Mistral · Open weights
Mistral Medium 3.5 Thinking
Mistral Medium 3.5 with reasoning enabled by default (reasoning_effort=high), for complex coding, agentic, and multi-step reasoning prompts.
- Reasoning
- Tools
- Vision
- Structured output
- Input
- Text, Images
- Output
- Text
- Reasoning effort
- none · high
- Discovery tier
- Open weights
Pricing
USD per 1M tokens- Input
- $1.50
- Output
- $7.50
Reference prices as published by the model provider. API example
Specifications
- Context window
- 256K
- Max output
- 32.8K
Side by side
Mistral Medium 3.5 Thinking compared
Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.
| Mistral Medium 3.5 ThinkingMistralThis model | Mistral Code Agent LatestMistralRemove | |
|---|---|---|
| Provider | Mistral | Mistral |
| Discovery tier | Open weights | Open weights |
| Added | Apr 30, 2026 | Jun 2, 2026 |
| Limits | ||
| Context window | 256K | 262.14KBest |
| Max output | 32.77K | 32.77K |
| Average output | — | 316 tokens |
| Pricing, USD per 1M tokens | ||
| Input | $1.50 | $0.40Best |
| Output | $7.50 | $2.00Best |
| Performance | ||
| Request success (24h) | — | 100.0% |
| Benchmark indexes | ||
| Intelligence Index | — | 8.6 |
| Coding Index | — | 31.3 |
| Math Index | — | 36.7 |
| Capabilities | ||
| Reasoning | Yes | No |
| Tools | Yes | Yes |
| Vision | Yes | No |
| Structured output | Yes | Yes |
| Reasoning effort | none, high | — |
Public evaluations
Benchmarks
Independent evaluation results, with how each score ranks among the models in the Noumen catalog.
- Mistral Medium 3.5 Thinking
- Mistral Code Agent Latest
Intelligence Index
- Mistral Medium 3.5 Thinking—
- Mistral Code Agent Latest8.6
Indexes
Coding Index
Composite of the coding evaluations
- Mistral Medium 3.5 Thinking: —
- Mistral Code Agent Latest: 31.3
Math Index
Composite of the math evaluations
- Mistral Medium 3.5 Thinking: —
- Mistral Code Agent Latest: 36.7
Agentic work
AutomationBench-AA
Workflow automation, penalized for guardrail violations
- Mistral Medium 3.5 Thinking: —
- Mistral Code Agent Latest: 3.1%
AutomationBench-AA tasks completed
Workflows finished end to end without a guardrail violation
- Mistral Medium 3.5 Thinking: —
- Mistral Code Agent Latest: 0.0%
AA-Briefcasev1.1
Agentic knowledge work, rated head to head
- Mistral Medium 3.5 Thinking: —
- Mistral Code Agent Latest: 482 Elo
GDPval-AAv2.1
Economically valuable occupational tasks, rated head to head
- Mistral Medium 3.5 Thinking: —
- Mistral Code Agent Latest: 544 Elo
Document reasoning
GDP.pdf
Professional reasoning over PDF documents, all-pass rate
- Mistral Medium 3.5 Thinking: —
- Mistral Code Agent Latest: 2.4%
AA-LCRv1.1
Reasoning across long documents
- Mistral Medium 3.5 Thinking: —
- Mistral Code Agent Latest: 32.3%
Reasoning and knowledge
Humanity’s Last Exam
Expert-level questions across academic fields
- Mistral Medium 3.5 Thinking: —
- Mistral Code Agent Latest: 3.6%
GPQA Diamond
Graduate-level science questions
- Mistral Medium 3.5 Thinking: —
- Mistral Code Agent Latest: 59.4%
MMLU-Pro
Multi-subject knowledge and reasoning
- Mistral Medium 3.5 Thinking: —
- Mistral Code Agent Latest: 76.2%
IFBench
Following precise output instructions
- Mistral Medium 3.5 Thinking: —
- Mistral Code Agent Latest: 38.1%
Coding
Terminal-Benchv4.0
Practical software engineering in a terminal
- Mistral Medium 3.5 Thinking: —
- Mistral Code Agent Latest: 0.0%
SciCode
Scientific computing in Python
- Mistral Medium 3.5 Thinking: —
- Mistral Code Agent Latest: 32.8%
LiveCodeBench
Recent competitive programming problems
- Mistral Medium 3.5 Thinking: —
- Mistral Code Agent Latest: 44.8%
Math
AIME 2025
Competition mathematics, 2025 problems
- Mistral Medium 3.5 Thinking: —
- Mistral Code Agent Latest: 36.7%
Legacy evaluations
Terminal-Bench Hard
Earlier terminal task suite
- Mistral Medium 3.5 Thinking: —
- Mistral Code Agent Latest: 18.9%
τ²-Bench Telecom
Tool use in a customer-support simulation
- Mistral Medium 3.5 Thinking: —
- Mistral Code Agent Latest: 24.9%
AA-LCR (unversioned)
Long-context reasoning, earlier grading
- Mistral Medium 3.5 Thinking: —
- Mistral Code Agent Latest: 32.3%
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-medium-3.5:thinking",
"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-medium-3.5:thinking",
messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)Mistral
Related models
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