted.
| Metric | ||
|---|---|---|
| Input tokensPer 1M tokens | $2 | $2 |
| Output tokensPer 1M tokens | $6 | $6 |
| Cached input (read)Per 1M tokens | $0.20 | $0.20 |
| Cache writePer 1M tokens | Not available | Not available |
What it costs in practice
Estimated cost per 1,000 requests at standard rates. Coding and long-document figures also show the cost when the input is already cached.
Chat message
2K in, 500 out
Coding task
30K in, 4K out
Long document summary
150K in, 2K out
Specs
Context window and capabilities
How much each model can read, how much it can write, and what it accepts as input.
| Metric | ||
|---|---|---|
| Context window | 131K tokens | 128K tokens |
| Max output | 105K tokens | 102K tokens |
| Input types | File, Text | File, Text |
| Output types | Text | Text |
| Reasoning effort levels | Not available | Not available |
| Default reasoning effort | Not available | Not available |
| Release date | November 19, 2024 | February 26, 2024 |
Features
Supported features
API features available for each model.
| Metric | ||
|---|---|---|
| Tool calling | Supported | Supported |
| Structured outputs | Supported | Supported |
| JSON mode | Supported | Supported |
| Reasoning | Not supported | Not supported |
| Temperature | Supported | Supported |
| Stop sequences | Supported | Supported |
| Deterministic seed | Supported | Supported |
| Verbosity control | Not supported | Not supported |
Our take
Mistral Large 2407 scores higher on the Artificial Analysis Intelligence Index (7.6 vs 5.8). Mistral Large 2407 has the larger context window (131K vs 128K tokens). The right pick depends on your workload, so the quickest way to settle it is to send the same prompt to both and compare.
FAQ
Frequently asked questions
Is Mistral Large 2407 or Mistral Large cheaper?
Mistral Large 2407 and Mistral Large cost the same for a typical chat (2K input tokens and 500 output tokens). Mistral Large 2407 costs $2 per 1M input tokens and $6 per 1M output tokens, and Mistral Large costs $2 per 1M input tokens and $6 per 1M output tokens.
Which has a bigger context window, Mistral Large 2407 or Mistral Large?
Mistral Large 2407 supports up to 131K tokens of context, compared with 128K for Mistral Large.
Is Mistral Large 2407 smarter than Mistral Large?
On the Artificial Analysis Intelligence Index, Mistral Large 2407 scores 7.6 and Mistral Large scores 5.8, putting Mistral Large 2407 ahead. Benchmarks don't capture everything, so the best test is running your own prompts through both.
Can I use Mistral Large 2407 and Mistral Large at the same time?
Yes. Shortcut Chat sends one prompt to multiple models at once, so you can see Mistral Large 2407 and Mistral Large answer side by side and keep the better response.
Why choose? Ask both.
Send one prompt to Mistral Large 2407 and Mistral Large at the same time. Compare the answers side by side and keep the best one.