Mistral · Open weights
Mistral Large 4
Mistral Large 4 is an open-weight multimodal mixture-of-experts model with 1.05T total and 49B active parameters, built for reasoning, coding, tool use, and structured output. It supports a 512K-token context window.
- Reasoning
- Tools
- Vision
- Structured output
- Input
- Text, Images
- Output
- Text
- Reasoning effort
- none · high
- Discovery tier
- Open weights
Pricing
USD per 1M tokens- Input
- $0.68
- Output
- $2.09
- Cache read
- $0.07
Reference prices as published by the model provider. API example
Specifications
- Context window
- 524.3K
- Max output
- —
Side by side
Mistral Large 4 compared
Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.
| Mistral Large 4MistralThis model | Mistral Large 2411MistralRemove | |
|---|---|---|
| Provider | Mistral | Mistral |
| Discovery tier | Open weights | Balanced |
| Added | Oct 6, 2026 | Jan 1, 2024 |
| Limits | ||
| Context window | 524.29KBest | 128K |
| Max output | — | 102.4K |
| Average output | — | 955 tokens |
| Pricing, USD per 1M tokens | ||
| Input | $0.68Best | $2.01 |
| Output | $2.09Best | $6.00 |
| Cache read | $0.07Best | $0.20 |
| Performance | ||
| Request success (24h) | 68.9% | 100.0%Best |
| Benchmark indexes | ||
| Intelligence Index | — | 5.8 |
| Capabilities | ||
| Reasoning | Yes | No |
| Tools | Yes | No |
| Vision | Yes | No |
| Structured output | Yes | No |
| Reasoning effort | none, high | — |
Public evaluations
Benchmarks
Independent evaluation results, with how each score ranks among the models in the Noumen catalog.
- Mistral Large 4
- Mistral Large 2411
Intelligence Index
- Mistral Large 4—
- Mistral Large 24115.8
Reasoning and knowledge
Humanity’s Last Exam
Expert-level questions across academic fields
- Mistral Large 4: —
- Mistral Large 2411: 3.5%
GPQA Diamond
Graduate-level science questions
- Mistral Large 4: —
- Mistral Large 2411: 35.1%
MMLU-Pro
Multi-subject knowledge and reasoning
- Mistral Large 4: —
- Mistral Large 2411: 51.5%
Coding
LiveCodeBench
Recent competitive programming problems
- Mistral Large 4: —
- Mistral Large 2411: 17.8%
Math
AIME
Competition mathematics
- Mistral Large 4: —
- Mistral Large 2411: 0.0%
MATH-500
Competition math problems
- Mistral Large 4: —
- Mistral Large 2411: 52.7%
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.
- Prompt cache hits
- 57.2%
- About $0.33 per 1M input tokens with caching
- Request success (24h)
- 68.8%
- Failing
Cache hits are the share of input tokens recently served from a prompt cache across all requests. They are not a forecast of your own hit rate.
- 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-4",
"reasoning_effort": "none",
"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-4",
messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)Mistral
Related models
Other Mistral models, starting with the same family.