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Mistral · Open weights

Mistral Saba

Added Jan 1, 2024Open weights

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Use in the app

Mistral Saba is a 24B-parameter language model specifically designed for the Middle East and South Asia, delivering accurate and contextually relevant responses while maintaining efficient performance. Trained on curated regional datasets, it supports multiple Indian-origin languages—including Tamil and Malayalam—alongside Arabic. This makes it a versatile option for a range of regional and multilingual applications.

Input
Text
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.20
Output
$0.60

Reference prices as published by the model provider. API example

Specifications

Context window
32.8K
Max output
26.2K
Average output (7 days)
63 tokens
Longer than 6% of 468 models

Side by side

Mistral Saba compared

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

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Mistral SabaMistralThis modelCeleris 1celerisRemove
ProviderMistralceleris
Discovery tierOpen weightsBalanced
AddedJan 1, 2024Jul 25, 2026
Limits
Context window32.77KBest8.19K
Max output26.21KBest8.19K
Average output63 tokens—
Pricing, USD per 1M tokens
Input$0.20Best$2.00
Output$0.60Best$6.00
Performance
Request success (24h)100.0%—
Benchmark indexes
Intelligence Index6.5Best6.3
Coding Index—14.4
Capabilities
Reasoning effort——

Public evaluations

Benchmarks

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

  • Mistral Saba
  • Celeris 1

Intelligence Index

  • Mistral Saba6.5
  • Celeris 16.3

Mistral Saba: Better than 5% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Mistral Saba: —
    • Celeris 1: 14.4

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Mistral Saba: —
    • Celeris 1: 0.4%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Mistral Saba: —
    • Celeris 1: 0.2%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Mistral Saba: —
    • Celeris 1: 151 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Mistral Saba: —
    • Celeris 1: 301 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Mistral Saba: —
    • Celeris 1: 1.2%

  • AA-LCRv1.1

    Reasoning across long documents

    • Mistral Saba: —
    • Celeris 1: 38.0%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Mistral Saba: 4.3%
    • Celeris 1: 6.8%Top

  • GPQA Diamond

    Graduate-level science questions

    • Mistral Saba: 42.4%
    • Celeris 1: 63.1%Top

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Mistral Saba: 61.1%
    • Celeris 1: 78.0%Top

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Mistral Saba: —
    • Celeris 1: 0.0%

  • SciCode

    Scientific computing in Python

    • Mistral Saba: —
    • Celeris 1: 21.6%

Math

  • AIME

    Competition mathematics

    • Mistral Saba: 13.0%
    • Celeris 1: —

  • MATH-500

    Competition math problems

    • Mistral Saba: 67.7%
    • Celeris 1: —

Legacy evaluations

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Mistral Saba: —
    • Celeris 1: 38.0%

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.

Request success (24h)
100.0%
Reliable
Hourly request success · last 72 hoursLowest hour 100.0%
  • 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/mistral-saba",
    "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/mistral-saba",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Mistral

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