Mistral · Open weights
Mistral Small 3 24B (2501)
Mistral Small 3 24B (2501) hosted by IONOS in Berlin, Germany. Zero data retention.
- Tools
- Input
- Text
- Output
- Text
- Discovery tier
- Open weights
Pricing
USD per 1M tokens- Input
- $0.12
- Output
- $0.35
Reference prices as published by the model provider. API example
Specifications
- Context window
- 32.8K
- Max output
- 8.2K
- Average output (7 days)
- 403 tokens
- Longer than 40% of 468 models
Side by side
Mistral Small 3 24B (2501) compared
Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.
| Mistral Small 3 24B (2501)MistralThis model | Mistral Large 4MistralRemove | |
|---|---|---|
| Provider | Mistral | Mistral |
| Discovery tier | Open weights | Open weights |
| Added | Sep 10, 2026 | Oct 6, 2026 |
| Limits | ||
| Context window | 32.77K | 524.29KBest |
| Max output | 8.19K | — |
| Average output | 403 tokens | — |
| Pricing, USD per 1M tokens | ||
| Input | $0.12Best | $0.68 |
| Output | $0.35Best | $2.09 |
| Cache read | — | $0.07 |
| Performance | ||
| Request success (24h) | — | 68.8% |
| Benchmark indexes | ||
| Intelligence Index | 6.7 | — |
| Math Index | 4.3 | — |
| Capabilities | ||
| Reasoning | No | Yes |
| Tools | Yes | 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 Small 3 24B (2501)
- Mistral Large 4
Intelligence Index
- Mistral Small 3 24B (2501)6.7
- Mistral Large 4—
Mistral Small 3 24B (2501): Better than 6% of 326 scored models
Indexes
Math Index
Composite of the math evaluations
- Mistral Small 3 24B (2501): 4.3
- Mistral Large 4: —
Document reasoning
AA-LCRv1.1
Reasoning across long documents
- Mistral Small 3 24B (2501): 0.0%
- Mistral Large 4: —
Reasoning and knowledge
Humanity’s Last Exam
Expert-level questions across academic fields
- Mistral Small 3 24B (2501): 3.8%
- Mistral Large 4: —
GPQA Diamond
Graduate-level science questions
- Mistral Small 3 24B (2501): 46.2%
- Mistral Large 4: —
MMLU-Pro
Multi-subject knowledge and reasoning
- Mistral Small 3 24B (2501): 65.2%
- Mistral Large 4: —
IFBench
Following precise output instructions
- Mistral Small 3 24B (2501): 26.4%
- Mistral Large 4: —
Coding
LiveCodeBench
Recent competitive programming problems
- Mistral Small 3 24B (2501): 25.2%
- Mistral Large 4: —
Math
AIME 2025
Competition mathematics, 2025 problems
- Mistral Small 3 24B (2501): 4.3%
- Mistral Large 4: —
AIME
Competition mathematics
- Mistral Small 3 24B (2501): 8.0%
- Mistral Large 4: —
MATH-500
Competition math problems
- Mistral Small 3 24B (2501): 71.5%
- Mistral Large 4: —
Legacy evaluations
τ²-Bench Telecom
Tool use in a customer-support simulation
- Mistral Small 3 24B (2501): 19.6%
- Mistral Large 4: —
AA-LCR (unversioned)
Long-context reasoning, earlier grading
- Mistral Small 3 24B (2501): 0.0%
- Mistral Large 4: —
Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis
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-24b-instruct-2501",
"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-24b-instruct-2501",
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.