Model profile
Mistral

Mistral: Devstral Medium

Mistral: Devstral Medium is a budget-priced text-first model from Mistral with fast runtime profile, extended context posture, and the clearest fit around long-context research / agent workflows.

Best for: Long-context research / Agent workflowsFast latencyExtended contextBudget pricing
Intelligence
18.7

Benchmark blend

Coding
15.9

Dev workflow signal

Context
131K Tokens

Extended

Input Price
$0.40

Budget tier

Decision snapshot
53

Mistral: Devstral Medium currently reads as a budget text-first option with extended context and a fast runtime profile.

Overall profile
Use-case specific
Best for
Long-context research / Agent workflows
Latency tier
Fast
Price tier
Budget
Source coverage
OpenRouterArtificial Analysis

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
18.7
19

General reasoning and benchmark headroom.

Limited
Speed
145 tok/s
99

TTFT 0.44s

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: Devstral Medium 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 16Math score 5

Devstral Medium is a high-performance code generation and agentic reasoning model developed jointly by Mistral AI and All Hands AI. Positioned as a step up from Devstral Small, it achieves...

Identity

Mistral text-first profile

Positioning

Long-context research / Agent workflows 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.

  • Latency and throughput look responsive enough for interactive loops.

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

  • Current metadata points to a text-first profile rather than a broad multimodal one.

Best fit
  • 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
18.7
MMLU Pro
70.8%
GPQA
49.2%
HLE
3.8%
Coding

Software implementation, debugging quality, and coding benchmark signal.

Coding Index
15.9
LiveCodeBench
0.337
SciCode
29.4%
Math

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

Math Index
4.7
AIME
6.7%
AIME 2025
4.7%
Math 500
70.7%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
29.9%
TAU2
19.9%
TerminalBench Hard
9.1%
LCR
28.7%

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
Text-first
Modalities
text
Tokenizer
Mistral
Moderation
No
Supported Parameters
frequency_penaltymax_tokenspresence_penaltyresponse_formatseedstopstructured_outputstemperaturetool_choicetoolstop_p
Input Modalities
text
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.

OR Cache Read
$0.00

Metadata

Raw source tables at the end

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