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Model profile
Mistral

Mistral Small (Sep '24)

Mistral Small (Sep '24) is a budget text-first model from Mistral with a fast runtime profile, unknown context posture, and the clearest fit around agent workflows / long-context research.

Best for: Agent workflows / Long-context researchFast latencyUnknown contextBudget pricing
Intelligence
10.2

Benchmark blend

Coding
0.141

Dev workflow signal

Context
N/A

Unknown

Input Price
$0.20

Budget tier

Decision snapshot
43

Mistral Small (Sep '24) currently reads as a budget text-first option with unknown context and a fast runtime profile.

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

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
10.2
10

General reasoning and benchmark headroom.

Limited
Speed
174 tok/s
100

TTFT 0.31s

Above average
Context
N/A
34

How much prompt and task state can stay in view.

Limited
Price
$0.20
86

$0.60 output / 1M

Efficient

Editorial Profile

Mistral Small (Sep '24) 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 14Math score 56

The Mistral Small (Sep '24) AI model by Mistral.

Identity

Mistral text-first profile

Positioning

Agent workflows / Long-context research with unknown 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.

Best fit

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

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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
10.2
MMLU Pro
52.9%
GPQA
38.1%
HLE
4.3%
Coding

Software implementation, debugging quality, and coding benchmark signal.

LiveCodeBench
0.141
SciCode
15.6%
Math

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

AIME
6.3%
Math 500
56.3%

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
N/A
Vision
Text-first
Price architecture
Input
per 1M input tokens
$0.20
Output
per 1M output tokens
$0.60
Blended
AA 3:1 mix
$0.30

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.