Mistral Large 3 2512
Mistral's largest general model for enterprise agents, coding, and multilingual reasoning
- Context
- 262K
- Released
- 10mo ago
- Parameters
- —
- Input / 1M tokens
- $0.50
- Output / 1M tokens
- $1.50
- Cached input / 1M tokens
- $0.05
Sources:models.devOpenRouter
Benchmarks
DeepSWE is the primary coding benchmark where an exact model evaluation exists. Other sourced evaluations appear below; missing scores are not zero.
Not evaluated in DeepSWE 1.1 as of 2026-09-22. Results are only shown for exact model versions in the official dataset. Official leaderboard.
No benchmark results have been matched to this model yet.
Specifications
- Developer
- Mistral AI
- Model family
- Mistral Large
- Type
- Language model
- Predecessor
- —
- Successor
- —
- Released
- December 2, 2025
- Model ID
mistralai/mistral-large-2512- Openness
- Open weights
- License
- —
- Parameters
- —
- Input modalities
- text, image, file
- Output modalities
- text
- Modality
- text+image+file->text
- Tokenizer
- Mistral
- Max output tokens
- 210K
- Knowledge cutoff
- 2024-11
- Hugging Face
- —
- Source freshness
- Metadata synced Oct 8, 2026, 10:17 PM UTC
Additional source evidence
- Source model
- mistral/mistral-large-2512
- Source family
- mistral-large
- Last updated
- 2025-12-02
- Knowledge cutoff
- 2024-11
- Model context
- 262K
- Model output limit
- 262K
- Open weights
- Yes
- File attachments
- Yes
- Reasoning
- No
- Tool calling
- Yes
- Temperature control
- Yes
Downloadable weights
Published benchmarks
Complete source record (JSON)
{
"id": "mistral/mistral-large-2512",
"name": "Mistral Large 3",
"description": "Mistral's largest general model for enterprise agents, coding, and multilingual reasoning",
"family": "mistral-large",
"last_updated": "2025-12-02",
"knowledge": "2024-11",
"attachment": true,
"reasoning": false,
"tool_call": true,
"temperature": true,
"modalities": {
"input": [
"text",
"image"
],
"output": [
"text"
]
},
"limit": {
"context": 262144,
"output": 262144
},
"benchmarks": [
{
"name": "Artificial Analysis Coding Index",
"score": 22.7,
"source": "https://openrouter.ai/mistralai/mistral-large-2512/benchmarks",
"metric": "index",
"date": "2026-06-04"
},
{
"name": "SciCode",
"score": 36.2,
"source": "https://openrouter.ai/mistralai/mistral-large-2512/benchmarks",
"metric": "percent correct",
"date": "2026-06-04"
},
{
"name": "Terminal-Bench Hard",
"score": 15.9,
"source": "https://openrouter.ai/mistralai/mistral-large-2512/benchmarks",
"metric": "success rate",
"date": "2026-06-04"
}
],
"release_date": "2025-12-02",
"open_weights": true,
"weights": [
{
"label": "Hugging Face",
"url": "https://huggingface.co/mistralai/Mistral-Large-3-675B-Instruct-2512"
}
]
}Infrastructure
Provider availability
| Provider | Endpoint | Context | Input /M tokens | Output /M tokens | Cached input /M tokens | Cache write /M tokens | Uptime 1d | Data handling |
|---|---|---|---|---|---|---|---|---|
| mistral/zdr | 262K | $0.50 | $1.50 | $0.05 | — | 99.86% | ZDR: YesTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| mistral/zdrBatch pricing | 262K | $0.50 | $1.50 | $0.05 | — | 99.86% | ZDR: NoTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| mistral/eu | 262K | $0.55 | $1.65 | $0.06 | — | 100.00% | ZDR: YesTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| mistral/euBatch pricing | 262K | $0.55 | $1.65 | $0.06 | — | 100.00% | ZDR: NoTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 |
Provider data:OpenRouter
Deployment graph
Can I run this?
Vertical Tabs only shows compatibility when it has structured evidence tying this exact model to hardware, runtime, quantization, memory use, and observed performance.