Mistralai Mistral Small 3.2 24B Instruct 2506

DeepInfra chat

Mistralai Mistral Small 3.2 24B Instruct 2506 is a DeepInfra chat model.It supports a 128,000-token context windowwith up to 128,000 output tokens.Input is priced at $0.0750/M tokens and output at $0.200/M tokens. Capabilities include function calling. Route Mistralai Mistral Small 3.2 24B Instruct 2506 via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.

Pricing source: litellm Last verified: May 12, 2026 View source ↗
Cost calculator

Estimate Mistralai Mistral Small 3.2 24B Instruct 2506 spend

Pick a workload, fine-tune the sliders, and see the monthly bill.

~3K in / ~400 out · 5K req/day
3,000
0128,000
400
0128,000
5,000
01,000,000
Per request
$0.000305
in $0.000225 · out $0.000080
Per day
$1.53
5,000 requests
Per month
$46.42
152,188 requests

Estimate uses $0.0750/M input · $0.2000/M output. Provider pricing changes. Production costs vary with retries, streaming overhead, and tool-call rounds.
Want this for free? Cache + route via Agent Command Center — first 100K requests and 100K cache hits free every month.

Pricing

Per-token rates, expressed in USD per 1M tokens. Verified May 12, 2026.

Input $0.0750/M
Output $0.200/M

Limits

Context window
128,000 tokens
Max input
128,000 tokens
Max output
128,000 tokens
Modalities
text

Capabilities

  • Function calling ✓ supported
  • Parallel tool calls — not advertised
  • Vision input — not advertised
  • Audio input — not advertised
  • Audio output — not advertised
  • PDF input — not advertised
  • Streaming ✓ supported
  • Structured output — not advertised
  • Prompt caching — not advertised
  • Reasoning — not advertised

Where it's strong

  • +agentic workflows that depend on reliable tool calls

Watch out for

  • !high cost — input + output rates are in the top 91% of priced chat peers; consider a cheaper sibling for high-volume workloads
  • !strict structured output — no JSON-schema enforcement, expect retry loops

Benchmarks pending

We haven't logged public benchmark scores for Mistralai Mistral Small 3.2 24B Instruct 2506 yet. Have one to contribute? Submit a source — citations help us prioritise.

Try it

Call Mistralai Mistral Small 3.2 24B Instruct 2506 via Agent Command Center

One OpenAI-compatible endpoint. Routing, fallback, semantic caching, guardrails, and cost tracking come along for the ride. First 100K requests + 100K cache hits free every month.

SDK
Native Future AGI client (agentcc / @agentcc/client). Per-call metadata — provider, cost, latency, cache hit, request id — is returned on x-agentcc-* response headers, so any HTTP client can read it.
# Mistralai Mistral Small 3.2 24B Instruct 2506 via the Agent Command Center Python SDK
# pip install agentcc
import os
from agentcc import AgentCC

client = AgentCC(
    api_key=os.environ["AGENTCC_API_KEY"],   # from app.futureagi.com → Settings → API Keys
    base_url="https://gateway.futureagi.com/v1",
)

resp = client.chat.completions.create(
    model="deepinfra/mistralai-mistral-small-3-2-24b-instruct-2506",
    messages=[{"role": "user", "content": "Hello, Mistralai Mistral Small 3.2 24B Instruct 2506!"}],
)

print(resp.choices[0].message.content)
print(f"Tokens: {resp.usage.total_tokens}")

# Per-call gateway metadata is returned on x-agentcc-* response headers.
# When you need it programmatically, use .with_raw_response to get them:
raw = client.chat.completions.with_raw_response.create(
    model="deepinfra/mistralai-mistral-small-3-2-24b-instruct-2506",
    messages=[{"role": "user", "content": "Same call, but I want the headers."}],
)
print("Provider:", raw.headers.get("x-agentcc-provider"))
print("Latency:", raw.headers.get("x-agentcc-latency-ms"), "ms")
print("Cost:   ", raw.headers.get("x-agentcc-cost"), "USD")
print("Cache:  ", raw.headers.get("x-agentcc-cache"))
Set AGENTCC_API_KEY with a key fromapp.futureagi.com.Gateway docs ↗

Compare with similar models

Mistralai Mistral Small 3.2 24B Instruct 2506 doesn't have a public Arena ELO score yet, so we group by provider only — quality-tier comparisons need a benchmark.

FAQ

How much does Mistralai Mistral Small 3.2 24B Instruct 2506 cost?

Input is priced at $0.0750 per 1M tokens and output at $0.200 per 1M tokens (DeepInfra, last verified May 12, 2026).

What is the context window of Mistralai Mistral Small 3.2 24B Instruct 2506?

Mistralai Mistral Small 3.2 24B Instruct 2506 supports a 128,000-token context window with up to 128,000 output tokens.

Does Mistralai Mistral Small 3.2 24B Instruct 2506 support function calling?

Yes — Mistralai Mistral Small 3.2 24B Instruct 2506 supports function (tool) calling.

Is Mistralai Mistral Small 3.2 24B Instruct 2506 good for production?

Mistralai Mistral Small 3.2 24B Instruct 2506 is well-suited for agentic workflows that depend on reliable tool calls. Consider alternatives if you need high cost — input + output rates are in the top 91% of priced chat peers; consider a cheaper sibling for high-volume workloads.

How can I route to Mistralai Mistral Small 3.2 24B Instruct 2506 with fallback?

Use Agent Command Center: a single OpenAI-compatible endpoint that supports cost-optimized routing, latency-aware retries, model fallback, and shadow traffic. Configure once, swap models without app changes.

Useful links for Mistralai Mistral Small 3.2 24B Instruct 2506

Official sources, independent benchmarks, and pricing aggregators — no random search-engine guesses.