Mistral · Balanced
Mistral Medium 3
Mistral Medium 3 delivers frontier performance while being an order of magnitude less expensive. For instance, the model performs at or above 90% of Claude Sonnet 3.7 on benchmarks across the board at a significantly lower cost. On performance, Mistral Medium 3 also surpasses leading open models such as Llama 4 Maverick and enterprise models such as Cohere Command A. On pricing, the model beats cost leaders such as DeepSeek v3, both in API and self-deployed systems.
- Vision
- Input
- Text, Images
- Output
- Text
- Discovery tier
- Balanced
Pricing
USD per 1M tokens- Input
- $0.40
- Output
- $2.00
Reference prices as published by the model provider. API example
Specifications
- Context window
- 131.1K
- Max output
- 32.8K
- Average output (7 days)
- 683 tokens
- Longer than 61% of 468 models
Side by side
Mistral Medium 3 compared
Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.
| Mistral Medium 3MistralThis model | Mistral Code Agent LatestMistralRemove | |
|---|---|---|
| Provider | Mistral | Mistral |
| Discovery tier | Balanced | Open weights |
| Added | Sep 25, 2025 | Jun 2, 2026 |
| Limits | ||
| Context window | 131.07K | 262.14KBest |
| Max output | 32.77K | 32.77K |
| Average output | 683 tokens | 316 tokens |
| Pricing, USD per 1M tokens | ||
| Input | $0.40 | $0.40 |
| Output | $2.00 | $2.00 |
| Performance | ||
| Throughput | 105.1 tok/s | — |
| Time to first token | 2.9 s | — |
| Request success (24h) | 100.0% | 100.0% |
| Benchmark indexes | ||
| Intelligence Index | 9.0Best | 8.6 |
| Coding Index | — | 31.3 |
| Math Index | 30.3 | 36.7Best |
| Capabilities | ||
| Tools | No | Yes |
| Vision | Yes | No |
| 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 Medium 3
- Mistral Code Agent Latest
Intelligence Index
- Mistral Medium 39.0
- Mistral Code Agent Latest8.6
Mistral Medium 3: Better than 16% of 326 scored models
Indexes
Coding Index
Composite of the coding evaluations
- Mistral Medium 3: —
- Mistral Code Agent Latest: 31.3
Math Index
Composite of the math evaluations
- Mistral Medium 3: 30.3
- Mistral Code Agent Latest: 36.7Top
Agentic work
AutomationBench-AA
Workflow automation, penalized for guardrail violations
- Mistral Medium 3: —
- Mistral Code Agent Latest: 3.1%
AutomationBench-AA tasks completed
Workflows finished end to end without a guardrail violation
- Mistral Medium 3: —
- Mistral Code Agent Latest: 0.0%
AA-Briefcasev1.1
Agentic knowledge work, rated head to head
- Mistral Medium 3: —
- Mistral Code Agent Latest: 482 Elo
GDPval-AAv2.1
Economically valuable occupational tasks, rated head to head
- Mistral Medium 3: —
- Mistral Code Agent Latest: 544 Elo
Document reasoning
GDP.pdf
Professional reasoning over PDF documents, all-pass rate
- Mistral Medium 3: —
- Mistral Code Agent Latest: 2.4%
AA-LCRv1.1
Reasoning across long documents
- Mistral Medium 3: 31.3%
- Mistral Code Agent Latest: 32.3%Top
Reasoning and knowledge
Humanity’s Last Exam
Expert-level questions across academic fields
- Mistral Medium 3: 4.1%Top
- Mistral Code Agent Latest: 3.6%
GPQA Diamond
Graduate-level science questions
- Mistral Medium 3: 57.8%
- Mistral Code Agent Latest: 59.4%Top
MMLU-Pro
Multi-subject knowledge and reasoning
- Mistral Medium 3: 76.0%
- Mistral Code Agent Latest: 76.2%Top
IFBench
Following precise output instructions
- Mistral Medium 3: 39.3%Top
- Mistral Code Agent Latest: 38.1%
Coding
Terminal-Benchv4.0
Practical software engineering in a terminal
- Mistral Medium 3: —
- Mistral Code Agent Latest: 0.0%
SciCode
Scientific computing in Python
- Mistral Medium 3: —
- Mistral Code Agent Latest: 32.8%
LiveCodeBench
Recent competitive programming problems
- Mistral Medium 3: 40.0%
- Mistral Code Agent Latest: 44.8%Top
Math
AIME 2025
Competition mathematics, 2025 problems
- Mistral Medium 3: 30.3%
- Mistral Code Agent Latest: 36.7%Top
AIME
Competition mathematics
- Mistral Medium 3: 44.0%
- Mistral Code Agent Latest: —
MATH-500
Competition math problems
- Mistral Medium 3: 90.7%
- Mistral Code Agent Latest: —
Legacy evaluations
Terminal-Bench Hard
Earlier terminal task suite
- Mistral Medium 3: 3.8%
- Mistral Code Agent Latest: 18.9%Top
τ²-Bench Telecom
Tool use in a customer-support simulation
- Mistral Medium 3: 24.3%
- Mistral Code Agent Latest: 24.9%Top
AA-LCR (unversioned)
Long-context reasoning, earlier grading
- Mistral Medium 3: 31.3%
- Mistral Code Agent Latest: 32.3%Top
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.
- Throughput
- 105.1 tok/s
- Faster than 65% of 254 models
- Time to first token
- 2.9 s
- Quicker than 59% of 254 models
- Prompt cache hits
- 86.4%
- Request success (24h)
- 100.0%
- 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-medium-3",
"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",
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.