Mistral: Saba

Mistral: Saba

Mistral AILLM

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...

Open SourceAPI AvailableTool Calling

Specifications

Context Window

33K tokens

Input Price/1M

Output Price/1M

Parameters

00

Benchmarks

Mistral: Saba results on the main AI model evaluation benchmarks. Higher scores indicate better performance.

Coding

BenchmarkScoreMaximumMethodology
SciCode24.0100.0

Knowledge

BenchmarkScoreMaximumMethodology
MMLU-Pro61.0100.0

Math

BenchmarkScoreMaximumMethodology
MATH-50067.7100.0Artificial Analysis official API

overall

BenchmarkScoreMaximumMethodology
AA Intelligence Index12.1100.0Artificial Analysis official API

Reasoning

BenchmarkScoreMaximumMethodology
MMLU Pro61.1100.0Artificial Analysis official API
GPQA Diamond42.0100.0Artificial Analysis official API
HLE4.0100.0

Information

Release date
February 17, 2025
Tool Calling
✅ Supported
Vision
❌ Not supported
Audio
❌ Not supported

Full Analysis: Mistral: Saba

What is Mistral: Saba?

Mistral: Saba is an AI model developed by Mistral AI, classified as a large language model (LLM). It focuses on text processing and natural language generation. As an open source model, it is available for download, customization, and on-premises deployment. With a context window of 33K tokens, it is suitable for processing medium-sized documents like articles, reports, and code sections.

Pricing & Costs in 2026

Mistral: Saba does not have public per-token pricing available at this time. Some models offer access via enterprise plans or research programs. Check Mistral AI's official website for up-to-date availability and pricing.

Benchmarks & Performance

Mistral: Saba was evaluated on 7 different benchmarks, covering categories like Coding, Knowledge, Math, overall, Reasoning. Results show moderate performance across available evaluations.

It's important to note that benchmarks measure specific aspects and don't capture the full user experience. Factors like instruction adherence, behavior in long conversations, and real-world task quality vary significantly between models and aren't always reflected in standard scores.

Recommended Use Cases

Mistral: Saba is suitable for a wide range of AI applications: automation with tool calling (API integration, databases, external systems), text generation, summarization, translation, and general assistance.

Comparison with Alternatives

In the 2026 AI model ecosystem, Mistral: Saba competes directly with similarly capable models. Mistral AI competes in this segment against OpenAI, Anthropic, Google, and Meta. The choice between models depends on the specific use case, budget, latency requirements, and need for features like multimodality and tool calling.

For a detailed side-by-side comparison, use our comparison tool or check the overall model ranking.

Frequently Asked Questions

What is 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. Trained on curated regional...

How much does Mistral: Saba cost?

Mistral: Saba does not have public per-token pricing available at this time. Check Mistral AI's official website for up-to-date information.

How does Mistral: Saba compare with other models?

In available benchmarks, Mistral: Saba scored: SciCode: 24/100, MMLU-Pro: 61/100, MATH-500: 67.7/100. See the full table above for a detailed comparison.

Is Mistral: Saba open source?

Yes, Mistral: Saba is an open source model. You can deploy it on-premises, customize it via fine-tuning, and maintain full control over your data. Check the official repository for the specific license.

What is Mistral: Saba best for?

Mistral: Saba excels at general-purpose language tasks. It supports tool calling for API integrations and automation.

Last updated: June 01, 2026 View methodology →