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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 modelQwen3 30B A3BQwenRemove
ProviderMistralQwen
Discovery tierOpen weightsOpen weights
AddedJan 1, 2024Feb 27, 2025
Limits
Context window32.77K41KBest
Max output26.21K32.77KBest
Average output63 tokens228 tokens
Pricing, USD per 1M tokens
Input$0.20$0.10Best
Output$0.60$0.30Best
Performance
Request success (24h)100.0%Best99.2%
Benchmark indexes
Intelligence Index6.56.6Best
Math Index—21.7
Capabilities
Reasoning effort——

Public evaluations

Benchmarks

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

  • Mistral Saba
  • Qwen3 30B A3B

Intelligence Index

  • Mistral Saba6.5
  • Qwen3 30B A3B6.6

Mistral Saba: Better than 5% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Math Index

    Composite of the math evaluations

    • Mistral Saba: —
    • Qwen3 30B A3B: 21.7

Document reasoning

  • AA-LCRv1.1

    Reasoning across long documents

    • Mistral Saba: —
    • Qwen3 30B A3B: 0.0%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Mistral Saba: 4.3%
    • Qwen3 30B A3B: 4.6%Top

  • GPQA Diamond

    Graduate-level science questions

    • Mistral Saba: 42.4%
    • Qwen3 30B A3B: 51.5%Top

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Mistral Saba: 61.1%
    • Qwen3 30B A3B: 71.0%Top

  • IFBench

    Following precise output instructions

    • Mistral Saba: —
    • Qwen3 30B A3B: 31.9%

Coding

  • LiveCodeBench

    Recent competitive programming problems

    • Mistral Saba: —
    • Qwen3 30B A3B: 32.2%

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Mistral Saba: —
    • Qwen3 30B A3B: 21.7%

  • AIME

    Competition mathematics

    • Mistral Saba: 13.0%
    • Qwen3 30B A3B: 26.0%Top

  • MATH-500

    Competition math problems

    • Mistral Saba: 67.7%
    • Qwen3 30B A3B: 86.3%Top

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Mistral Saba: —
    • Qwen3 30B A3B: 6.8%

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Mistral Saba: —
    • Qwen3 30B A3B: 22.2%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Mistral Saba: —
    • Qwen3 30B A3B: 0.0%

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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