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Mistral

Saba

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.

At a glance

Input / 1M
$0.20
Output / 1M
$0.60
Context
33K
Max output
26K
Intelligence
6.5
Released
Feb 2025
Text inputFile inputTool calling

Pricing

Saba API pricing

Per-token rates, plus what common tasks actually cost.

Input tokensPer 1M tokens$0.20
Output tokensPer 1M tokens$0.60
Cached input (read)Per 1M tokens$0.02
Cache writePer 1M tokensNot available

Chat message

2K in, 500 out

$0.70

per 1,000 requests

Coding task

30K in, 4K out

$8.40

per 1,000 requests

Long document summary

150K in, 2K out

$31.20

per 1,000 requests

Benchmarks

Saba benchmark scores

Independent scores from Artificial Analysis. Higher is better.

Intelligence index

6.5

Coding index

Not available

Agentic index

Not available

Specs

Context window and capabilities

What it can read, what it can write, and which API features it supports.

Context window
33K tokens
Max output
26K tokens
Input types
File, Text
Output types
Text
Reasoning effort
Not available
Release date
February 17, 2025
Knowledge cutoff
September 30, 2024
  • Tool calling
  • Structured outputs
  • JSON mode
  • Reasoning
  • Temperature
  • Stop sequences
  • Deterministic seed
  • Verbosity control

FAQ

Frequently asked questions

How much does Saba cost?

Saba costs $0.20 per 1M input tokens and $0.60 per 1M output tokens through the API. A typical chat message costs about $0.70 per 1,000 messages.

What is the context window of Saba?

Saba accepts up to 33K tokens of input and can write up to 26K tokens in a single response.

What is the best alternative to Saba?

Mistral Small 4 from Mistral is a strong alternative. It offers: higher intelligence score, larger 262k context, accepts image.

Can I use Saba alongside other models?

Yes. Shortcut Chat sends one prompt to Saba and any other models you pick at the same time, so you can compare answers side by side.

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Try Saba next to every other model.

Use Saba alongside all the other models. Use one at a time, or multiple together. Get the best answer from every AI.