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Devstral 2 123B

Added Dec 9, 2025Open weights

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Use in the app

Devstral 2 123B is a 123 billion parameter model from Mistral AI optimized for coding and development tasks. Features advanced reasoning capabilities for software engineering workflows.

Input
Text
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.40
Output
$1.40

Reference prices as published by the model provider. API example

Specifications

Context window
262.1K
Max output
65.5K
Average output (7 days)
120 tokens
Longer than 11% of 468 models

Side by side

Devstral 2 123B compared

Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.

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Devstral 2 123BMistralThis modelMistral Medium 3.5 ThinkingMistralRemove
ProviderMistralMistral
Discovery tierOpen weightsOpen weights
AddedDec 9, 2025Apr 30, 2026
Limits
Context window262.14KBest256K
Max output65.54KBest32.77K
Average output120 tokens—
Pricing, USD per 1M tokens
Input$0.40Best$1.50
Output$1.40Best$7.50
Performance
Request success (24h)99.7%—
Benchmark indexes
Intelligence Index8.6—
Coding Index31.3—
Math Index36.7—
Capabilities
ReasoningNoYes
ToolsNoYes
VisionNoYes
Structured outputNoYes
Reasoning effort—none, high

Public evaluations

Benchmarks

Independent evaluation results, with how each score ranks among the models in the Noumen catalog.

  • Devstral 2 123B
  • Mistral Medium 3.5 Thinking

Intelligence Index

  • Devstral 2 123B8.6
  • Mistral Medium 3.5 Thinking—

Devstral 2 123B: Better than 15% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Devstral 2 123B: 31.3
    • Mistral Medium 3.5 Thinking: —

  • Math Index

    Composite of the math evaluations

    • Devstral 2 123B: 36.7
    • Mistral Medium 3.5 Thinking: —

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Devstral 2 123B: 3.1%
    • Mistral Medium 3.5 Thinking: —

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Devstral 2 123B: 0.0%
    • Mistral Medium 3.5 Thinking: —

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Devstral 2 123B: 482 Elo
    • Mistral Medium 3.5 Thinking: —

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Devstral 2 123B: 544 Elo
    • Mistral Medium 3.5 Thinking: —

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Devstral 2 123B: 2.4%
    • Mistral Medium 3.5 Thinking: —

  • AA-LCRv1.1

    Reasoning across long documents

    • Devstral 2 123B: 32.3%
    • Mistral Medium 3.5 Thinking: —

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Devstral 2 123B: 3.6%
    • Mistral Medium 3.5 Thinking: —

  • GPQA Diamond

    Graduate-level science questions

    • Devstral 2 123B: 59.4%
    • Mistral Medium 3.5 Thinking: —

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Devstral 2 123B: 76.2%
    • Mistral Medium 3.5 Thinking: —

  • IFBench

    Following precise output instructions

    • Devstral 2 123B: 38.1%
    • Mistral Medium 3.5 Thinking: —

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Devstral 2 123B: 0.0%
    • Mistral Medium 3.5 Thinking: —

  • SciCode

    Scientific computing in Python

    • Devstral 2 123B: 32.8%
    • Mistral Medium 3.5 Thinking: —

  • LiveCodeBench

    Recent competitive programming problems

    • Devstral 2 123B: 44.8%
    • Mistral Medium 3.5 Thinking: —

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Devstral 2 123B: 36.7%
    • Mistral Medium 3.5 Thinking: —

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Devstral 2 123B: 18.9%
    • Mistral Medium 3.5 Thinking: —

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Devstral 2 123B: 24.9%
    • Mistral Medium 3.5 Thinking: —

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Devstral 2 123B: 32.3%
    • Mistral Medium 3.5 Thinking: —

Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis

Live routing data

Performance and reliability

Recent averages across requests routed for this model. Your own speed depends on prompt length, reasoning effort and load.

Prompt cache hits
0.0%
Request success (24h)
99.7%
Reliable

Cache hits are the share of input tokens recently served from a prompt cache across all requests. They are not a forecast of your own hit rate.

Hourly request success · last 72 hoursLowest hour 20.0%
  • Healthy (98% or more)
  • Degraded (90–98%)
  • Down (under 90%)
  • No requests

Developers

Use it through the API

Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.

Read the quickstart
curl https://www.noumen.xyz/api/v1/chat/completions \
  -H "Authorization: Bearer $NOUMEN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "mistralai/devstral-2-123b-instruct-2512",
    "messages": [{ "role": "user", "content": "Explain this stack trace." }]
  }'
import os
from openai import OpenAI

client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
    model="mistralai/devstral-2-123b-instruct-2512",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Mistral

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