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Devstral 2 123B

Added Dec 9, 2025Open weights

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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 modelGemini 2.0 Pro 0205GoogleRemove
ProviderMistralGoogle
Discovery tierOpen weightsBalanced
AddedDec 9, 2025Feb 5, 2025
Limits
Context window262.14K2.1MBest
Max output65.54KBest8.19K
Average output120 tokens—
Pricing, USD per 1M tokens
Input$0.40Best$1.99
Output$1.40Best$7.96
Cache read—$0.50
Performance
Request success (24h)99.7%—
Benchmark indexes
Intelligence Index8.68.7Best
Coding Index31.3Best25.5
Math Index36.7—
Capabilities
VisionNoYes
Audio inputNoYes
Reasoning effort——

Public evaluations

Benchmarks

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

  • Devstral 2 123B
  • Gemini 2.0 Pro 0205

Intelligence Index

  • Devstral 2 123B8.6
  • Gemini 2.0 Pro 02058.7

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: 31.3Top
    • Gemini 2.0 Pro 0205: 25.5

  • Math Index

    Composite of the math evaluations

    • Devstral 2 123B: 36.7
    • Gemini 2.0 Pro 0205: —

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Devstral 2 123B: 3.1%
    • Gemini 2.0 Pro 0205: —

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Devstral 2 123B: 0.0%
    • Gemini 2.0 Pro 0205: —

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Devstral 2 123B: 482 Elo
    • Gemini 2.0 Pro 0205: —

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Devstral 2 123B: 544 Elo
    • Gemini 2.0 Pro 0205: —

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Devstral 2 123B: 2.4%
    • Gemini 2.0 Pro 0205: —

  • AA-LCRv1.1

    Reasoning across long documents

    • Devstral 2 123B: 32.3%
    • Gemini 2.0 Pro 0205: —

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Devstral 2 123B: 3.6%
    • Gemini 2.0 Pro 0205: 6.2%Top

  • GPQA Diamond

    Graduate-level science questions

    • Devstral 2 123B: 59.4%
    • Gemini 2.0 Pro 0205: 62.2%Top

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Devstral 2 123B: 76.2%
    • Gemini 2.0 Pro 0205: 80.5%Top

  • IFBench

    Following precise output instructions

    • Devstral 2 123B: 38.1%
    • Gemini 2.0 Pro 0205: —

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Devstral 2 123B: 0.0%
    • Gemini 2.0 Pro 0205: —

  • SciCode

    Scientific computing in Python

    • Devstral 2 123B: 32.8%
    • Gemini 2.0 Pro 0205: —

  • LiveCodeBench

    Recent competitive programming problems

    • Devstral 2 123B: 44.8%Top
    • Gemini 2.0 Pro 0205: 34.7%

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Devstral 2 123B: 36.7%
    • Gemini 2.0 Pro 0205: —

  • AIME

    Competition mathematics

    • Devstral 2 123B: —
    • Gemini 2.0 Pro 0205: 36.0%

  • MATH-500

    Competition math problems

    • Devstral 2 123B: —
    • Gemini 2.0 Pro 0205: 92.3%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Devstral 2 123B: 18.9%
    • Gemini 2.0 Pro 0205: —

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Devstral 2 123B: 24.9%
    • Gemini 2.0 Pro 0205: —

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Devstral 2 123B: 32.3%
    • Gemini 2.0 Pro 0205: —

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

Other Mistral models, starting with the same family.