Model profile
Mistral
New in 2026

Mistral: Mistral Medium 3.5

Mistral: Mistral Medium 3.5 is a budget-priced multimodal generalist from Mistral with fast runtime profile, large context posture, and the clearest fit around long-context research / multimodal.

Best for: Long-context research / MultimodalFast latencyLarge contextBudget pricing
Intelligence
30.4

Benchmark blend

Coding
46.9

Dev workflow signal

Context
262K Tokens

Large

Input Price
$1.50

Budget tier

Decision snapshot
63

Mistral: Mistral Medium 3.5 currently reads as a budget multimodal option with large context and a fast runtime profile.

Overall profile
Selective fit
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
30.4
30

General reasoning and benchmark headroom.

Limited
Speed
152 tok/s
96

TTFT 0.66s

Above average
Context
262K Tokens
88

How much prompt and task state can stay in view.

Above average
Price
$1.50
86

$7.50 output / 1M

Efficient

Editorial Profile

Mistral: Mistral Medium 3.5 in one narrative

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

Selective fitCoding score 47Math score N/AVision enabled

Mistral Medium 3.5 is a dense 128B instruction-following model from Mistral AI. It supports text and image inputs with text output, and is designed for agentic workflows, coding, and complex...

Identity

Mistral multimodal profile

Positioning

Long-context research / Multimodal with large 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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Context
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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
30.4
GPQA
74.8%
HLE
13.8%
Coding

Software implementation, debugging quality, and coding benchmark signal.

Coding Index
46.9
SciCode
39.6%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
68.8%
TAU2
94.2%
TerminalBench Hard
33.3%
LCR
65.3%

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
262K Tokens
Vision
Enabled
Modalities
file, image, text
Tokenizer
Mistral
Moderation
No
Supported Parameters
frequency_penaltyinclude_reasoningmax_tokenspresence_penaltyreasoningreasoning_effortresponse_formatseedstopstructured_outputstemperaturetool_choicetoolstop_p
Input Modalities
fileimagetext
Output Modalities
text
Price architecture
Input
per 1M input tokens
$1.50
Output
per 1M output tokens
$7.50
Blended
AA 3:1 mix
$3.00

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.