Saba
mistral/saba提供： Mistral · ファミリー: mistral · リリース 2025-02-17 · 知識カットオフ: 2024-09-30
Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
機能一覧
Model fit scores
0–100 · higher is betterThese scores reward declared capabilities, context size, price and provider availability — they are not benchmark results. Use them as a directional signal alongside your own evaluation.
Coding63
- Tool calling40/40
- Structured output20/20
- Reasoning0/10
- Context window (100K → 1M)0/20
- Provider availability3/10
Agents78
- Tool calling35/35
- Structured output25/25
- Reasoning0/15
- Output token limit15/15
- Provider availability3/10
JSON / structured output98
- Structured output / JSON mode50/50
- Tool calling20/20
- Temperature control10/10
- Price-friendly for high-volume18/20
Cost efficiency65
- Headline price (log-scaled)65/95
- Has prompt-cache pricing0/5
Long context0
- Context ≥ 100K0/100
Production-readiness71
- Number of independent providers15/40
- Has published per-token price20/20
- Context window ≥ 8K15/15
- No data inconsistencies across providers6/10
- Official model (not derivative)15/15
Cost Efficiency Index
Open full calculator →Estimated cost using the recommended provider's headline rate. Each scenario fixes average input/output tokens — the assumptions are shown in the third column.
| Scenario | Cost | Assumption |
|---|---|---|
RAG answer per 1,000 RAG answers | $1.29 < $0.01 per request | 5K input tokens (query + 4 retrieved chunks of ~1K each) and a 500-token answer. Typical SaaS knowledge-base bot. |
Support ticket triage per 10,000 tickets | $2.58 < $0.01 per request | 1K input tokens (ticket body + system prompt) and a 100-token JSON classification reply. High-volume customer support. |
Data extraction per 1,000 documents | $0.70 < $0.01 per request | 2K input tokens (a single document page) and a 500-token JSON extraction. ETL / invoice / form pipelines. |
Code review per 1,000 PRs | $2.19 < $0.01 per request | 8K input tokens (diff + surrounding files) and a 1K-token review comment. PR-bot workloads. |
Agent step per 1,000 steps | $2.74 < $0.01 per request | 12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step. |
料金詳細
推奨料金 (提供元): nano-gpt · mistralai/mistral-saba
3 か所で利用可能
| プロバイダー | プロバイダーモデルID | 入力 / 1M | 出力 / 1M | コンテキスト | リリース日 |
|---|---|---|---|---|---|
| OpenRouter openrouter | mistralai/mistral-saba | $0.200 | $0.600 | 33K | 2025-02-17 |
| NanoGPT nano-gpt | mistralai/mistral-saba | $0.199 | $0.595 | 32K | 2025-02-17 |
| Kilo Gateway kilo | mistralai/mistral-saba | $0.200 | $0.600 | 33K | 2025-02-17 |
プロバイダー間でデータに差異
- context_window varies: 32000, 32768
- modalities varies across offerings
プロバイダーごとに本モデルの値が異なります。上部の「主要数値」は代表的プロバイダーを使用しています。詳細は表をご確認ください。
Frequently asked questions
How much does Saba cost?
Saba costs $0.199 per 1M input tokens and $0.595 per 1M output tokens, sourced from nano-gpt. Cache reads, audio tokens and >200K-context tiers (where applicable) are listed in the Pricing detail block above.
What is the context window of Saba?
Saba has a context window of 33K tokens, with a max output of 33K tokens per reply. This is the total combined size of prompt + completion.
Does Saba support tool calling?
Yes. Saba supports tool calling (function calling). This makes it suitable for production agent and automation workloads where the model has to invoke external functions reliably.
Does Saba support structured output / JSON mode?
Yes. Saba supports structured output / JSON-schema-constrained decoding. This makes it suitable for production agent and automation workloads where the model has to invoke external functions reliably.
Can Saba accept image input?
No. Saba only accepts text, pdf as input. If you need image input, see our /capabilities/vision list for current vision-capable models.
Is Saba open-weight?
No. Saba is a proprietary model — only Mistral (and any approved hosting partners) can serve it. The pricing above reflects the cheapest API access.
What are the best alternatives to Saba?
If Saba doesn't fit, consider Mistral Nemo, Mistral Nemo, Mistral Large 2.1. Each one targets the same use case — see the Related links below for direct head-to-head pages.
Where does this data come from?
All numbers are normalised into a single canonical model record and reconciled with each provider's official documentation. We re-pull daily and write any changes (price, context, capability) to the /changelog page.
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Capability lists this model is in
最終更新：
Pricing and capabilities are refreshed daily and reconciled against each provider's official documentation. Always verify critical production decisions with the provider directly.