Model profile
mistralai

Mistral: Mistral Medium 3.1

Mistral: Mistral Medium 3.1 is a budget multimodal generalist from mistralai with a fast runtime profile, extended context posture, and the clearest fit around long-context research / multimodal.

Best for: Long-context research / MultimodalFast latencyExtended contextBudget pricing
Intelligence
21.3

Benchmark blend

Coding
18.3

Dev workflow signal

Context
131K Tokens

Extended

Input Price
$0.40

Budget tier

Decision snapshot
50

Mistral: Mistral Medium 3.1 currently reads as a budget multimodal option with extended context and a fast runtime profile.

Overall profile
Use-case specific
Best for
Long-context research / Multimodal
Latency tier
Fast
Price tier
Budget
Source coverage
OpenRouterArtificial AnalysisVision signal

Decision Strip

Decision rail before the raw tables

Core buy-side signals stay in one pass. The rest of the page expands only after intelligence, speed, context, and price are clear.

Intelligence
21.3
21

General reasoning and benchmark headroom.

Limited
Speed
81 tok/s
78

TTFT 0.42s

Above average
Context
131K Tokens
76

How much prompt and task state can stay in view.

Competitive
Price
$0.40
86

$2.00 output / 1M

Efficient

Editorial Profile

Mistral: Mistral Medium 3.1 in one narrative

Positioning, tradeoffs, and fit are consolidated into one read instead of repeating the same story across separate cards.

Use-case specificCoding score 18Math score 38Vision enabled

Mistral Medium 3.1 is an updated version of Mistral Medium 3, which is a high-performance enterprise-grade language model designed to deliver frontier-level capabilities at significantly reduced operational cost. It balances state-of-the-art reasoning and multimodal performance with 8× lower cost compared to traditional large models, making it suitable for scalable deployments across professional and industrial use cases. The model excels in domains such as coding, STEM reasoning, and enterprise adaptation. It supports hybrid, on-prem, and in-VPC deployments and is optimized for integration into custom workflows. Mistral Medium 3.1 offers competitive accuracy relative to larger models like Claude Sonnet 3.5/3.7, Llama 4 Maverick, and Command R+, while maintaining broad compatibility across cloud environments.

Identity

mistralai multimodal profile

Positioning

Long-context research / Multimodal with extended context and fast runtime.

Cost posture

Efficient spend profile. More comfortable for sustained prompt volume if the capability fit is right.

Strengths
  • Large context headroom supports repo-wide prompts and long research sessions.

  • Vision-capable routing opens up multimodal review and extraction workflows.

  • Latency and throughput look responsive enough for interactive loops.

Tradeoffs
  • Budget-friendly input pricing is a strength, but raw capability may vary by workload.

Best fit
  • Image-grounded review, multimodal extraction, and UI audit workflows.

  • Long-context summarization, repo analysis, and policy or document review.

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Benchmarks

Grouped by job-to-be-done

Only benchmark categories with actual signal are shown. Secondary values stay as simple definitions instead of nested micro-cards.

General intelligence

Broad reasoning, knowledge depth, and flagship benchmark posture.

Intelligence Index
21.3
MMLU Pro
68.3%
GPQA
58.8%
HLE
4.4%
Coding

Software implementation, debugging quality, and coding benchmark signal.

Coding Index
18.3
LiveCodeBench
0.406
SciCode
33.8%
Math

Formal reasoning, structured problem solving, and competition-style math.

Math Index
38.3
AIME 2025
38.3%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
39.8%
TAU2
40.6%
TerminalBench Hard
10.6%
LCR
19.7%

Specs & Pricing

Technical snapshot and cost posture

Specs stay neutral, pricing gets emphasis through values rather than extra containers. Raw provider internals remain in metadata at the end.

Technical snapshot
Context Window
131K Tokens
Vision
Enabled
Modalities
text, image->text, image
Tokenizer
Mistral
Moderation
No
Supported Parameters
frequency_penaltymax_tokenspresence_penaltyresponse_formatseedstopstructured_outputstemperaturetool_choicetoolstop_p
Input Modalities
textimage
Output Modalities
text
Price architecture
Input
per 1M input tokens
$0.40
Output
per 1M output tokens
$2.00
Blended
AA 3:1 mix
$0.80

This model is relatively efficient on price. It is the easier fit when sustained prompt volume matters.

Metadata

Raw source tables at the end

Verification details remain available, but the page no longer forces them ahead of the editorial read.