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 Medium 3.5MistralRemove | |
|---|---|---|
| Provider | Mistral | Mistral |
| Discovery tier | Open weights | Open weights |
| Added | Apr 30, 2026 | Apr 29, 2026 |
| Limits | ||
| Context window | 256K | 256K |
| Max output | 32.77K | 32.77K |
| Pricing, USD per 1M tokens | ||
| Input | $1.50 | $1.50 |
| Output | $7.50 | $7.50 |
| Performance | ||
| Request success (24h) | — | 100.0% |
| Capabilities | ||
| Reasoning | Yes | Yes |
| Tools | Yes | Yes |
| Vision | Yes | Yes |
| Structured output | Yes | Yes |
| Reasoning effort | none, high | none, high |
Public evaluations
Benchmarks
There are no public benchmark results for these models yet.
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
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