Model profile
Mistral

Mistral 7B Instruct

Mistral 7B Instruct is a budget text-first model from Mistral with a fast runtime profile, unknown context posture, and the clearest fit around long-context research / multimodal.

Best for: Long-context research / MultimodalFast latencyUnknown contextBudget pricing
Intelligence
7.4

Benchmark blend

Coding
0.046

Dev workflow signal

Context
N/A

Unknown

Input Price
$0.25

Budget tier

Decision snapshot
40

Mistral 7B Instruct currently reads as a budget text-first option with unknown context and a fast runtime profile.

Overall profile
Use-case specific
Best for
Long-context research / Multimodal
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
7.4
7

General reasoning and benchmark headroom.

Limited
Speed
172 tok/s
100

TTFT 0.27s

Above average
Context
N/A
34

How much prompt and task state can stay in view.

Limited
Price
$0.25
86

$0.25 output / 1M

Efficient

Editorial Profile

Mistral 7B Instruct 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 5Math score 12

The Mistral 7B Instruct AI model by Mistral.

Identity

Mistral text-first profile

Positioning

Long-context research / Multimodal 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
7.4
MMLU Pro
24.5%
GPQA
17.7%
HLE
4.3%
Coding

Software implementation, debugging quality, and coding benchmark signal.

LiveCodeBench
0.046
SciCode
2.4%
Math

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

AIME
0.0%
Math 500
12.1%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
19.9%
TAU2
0.0%
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
N/A
Vision
Text-first
Price architecture
Input
per 1M input tokens
$0.25
Output
per 1M output tokens
$0.25
Blended
AA 3:1 mix
$0.25

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.