Model profile
Mistral

Magistral Medium 1.2

Magistral Medium 1.2 is a mid-range-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 contextMid-range pricing
Intelligence
27.1

Benchmark blend

Coding
21.7

Dev workflow signal

Context
N/A

N/A

Input Price
$2.00

Mid-range tier

Decision snapshot
45

Magistral Medium 1.2 currently reads as a mid-range 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
Mid-range
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
27.1
27

General reasoning and benchmark headroom.

Limited
Speed
95 tok/s
83

TTFT 0.45s

Above average
Context
N/A
N/A

How much prompt and task state can stay in view.

Unavailable
Price
$2.00
62

$5.00 output / 1M

Competitive

Editorial Profile

Magistral Medium 1.2 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 22Math score 82

The Magistral Medium 1.2 AI model by Mistral.

Identity

Mistral text-first profile

Positioning

Agent workflows / Reasoning with partially published context and fast runtime.

Cost posture

Balanced spend profile. Easier to justify in mixed production and exploration workloads.

Strengths
  • Latency and throughput look responsive enough for interactive loops.

Tradeoffs
  • Costs look manageable, but still deserve attention in always-on agents or batch jobs.

  • 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
27.1
MMLU Pro
81.5%
GPQA
73.9%
HLE
9.6%
Coding

Software implementation, debugging quality, and coding benchmark signal.

Coding Index
21.7
LiveCodeBench
0.750
SciCode
39.2%
Math

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

Math Index
82.0
AIME 2025
82.0%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
43.0%
TAU2
52.0%
TerminalBench Hard
12.9%
LCR
51.3%

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
$2.00
Output
per 1M output tokens
$5.00
Blended
AA 3:1 mix
$2.75

This model sits in a balanced spend range. It is easier to justify across both production and exploratory workflows.

Metadata

Raw source tables at the end

Verification details remain available, but the page no longer forces them ahead of the editorial read.