Mistral · Open weights
Mistral Small 3.2 24B (2506)
The latest iteration of Mistral Small, version 3.2 (2506) brings enhanced performance and capabilities. With 24 billion parameters, this model delivers state-of-the-art results across text generation tasks with improved efficiency.
- Input
- Text
- Output
- Text
- Discovery tier
- Open weights
Pricing
USD per 1M tokens- Input
- $0.20
- Output
- $0.40
Reference prices as published by the model provider. API example
Specifications
- Context window
- 128K
- Max output
- 16.4K
Side by side
Mistral Small 3.2 24B (2506) compared
Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.
| Mistral Small 3.2 24B (2506)MistralThis model | Mistral Small 4 119B ThinkingMistralRemove | |
|---|---|---|
| Provider | Mistral | Mistral |
| Discovery tier | Open weights | Open weights |
| Added | Apr 15, 2025 | Mar 17, 2026 |
| Limits | ||
| Context window | 128K | 262.14KBest |
| Max output | 16.38K | 16.38K |
| Average output | — | 997 tokens |
| Pricing, USD per 1M tokens | ||
| Input | $0.20Best | $0.40 |
| Output | $0.40Best | $1.40 |
| Performance | ||
| Throughput | — | 126.8 tok/s |
| Time to first token | — | 3.0 s |
| Request success (24h) | 100.0%Best | 95.9% |
| Benchmark indexes | ||
| Intelligence Index | — | 11.3 |
| Coding Index | — | 26.6 |
| 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 Small 3.2 24B (2506)
- Mistral Small 4 119B Thinking
Intelligence Index
- Mistral Small 3.2 24B (2506)—
- Mistral Small 4 119B Thinking11.3
Indexes
Coding Index
Composite of the coding evaluations
- Mistral Small 3.2 24B (2506): —
- Mistral Small 4 119B Thinking: 26.6
Agentic work
AutomationBench-AA
Workflow automation, penalized for guardrail violations
- Mistral Small 3.2 24B (2506): —
- Mistral Small 4 119B Thinking: 1.0%
AutomationBench-AA tasks completed
Workflows finished end to end without a guardrail violation
- Mistral Small 3.2 24B (2506): —
- Mistral Small 4 119B Thinking: 0.0%
AA-Briefcasev1.1
Agentic knowledge work, rated head to head
- Mistral Small 3.2 24B (2506): —
- Mistral Small 4 119B Thinking: 241 Elo
GDPval-AAv2.1
Economically valuable occupational tasks, rated head to head
- Mistral Small 3.2 24B (2506): —
- Mistral Small 4 119B Thinking: 362 Elo
Document reasoning
GDP.pdf
Professional reasoning over PDF documents, all-pass rate
- Mistral Small 3.2 24B (2506): —
- Mistral Small 4 119B Thinking: 0.0%
AA-LCRv1.1
Reasoning across long documents
- Mistral Small 3.2 24B (2506): —
- Mistral Small 4 119B Thinking: 49.7%
MLCR-AA
Long-context reasoning over medical records
- Mistral Small 3.2 24B (2506): —
- Mistral Small 4 119B Thinking: 1.7%
Reasoning and knowledge
Humanity’s Last Exam
Expert-level questions across academic fields
- Mistral Small 3.2 24B (2506): —
- Mistral Small 4 119B Thinking: 9.9%
GPQA Diamond
Graduate-level science questions
- Mistral Small 3.2 24B (2506): —
- Mistral Small 4 119B Thinking: 76.9%
IFBench
Following precise output instructions
- Mistral Small 3.2 24B (2506): —
- Mistral Small 4 119B Thinking: 48.2%
Coding
Terminal-Benchv4.0
Practical software engineering in a terminal
- Mistral Small 3.2 24B (2506): —
- Mistral Small 4 119B Thinking: 0.0%
SciCode
Scientific computing in Python
- Mistral Small 3.2 24B (2506): —
- Mistral Small 4 119B Thinking: 38.8%
Legacy evaluations
Terminal-Bench Hard
Earlier terminal task suite
- Mistral Small 3.2 24B (2506): —
- Mistral Small 4 119B Thinking: 17.4%
τ²-Bench Telecom
Tool use in a customer-support simulation
- Mistral Small 3.2 24B (2506): —
- Mistral Small 4 119B Thinking: 41.2%
AA-LCR (unversioned)
Long-context reasoning, earlier grading
- Mistral Small 3.2 24B (2506): —
- Mistral Small 4 119B Thinking: 49.7%
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.
- 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-small-3.2-24b-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-3.2-24b-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.