Mistral-Small-3.2-24B-Instruct-2506
mistral/small-3-2-24b-instruct-2506Por Mistral · família: mistral-small · lançado 2025-06-20 · data de conhecimento: 2023-10-31
Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
Capacidades
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.
Coding68
- Tool calling40/40
- Structured output20/20
- Reasoning0/10
- Context window (100K → 1M)2/20
- Provider availability6/10
Agents81
- Tool calling35/35
- Structured output25/25
- Reasoning0/15
- Output token limit15/15
- Provider availability6/10
JSON / structured output99
- Structured output / JSON mode50/50
- Tool calling20/20
- Temperature control10/10
- Price-friendly for high-volume19/20
Cost efficiency72
- Headline price (log-scaled)72/95
- Has prompt-cache pricing0/5
Long context46
- Context window (100K → 2M)36/90
- Has published price for full window10/10
Vision83
- Accepts image input50/50
- Context window (10K → 1M)17/30
- Has published price10/10
- Provider availability6/10
Production-readiness86
- Number of independent providers30/40
- 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 | $0.66 < $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 | $1.31 < $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 | $0.35 < $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 | $1.11 < $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 | $1.39 < $0.01 per request | 12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step. |
Detalhes de preço
Preço recomendado de ovhcloud · mistral-small-3.2-24b-instruct-2506
Provedor mais barato: meganova · Unknown entrada + Unknown saída
Disponível em 6 provedores
| Provedor | ID do modelo do provedor | Entrada / 1M | Saída / 1M | Contexto | Lançado |
|---|---|---|---|---|---|
| OVHcloud AI Endpoints ovhcloud | mistral-small-3.2-24b-instruct-2506 | $0.100 | $0.310 | 131K | 2025-06-20 |
| Meganova meganova | mistralai/Mistral-Small-3.2-24B-Instruct-2506 | Unknown | Unknown | 33K | 2025-06-20 |
| GreenPT greenpt | mistral-small-3.2-24b-instruct-2506 | $0.228 | $0.456 | 128K | 2025-06-20 |
| Scaleway scaleway | mistral-small-3.2-24b-instruct-2506 | $0.168 | $0.393 | 128K | 2025-06-20 |
| Cortecs cortecs | mistral-small-3.2-24b-instruct-2506 | $0.100 | $0.312 | 131K | 2025-06-20 |
| Berget.AI berget | mistralai/Mistral-Small-3.2-24B-Instruct-2506 | $0.336 | $0.336 | 32K | 2025-06-20 |
Inconsistências de dados entre provedores
- context_window varies: 128000, 131000, 131072, 32000, 32768
- modalities varies across offerings
Os provedores reportam valores diferentes para este modelo. Os dados rápidos acima usam um provedor representativo; consulte a tabela para detalhes por provedor.
Frequently asked questions
How much does Mistral-Small-3.2-24B-Instruct-2506 cost?
Mistral-Small-3.2-24B-Instruct-2506 costs $0.100 per 1M input tokens and $0.310 per 1M output tokens, sourced from ovhcloud. Cache reads, audio tokens and >200K-context tiers (where applicable) are listed in the Pricing detail block above.
What is the context window of Mistral-Small-3.2-24B-Instruct-2506?
Mistral-Small-3.2-24B-Instruct-2506 has a context window of 131K tokens, with a max output of 131K tokens per reply. This is the total combined size of prompt + completion.
Does Mistral-Small-3.2-24B-Instruct-2506 support tool calling?
Yes. Mistral-Small-3.2-24B-Instruct-2506 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 Mistral-Small-3.2-24B-Instruct-2506 support structured output / JSON mode?
Yes. Mistral-Small-3.2-24B-Instruct-2506 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 Mistral-Small-3.2-24B-Instruct-2506 accept image input?
Yes. Mistral-Small-3.2-24B-Instruct-2506 accepts both text and image input. Vision pricing per image is usually billed on top of the regular token rate — check Mistral's docs for the exact rule.
Is Mistral-Small-3.2-24B-Instruct-2506 open-weight?
Yes. Mistral-Small-3.2-24B-Instruct-2506's weights are publicly available, so you can self-host or fine-tune. Note that open weights ≠ open source — the training data and code are typically not released.
What are the best alternatives to Mistral-Small-3.2-24B-Instruct-2506?
If Mistral-Small-3.2-24B-Instruct-2506 doesn't fit, consider Mistral Large 3, Mistral Small 4, Mistral Medium 3.5. 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
Última atualização:
Pricing and capabilities are refreshed daily and reconciled against each provider's official documentation. Always verify critical production decisions with the provider directly.