Mistral · Open weights
Mistral Small 3.1 24B (2503)
Building upon Mistral Small 3 (2501), Mistral Small 3.1 (2503) adds state-of-the-art vision understanding and enhances long context capabilities up to 128k tokens without compromising text performance. With 24 billion parameters, this model achieves top-tier capabilities in both text and vision tasks.
- Vision
- Input
- Text, Images
- Output
- Text
- Discovery tier
- Open weights
Pricing
USD per 1M tokens- Input
- $0.10
- Output
- $0.30
Reference prices as published by the model provider. API example
Specifications
- Context window
- 128K
- Max output
- 102.4K
Side by side
Mistral Small 3.1 24B (2503) compared
Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.
| Mistral Small 3.1 24B (2503)MistralThis model | Mistral Small 4 119BMistralRemove | |
|---|---|---|
| Provider | Mistral | Mistral |
| Discovery tier | Open weights | Open weights |
| Added | Apr 15, 2025 | Mar 16, 2026 |
| Limits | ||
| Context window | 128K | 262.14KBest |
| Max output | 102.4KBest | 16.38K |
| Average output | — | 92 tokens |
| Pricing, USD per 1M tokens | ||
| Input | $0.10Best | $0.40 |
| Output | $0.30Best | $1.40 |
| Performance | ||
| Throughput | — | 128.8 tok/s |
| Time to first token | — | 0.7 s |
| Request success (24h) | 99.6% | 99.6% |
| Benchmark indexes | ||
| Intelligence Index | — | 9.0 |
| Capabilities | ||
| Reasoning | No | Yes |
| Tools | No | Yes |
| Vision | Yes | Yes |
| Structured output | No | Yes |
| Reasoning effort | — | — |
Public evaluations
Benchmarks
Independent evaluation results, with how each score ranks among the models in the Noumen catalog.
- Mistral Small 3.1 24B (2503)
- Mistral Small 4 119B
Intelligence Index
- Mistral Small 3.1 24B (2503)—
- Mistral Small 4 119B9.0
Document reasoning
AA-LCRv1.1
Reasoning across long documents
- Mistral Small 3.1 24B (2503): —
- Mistral Small 4 119B: 28.3%
Reasoning and knowledge
Humanity’s Last Exam
Expert-level questions across academic fields
- Mistral Small 3.1 24B (2503): —
- Mistral Small 4 119B: 3.8%
GPQA Diamond
Graduate-level science questions
- Mistral Small 3.1 24B (2503): —
- Mistral Small 4 119B: 57.1%
IFBench
Following precise output instructions
- Mistral Small 3.1 24B (2503): —
- Mistral Small 4 119B: 32.8%
Legacy evaluations
Terminal-Bench Hard
Earlier terminal task suite
- Mistral Small 3.1 24B (2503): —
- Mistral Small 4 119B: 10.6%
τ²-Bench Telecom
Tool use in a customer-support simulation
- Mistral Small 3.1 24B (2503): —
- Mistral Small 4 119B: 18.4%
AA-LCR (unversioned)
Long-context reasoning, earlier grading
- Mistral Small 3.1 24B (2503): —
- Mistral Small 4 119B: 28.3%
Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis
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
- 0.0%
- Request success (24h)
- 99.6%
- Reliable
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-small-3.1-24b-instruct",
"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-small-3.1-24b-instruct",
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.