Mistral Small 3.2 24B APIOPEN WEIGHTS128K context

Pricing & specs mirror our live pricing as of July 2026 (pay-as-you-go).

What is Mistral Small 3.2 24B?

Mistral-Small-3.2-24B-Instruct-2506 is an updated 24B parameter model from Mistral optimized for instruction following, repetition reduction, and improved function calling. Compared to the 3.1 release, version 3.2 significantly improves accuracy on...

Mistral Small 3.2 24B on the release timeline

Mistral Small 3.2 24B pricing

Pay-as-you-go on DataLLM Lab — these are our live list prices (identical to the pricing page):

On output price, Mistral Small 3.2 24B is cheaper than 89% of the 309 models in the catalog. It is served by 4 providers; the best combined rate at our last snapshot was ≈ $0.07 / $0.20 per 1M via DeepInfra, with upstream quantizations bf16, fp8.

What Mistral Small 3.2 24B costs per month

Cost = input price × input volume + output price × output volume. The same five workloads run on every model page, so any two compare directly.

Mistral Small 3.2 24B vs alternatives

Specs

How to call Mistral Small 3.2 24B

Copyfrom openai import OpenAI
client = OpenAI(base_url="https://api.datallmlab.com/v1", api_key="YOUR_DATALLM_LAB_KEY")
resp = client.chat.completions.create(
    model="mistralai/mistral-small-3.2-24b-instruct",
    messages=[{"role": "user", "content": "Hello"}],
    # supports tools=[...] and tool_choice
)
print(resp.choices[0].message.content)

Copycurl https://api.datallmlab.com/v1/chat/completions \
  -H "Authorization: Bearer YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"mistralai/mistral-small-3.2-24b-instruct","messages":[{"role":"user","content":"Hello"}]}'

Copyimport OpenAI from "openai";
const client = new OpenAI({ baseURL: "https://api.datallmlab.com/v1", apiKey: process.env.DATALLM_KEY });
const r = await client.chat.completions.create({
  model: "mistralai/mistral-small-3.2-24b-instruct",
  messages: [{ role: "user", content: "Hello" }],
});
console.log(r.choices[0].message.content);

The same key routes Mistral Small 3.2 24B and 300+ other models — switch models by changing one string. How OpenAI-compatible APIs work.

Prompting tips for Mistral Small 3.2 24B

• Be explicit and show the shape of the answer. Spell out format, tone and constraints up front, and include one short example of the output you want — Mistral Small 3.2 24B follows concrete instructions better than abstract ones.
• It reads images. Send image parts alongside your text and ask for specific outputs (named JSON fields, a table, "what changed") rather than "describe this".
• Give it real tools. Pass a tools=[...] schema instead of asking it to "pretend" — let Mistral Small 3.2 24B emit tool calls, execute them, and feed results back for the next turn.
• Constrain JSON with a schema. Use response_format / structured outputs rather than "reply in JSON" to get valid, parseable objects every time.
• Cheap enough to batch. Great for high-volume classification, extraction and drafting — template the prompt, batch requests, and validate outputs programmatically.

Tips are derived from Mistral Small 3.2 24B's actual capabilities — context window, tool & JSON support, modality and price tier — not generic advice.

Open-source tools for Mistral Small 3.2 24B

Popular open-source projects for running and building with Mistral Small 3.2 24B — star counts pulled from GitHub (July 2026).

Listed by GitHub stars; inclusion is by ecosystem relevance (inference engines, agent frameworks and SDKs), not affiliation. Stars change — see each repo for current numbers.

Migrating to Mistral Small 3.2 24B

Coming from OpenAI or another gateway? On an OpenAI-compatible setup the only changes are base_url → https://api.datallmlab.com/v1 and model → mistralai/mistral-small-3.2-24b-instruct. Messages, streaming, tool calls and the rest of your code stay the same. Routing & failover guide.

Self-host or use the API?

Mistral Small 3.2 24B ships open weights (mistralai/Mistral-Small-3.2-24B-Instruct-2506, ~653.1K downloads and 596 likes in the last 30 days), with community quantizations (bf16, fp8) for smaller GPUs, so you can run it yourself. Most teams still use the API: no GPU to provision or keep warm, no inference ops, and instant access across 4 providers with automatic failover. Self-host when data residency or fixed per-token economics matter most.

Rate limits & reliability

Rate limits here are the DataLLM Lab gateway's, not the upstream vendor's. On 429 (rate limited) or 503 (provider busy), retry with exponential backoff; because Mistral Small 3.2 24B is served by 4 providers, requests can fail over to a healthy one automatically. See the error-code guide and failover setup.

Related reading

Frequently asked questions