What is Saba API?
Saba is available through AIMLAPI.
Technical Specifications
- Supported inputs: Image, Text
- Output: Text
- Context window: 32,768 tokens / Max output: —
- Endpoint: /chat/completions
Performance Benchmarks
[embed]
Saba API Pricing
- Input: $0.26 / 1M tokens
- Output: $0.78 / 1M tokens
Where to Use Saba
Coding and code review
Generate, debug, and review code across languages, reading whole files or repos in a single prompt.
Agentic and multi-step workflows
Reliable tool-calling and instruction-following suit agent loops that plan, call tools, and iterate.
Complex reasoning
Extended reasoning improves reliability on multi-step math, logic, and technical analysis.
Long-document analysis
Summarize and extract from contracts, papers, and reports without aggressive chunking.
Code Sample
[embed]
Measured on the AI/ML API gateway across live traffic. Figures are medians unless stated. Last updated 2026-10-12.
Conversational support: Mistral Saba is documented as ideal for scenarios requiring swift and precise Arabic responses, including virtual assistants that engage users in natural, real-time conversations in Arabic.
Domain-specific expertise: The model is described as useful when fine-tuned into specialized experts for fields such as energy, financial markets, and healthcare, with responses grounded in Arabic language and culture.
Cultural content creation: Mistral Saba is documented as suited to generating culturally relevant content, including educational resources, and to creating authentic materials that resonate with Middle Eastern audiences.
Translation and language support: The model is described as handling translation and language tasks across Arabic and several related regional languages while preserving cultural context and nuance.
import requests r = requests.post( "https://api.aimlapi.com/v1/chat/completions", headers={"Authorization": "Bearer " + AIMLAPI_KEY}, json={ "model": "mistralai/mistral-saba", "messages": [ { "role": "user", "content": "Hello!" } ] }, ) print(r.json())
const r = await fetch("https://api.aimlapi.com/v1/chat/completions", { method: "POST", headers: { Authorization: `Bearer ${process.env.AIMLAPI_KEY}`, "Content-Type": "application/json", }, body: JSON.stringify({ "model": "mistralai/mistral-saba", "messages": [ { "role": "user", "content": "Hello!" } ] }), }); console.log(await r.json());
curl -X POST https://api.aimlapi.com/v1/chat/completions \ -H "Authorization: Bearer $AIMLAPI_KEY" \ -H "Content-Type: application/json" \ -d '{"model":"mistralai/mistral-saba","messages":[{"role":"user","content":"Hello!"}]}'
OpenAI-compatible — swap the base URL and it works with your existing SDK.
| Type | Price |
|---|---|
| Input | |
| Output | |
| Cached input |
| Benchmark | Score | What it measures | Source | Retrieved |
|---|---|---|---|---|
| Intelligence | 6.5 | Composite score across standardised reasoning, knowledge and problem-solving evaluations, measured independently by Artificial Analysis | Source | September 12, 2026 |
| Model | Input | Output | Context | Best for |
|---|---|---|---|---|
Saba This page | Reasoning + agents | |||
| Reasoning + agents | ||||
| Balanced coding + agents | ||||
| Long-context, multimodal & agentic workflows | ||||
| Reasoning + agents |
Saba has a 32,768 tokens context window.
Saba takes image, text as input and returns text.
Use mistralai/mistral-saba as the model id. Requests go to https://api.aimlapi.com/v1/chat/completions.
Saba is priced at input $0.27508 / 1M tokens, output $0.82524 / 1M tokens, cached input $0.027508 / 1M tokens.
Yes, Saba can stream responses as they are generated.
Yes, Saba accepts image input alongside text.
Saba was built by Mistral AI.
Saba is a chat model from Mistral AI accessible via the endpoint api.aimlapi.com/v1/chat/completions, supporting text and image inputs with text output.
Yes, Saba supports function calling, parallel tool calls, and structured outputs.
