Mistral · Balanced
Mistral Large 2411
Upgrade to Mistral's flagship model. It is fluent in English, French, Spanish, German, and Italian, with high grammatical accuracy, with a long context window.
- Input
- Text
- Output
- Text
- Discovery tier
- Balanced
Pricing
USD per 1M tokens- Input
- $2.01
- Output
- $6.00
- Cache read
- $0.20
Reference prices as published by the model provider. API example
Specifications
- Context window
- 128K
- Max output
- 102.4K
- Average output (7 days)
- 955 tokens
- Longer than 69% of 468 models
Side by side
Mistral Large 2411 compared
Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.
| Mistral Large 2411MistralThis model | Mistral Medium 3.5 ThinkingMistralRemove | |
|---|---|---|
| Provider | Mistral | Mistral |
| Discovery tier | Balanced | Open weights |
| Added | Jan 1, 2024 | Apr 30, 2026 |
| Limits | ||
| Context window | 128K | 256KBest |
| Max output | 102.4KBest | 32.77K |
| Average output | 955 tokens | — |
| Pricing, USD per 1M tokens | ||
| Input | $2.01 | $1.50Best |
| Output | $6.00Best | $7.50 |
| Cache read | $0.20 | — |
| Performance | ||
| Request success (24h) | 100.0% | — |
| Benchmark indexes | ||
| Intelligence Index | 5.8 | — |
| Capabilities | ||
| Reasoning | No | Yes |
| Tools | No | Yes |
| Vision | No | Yes |
| Structured output | No | Yes |
| Reasoning effort | — | none, high |
Public evaluations
Benchmarks
Independent evaluation results, with how each score ranks among the models in the Noumen catalog.
- Mistral Large 2411
- Mistral Medium 3.5 Thinking
Intelligence Index
- Mistral Large 24115.8
- Mistral Medium 3.5 Thinking—
Mistral Large 2411: Better than 3% of 326 scored models
Reasoning and knowledge
Humanity’s Last Exam
Expert-level questions across academic fields
- Mistral Large 2411: 3.5%
- Mistral Medium 3.5 Thinking: —
GPQA Diamond
Graduate-level science questions
- Mistral Large 2411: 35.1%
- Mistral Medium 3.5 Thinking: —
MMLU-Pro
Multi-subject knowledge and reasoning
- Mistral Large 2411: 51.5%
- Mistral Medium 3.5 Thinking: —
Coding
LiveCodeBench
Recent competitive programming problems
- Mistral Large 2411: 17.8%
- Mistral Medium 3.5 Thinking: —
Math
AIME
Competition mathematics
- Mistral Large 2411: 0.0%
- Mistral Medium 3.5 Thinking: —
MATH-500
Competition math problems
- Mistral Large 2411: 52.7%
- Mistral Medium 3.5 Thinking: —
Scores from Artificial Analysis, updated Oct 26, 2026.
Live routing data
Performance and reliability
Recent averages across requests routed for this model. Your own speed depends on prompt length, reasoning effort and load.
- Request success (24h)
- 100.0%
- Reliable
- Healthy (98% or more)
- Degraded (90–98%)
- Down (under 90%)
- No requests
Developers
Use it through the API
Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.
curl https://www.noumen.xyz/api/v1/chat/completions \
-H "Authorization: Bearer $NOUMEN_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "mistralai/mistral-large",
"messages": [{ "role": "user", "content": "Explain this stack trace." }]
}'import os
from openai import OpenAI
client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
model="mistralai/mistral-large",
messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)Mistral
Related models
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