Model profile
Mistral

Ministral 3 14B

Ministral 3 14B is a budget-priced text-first model from Mistral with fast runtime profile, partial context coverage, and the clearest fit around agent workflows / reasoning.

Best for: Agent workflows / ReasoningFast latencyN/A contextBudget pricing
Intelligence
16.0

Benchmark blend

Coding
10.9

Dev workflow signal

Context
N/A

N/A

Input Price
$0.20

Budget tier

Decision snapshot
44

Ministral 3 14B currently reads as a budget text-first option with partially published context and a fast runtime profile.

Overall profile
Use-case specific
Best for
Agent workflows / Reasoning
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
16.0
16

General reasoning and benchmark headroom.

Limited
Speed
110 tok/s
89

TTFT 0.33s

Above average
Context
N/A
N/A

How much prompt and task state can stay in view.

Unavailable
Price
$0.20
86

$0.20 output / 1M

Efficient

Editorial Profile

Ministral 3 14B 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 11Math score 30

The Ministral 3 14B AI model by Mistral.

Identity

Mistral text-first profile

Positioning

Agent workflows / Reasoning with partially published 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 limits are only partially published, so long-session planning needs extra validation.

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
16.0
MMLU Pro
69.3%
GPQA
57.2%
HLE
4.6%
Coding

Software implementation, debugging quality, and coding benchmark signal.

Coding Index
10.9
LiveCodeBench
0.351
SciCode
23.6%
Math

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

Math Index
30.0
AIME 2025
30.0%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
32.0%
TAU2
27.2%
TerminalBench Hard
4.5%
LCR
22.0%

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

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.