Model profile
mistralai

Mistral: Mixtral 8x22B Instruct

Mistral: Mixtral 8x22B Instruct is a budget text-first model from mistralai with a heavy runtime profile, standard context posture, and the clearest fit around long-context research / agent workflows.

Best for: Long-context research / Agent workflowsHeavy latencyStandard contextBudget pricing
Intelligence
9.8

Benchmark blend

Coding
0.148

Dev workflow signal

Context
66K Tokens

Standard

Input Price
$0.00

Budget tier

Decision snapshot
38

Mistral: Mixtral 8x22B Instruct currently reads as a budget text-first option with standard context and a heavy runtime profile.

Overall profile
Use-case specific
Best for
Long-context research / Agent workflows
Latency tier
Heavy
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
9.8
10

General reasoning and benchmark headroom.

Limited
Speed
N/A
46

Latency data is partial.

Situational
Context
66K Tokens
64

How much prompt and task state can stay in view.

Competitive
Price
$0.00
86

$0.00 output / 1M

Efficient

Editorial Profile

Mistral: Mixtral 8x22B 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 15Math score 55

Mistral's official instruct fine-tuned version of [Mixtral 8x22B](/models/mistralai/mixtral-8x22b). It uses 39B active parameters out of 141B, offering unparalleled cost efficiency for its size. Its strengths include: - strong math, coding, and reasoning - large context length (64k) - fluency in English, French, Italian, German, and Spanish See benchmarks on the launch announcement [here](https://mistral.ai/news/mixtral-8x22b/). #moe

Identity

mistralai text-first profile

Positioning

Long-context research / Agent workflows with standard context and heavy runtime.

Cost posture

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

Strengths
  • The available source data suggests a balanced profile rather than one dominant edge.

Tradeoffs
  • Budget-friendly input pricing is a strength, but raw capability may vary by workload.

  • Latency profile is better for deliberate runs than rapid back-and-forth chat.

  • 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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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
9.8
MMLU Pro
53.7%
GPQA
33.2%
HLE
4.1%
Coding

Software implementation, debugging quality, and coding benchmark signal.

LiveCodeBench
0.148
SciCode
18.8%
Math

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

AIME
0.0%
Math 500
54.5%

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
66K Tokens
Vision
Text-first
Modalities
text->text, text
Tokenizer
Mistral
Moderation
No
Supported Parameters
frequency_penaltymax_tokenspresence_penaltyresponse_formatseedstopstructured_outputstemperaturetool_choicetoolstop_p
Input Modalities
text
Output Modalities
text
Price architecture
Input
per 1M input tokens
$0.00
Output
per 1M output tokens
$0.00
Blended
AA 3:1 mix
$0.00

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.