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Mistral · Balanced

Mistral Medium 3

Added Sep 25, 2025

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Use in the app

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.

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Mistral Medium 3MistralThis modelSarvam 105BsarvamRemove
ProviderMistralsarvam
Discovery tierBalancedEfficient
AddedSep 25, 2025May 12, 2026
Limits
Context window131.07K131.07K
Max output32.77KBest4.1K
Average output683 tokens1.1K tokens
Pricing, USD per 1M tokens
Input$0.40$0.054Best
Output$2.00$0.21Best
Cache read—$0.0336
Performance
Throughput105.1 tok/s—
Time to first token2.9 s—
Request success (24h)100.0%—
Benchmark indexes
Intelligence Index9.0Best8.8
Math Index30.3—
Capabilities
ReasoningNoYes
ToolsNoYes
VisionYesNo
Reasoning effort—low, medium, high

Public evaluations

Benchmarks

Independent evaluation results, with how each score ranks among the models in the Noumen catalog.

  • Mistral Medium 3
  • Sarvam 105B

Intelligence Index

  • Mistral Medium 39.0
  • Sarvam 105B8.8

Mistral Medium 3: Better than 16% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Math Index

    Composite of the math evaluations

    • Mistral Medium 3: 30.3
    • Sarvam 105B: —

Document reasoning

  • AA-LCRv1.1

    Reasoning across long documents

    • Mistral Medium 3: 31.3%Top
    • Sarvam 105B: 0.0%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Mistral Medium 3: 4.1%
    • Sarvam 105B: 11.0%Top

  • GPQA Diamond

    Graduate-level science questions

    • Mistral Medium 3: 57.8%
    • Sarvam 105B: 73.8%Top

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Mistral Medium 3: 76.0%
    • Sarvam 105B: —

  • IFBench

    Following precise output instructions

    • Mistral Medium 3: 39.3%Top
    • Sarvam 105B: 34.4%

Coding

  • LiveCodeBench

    Recent competitive programming problems

    • Mistral Medium 3: 40.0%
    • Sarvam 105B: —

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Mistral Medium 3: 30.3%
    • Sarvam 105B: —

  • AIME

    Competition mathematics

    • Mistral Medium 3: 44.0%
    • Sarvam 105B: —

  • MATH-500

    Competition math problems

    • Mistral Medium 3: 90.7%
    • Sarvam 105B: —

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Mistral Medium 3: 3.8%Top
    • Sarvam 105B: 1.5%

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Mistral Medium 3: 24.3%
    • Sarvam 105B: 46.8%Top

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Mistral Medium 3: 31.3%Top
    • Sarvam 105B: 0.0%

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 64% of 254 models
Time to first token
2.9 s
Quicker than 59% of 254 models
Prompt cache hits
41.1%
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.

Hourly request success · last 72 hoursLowest hour 100.0%
  • 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.

Read the quickstart
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

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