Mistral Small 3.1 24B Instruct 2503
IBM watsonx chatMistral Small 3.1 24B Instruct 2503 is an IBM watsonx chat model.It supports a 131,072-token context windowwith up to 16,384 output tokens.Input is priced at $0.106/M tokens and output at $0.318/M tokens. Capabilities include function calling. Route Mistral Small 3.1 24B Instruct 2503 via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.
Estimate Mistral Small 3.1 24B Instruct 2503 spend
Pick a workload, fine-tune the sliders, and see the monthly bill.
Estimate uses $0.1060/M input · $0.3180/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 Oct 22, 2026.
| Input | $0.106/M | |
| Output | $0.318/M | |
| Blended (3:1 in:out) | $0.159/M Cross-model comparable price |
Context & limits
- Context window
- 131,072 tokens
- Max input
- 131,072 tokens
- Max output
- 16,384 tokens
- Modalities
- text
Capabilities
- Function calling ✓ supported
- Parallel tool calls ✓ supported
- 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
Strengths & caveats — when to pick Mistral Small 3.1 24B Instruct 2503
Data-driven from Mistral Small 3.1 24B Instruct 2503's percentile within chat peers.
Where it's strong
- +parallel tool calls — only 18% of chat models on Future AGI advertise this
Watch out for
- !high cost — input + output rates are in the top 83% 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 Mistral Small 3.1 24B Instruct 2503 yet. Have one to contribute? Submit a source — citations help us prioritise.
Call Mistral Small 3.1 24B Instruct 2503 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.
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.# Mistral Small 3.1 24B Instruct 2503 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="watsonx/mistralai-mistral-small-3-1-24b-instruct-2503",
messages=[{"role": "user", "content": "Hello, Mistral Small 3.1 24B Instruct 2503!"}],
)
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="watsonx/mistralai-mistral-small-3-1-24b-instruct-2503",
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"))AGENTCC_API_KEY with a key fromapp.futureagi.com.Gateway docs ↗FAQ
How much does Mistral Small 3.1 24B Instruct 2503 cost?
Input is priced at $0.106 per 1M tokens and output at $0.318 per 1M tokens (IBM watsonx, last verified Oct 22, 2026).
What is the context window of Mistral Small 3.1 24B Instruct 2503?
Mistral Small 3.1 24B Instruct 2503 supports a 131,072-token context window with up to 16,384 output tokens.
Does Mistral Small 3.1 24B Instruct 2503 support function calling?
Yes — Mistral Small 3.1 24B Instruct 2503 supports function (tool) calling, including parallel tool calls.
Is Mistral Small 3.1 24B Instruct 2503 good for production?
Mistral Small 3.1 24B Instruct 2503 is well-suited for parallel tool calls — only 18% of chat models on Future AGI advertise this. Consider alternatives if you need high cost — input + output rates are in the top 83% of priced chat peers; consider a cheaper sibling for high-volume workloads.
How can I route to Mistral Small 3.1 24B Instruct 2503 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 Mistral Small 3.1 24B Instruct 2503
Official sources, independent benchmarks, and pricing aggregators — no random search-engine guesses.
Third-party evals — verify the marketing.
Cross-check our number against the rest of the ecosystem.