Mistral Medium 2505

Mistral AI chat

Mistral Medium 2505 is a Mistral AI chat model.It supports a 131,072-token context windowwith up to 8,191 output tokens.Input is priced at $0.400/M tokens and output at $2.00/M tokens. Capabilities include function calling. Route Mistral Medium 2505 via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.

Pricing source: litellm Last verified: Aug 6, 2026 View source ↗
Cost calculator

Estimate Mistral Medium 2505 spend

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

~3K in / ~400 out · 5K req/day
3,000
0131,072
400
08,191
5,000
01,000,000
Per request
$0.002000
in $0.001200 · out $0.000800
Per day
$10.00
5,000 requests
Per month
$304
152,188 requests

Estimate uses $0.4000/M input · $2.00/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 Aug 6, 2026.

Input $0.400/M
Output $2.00/M
Blended (3:1 in:out) $0.800/M
Cross-model comparable price

Context & limits

Context window
131,072 tokens
Max input
131,072 tokens
Max output
8,191 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 ✓ supported
  • Prompt caching — not advertised
  • Reasoning — not advertised

Strengths & caveats — when to pick Mistral Medium 2505

Data-driven from Mistral Medium 2505's percentile within chat peers.

Where it's strong

  • +agentic workflows that depend on reliable tool calls

Watch out for

  • No major caveats flagged from public spec.

Benchmark scores

Reported public benchmark numbers. Each row links to the source. Faded bar shows 6-peer average for context.

Chatbot Arena ELOgeneral· overall↓1% vs peers
Captured Aug 8, 2026
Try it

Call Mistral Medium 2505 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.
# Mistral Medium 2505 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="mistral/mistral-medium-2505",
    messages=[{"role": "user", "content": "Hello, Mistral Medium 2505!"}],
)

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="mistral/mistral-medium-2505",
    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 ↗
Advanced: fallback + cache config (YAML)
strategy: cost-optimized
targets:
  - model: mistral-medium-2505
    provider: mistral
    weight: 80
fallbacks:
  - model: claude-opus-4-1
    provider: azure-ai-foundry
  - model: claude-opus-4-1-20250805
    provider: anthropic
guardrails: [pii, prompt-injection, secrets]
cache: { exact: true, semantic: true }

Same model on other providers

mistral-medium-2505 is also available via 1 other route. Pricing, regions, and capabilities can differ — compare before routing production traffic.

ProviderInput / 1MOutput / 1MVerified
Azure AI Foundry$0.400/M$2.00/MAug 6, 2026

Compare with similar models

Grouped by Chatbot Arena tier (Mistral Medium 2505 sits at 1387 ELO).

FAQ

How much does Mistral Medium 2505 cost?

Input is priced at $0.400 per 1M tokens and output at $2.00 per 1M tokens (Mistral AI, last verified Aug 6, 2026).

What is the context window of Mistral Medium 2505?

Mistral Medium 2505 supports a 131,072-token context window with up to 8,191 output tokens.

Does Mistral Medium 2505 support function calling?

Yes — Mistral Medium 2505 supports function (tool) calling.

Is Mistral Medium 2505 good for production?

Mistral Medium 2505 is well-suited for agentic workflows that depend on reliable tool calls.

How can I route to Mistral Medium 2505 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 Medium 2505

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