Model profile
Mistral

Mistral: Mistral Small 3

Mistral: Mistral Small 3 is a budget-priced text-first model from Mistral with fast runtime profile, standard context posture, and the clearest fit around long-context research / coding.

Best for: Long-context research / CodingFast latencyStandard contextBudget pricing
Intelligence
12.7

Benchmark blend

Coding
0.252

Dev workflow signal

Context
33K Tokens

Standard

Input Price
$0.10

Budget tier

Decision snapshot
47

Mistral: Mistral Small 3 currently reads as a budget text-first option with standard context and a fast runtime profile.

Overall profile
Use-case specific
Best for
Long-context research / Coding
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
12.7
13

General reasoning and benchmark headroom.

Limited
Speed
155 tok/s
96

TTFT 0.65s

Above average
Context
33K Tokens
48

How much prompt and task state can stay in view.

Situational
Price
$0.10
86

$0.30 output / 1M

Efficient

Editorial Profile

Mistral: Mistral Small 3 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 25Math score 4

Mistral Small 3 is a 24B-parameter language model optimized for low-latency performance across common AI tasks. Released under the Apache 2.0 license, it features both pre-trained and instruction-tuned versions designed...

Identity

Mistral text-first profile

Positioning

Long-context research / Coding with standard context and fast runtime.

Cost posture

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

Strengths
  • 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.

  • Context window is more comfortable for focused tasks than extremely long sessions.

Best fit
  • Focused chat, retrieval-augmented flows, and narrower production tasks.

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Context
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Context
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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
12.7
MMLU Pro
65.2%
GPQA
46.2%
HLE
4.1%
Coding

Software implementation, debugging quality, and coding benchmark signal.

LiveCodeBench
0.252
SciCode
23.6%
Math

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

Math Index
4.3
AIME
8.0%
AIME 2025
4.3%
Math 500
71.5%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
26.4%
TAU2
19.6%
LCR
0.0%

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
33K Tokens
Vision
Text-first
Modalities
text
Tokenizer
Mistral
Max Completion
16384
Moderation
No
Supported Parameters
frequency_penaltymax_tokensmin_ppresence_penaltyrepetition_penaltyresponse_formatseedstoptemperaturetop_ktop_p
Input Modalities
text
Output Modalities
text
Price architecture
Input
per 1M input tokens
$0.10
Output
per 1M output tokens
$0.30
Blended
AA 3:1 mix
$0.15

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.