Mistral: Saba

Public pricingIntelligence 79/100Medium memoryUso di strumenti

Mistral: Saba è un modello testo pensato per chat generale, analisi e uso in produzione. Unisce bassa latenza e inferenza efficiente, un contesto di 33K tokens e un profilo a basso costo per un lavoro affidabile in general chat, analysis, and production workloads.

Input

$0.20/1M

Output

$0.60/1M

Cached

$0.02/1M

Batch

$0.10/1M

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Technical specifications

Saba at a glance.

Memory

32,768

tokens

Max reply

8,192

tokens

Memory tier

Medium

a long report or a codebase file

Tokenizer

mistral

Released

Feb 2025

Training cutoff

Oct 2024

Availability

Public pricing

Status

active

What it can do

Capabilities & limits.

  • Understands images
  • Deep step-by-step thinking
  • Uses tools / calls functions
  • Strict JSON output
  • Streams replies
  • Fine-tunable on your data

When to pick Saba

  • Agentic workflows that call tools or APIs.
  • High-volume workloads where unit cost matters.

When to look elsewhere

  • Your workload involves images — pick a vision-capable model instead.

Alternatives

Similar models to compare.

FAQ

Saba — the questions we see most.

Pricing, capabilities, alternatives — generated from the same data that powers the calculator above.

Get instant answers from our AI agent

At a typical workload of 50,000 conversations a month with 1,500-token prompts and 800-token replies, Saba costs roughly $39 per month. Input is $0.20 /1M tokens and output is $0.60 /1M tokens.
Saba has a 32,768-token context window (medium memory — a long report or a codebase file). That means you can fit about 6,144 words of input and history in a single call.
Beyond text generation, Saba supports calling functions / tools, strict JSON output, fine-tuning on your own data. It streams replies by default.
Saba was released in February 2025, with training data cut off around October 2024.
Models in a similar class include Ministral 3 14B 2512, Ministral 3 8B 2512, Mistral Small 4. The "Similar models" section below this FAQ links into each.

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