Mistral Medium 2505
Azure AI Foundry chat Deprecates in 35d (Dec 9, 2026)model fallback routing to swap models without code changes.
Mistral Medium 2505 is an Azure AI Foundry 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.
Estimate Mistral Medium 2505 spend
Pick a workload, fine-tune the sliders, and see the monthly bill.
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 Oct 22, 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 — not advertised
- 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
- !strict structured output — no JSON-schema enforcement, expect retry loops
- !scheduled deprecation on Dec 9, 2026 (35 days)
Benchmark scores
Reported public benchmark numbers. Each row links to the source. Faded bar shows 6-peer average for context.
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.
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="azure-ai-foundry/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="azure-ai-foundry/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"))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: azure-ai-foundry
weight: 80
fallbacks:
- model: gpt-5-4-mini-2026-03-17
provider: openai
- model: gpt-5-4-mini
provider: openai
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.
| Provider | Input / 1M | Output / 1M | Verified |
|---|---|---|---|
| Mistral AI | $0.400/M | $2.00/M | Aug 6, 2026 |
Explore similar models
Grouped by Chatbot Arena tier (Mistral Medium 2505 sits at 1387 ELO). Side-by-side links appear where a comparison page exists; otherwise, open the model profile.
- GPT-5.4Azure AI Foundry · $2.50/M in · $15.00/M out · 922,000 ctxCompare side-by-side →
- DeepSeek v3.2Azure AI Foundry · $0.580/M in · $1.68/M out · 128,000 ctxCompare side-by-side →
- DeepSeek R1Azure AI Foundry · $1.35/M in · $5.40/M outCompare side-by-side →
- GPT-5.5 (2026-04-23)Azure AI Foundry · $5.00/M in · $30.00/M out · 922,000 ctxCompare side-by-side →
- Grok 4.1 FastxAI · $0.200/M in · $0.500/M out · 2,000,000 ctxCompare side-by-side →
- DeepSeek v3.2DeepSeek · $0.280/M in · $0.400/M out · 163,840 ctxCompare side-by-side →
- DeepSeek R1Snowflake Cortex · $1.35/M in · $5.40/M out · 128,000 ctxCompare side-by-side →
- GPT-5 miniAzure OpenAI · $0.250/M in · $2.00/M out · 272,000 ctxCompare side-by-side →
≥30 ELO higher
- Gemini 4 ArgonGoogle AI · $2.00/M in · $10.00/M out · 1,048,576 ctxCompare side-by-side →
- GPT-5.5 (2026-04-23)OpenAI · $5.00/M in · $30.00/M out · 1,050,000 ctxCompare side-by-side →
- GPT-5.5OpenAI · $5.00/M in · $30.00/M out · 1,050,000 ctxCompare side-by-side →
- GPT-5.4Azure AI Foundry · $2.50/M in · $15.00/M out · 922,000 ctxCompare side-by-side →
25–100 ELO lower, ≤50% of price
- Gemini 1.5 FlashGoogle Vertex AI · $0.0750/M in · $0.300/M out · 1,000,000 ctxView model →
- GPT-4.1 nano (2025-04-14)Azure OpenAI · $0.1000/M in · $0.400/M out · 1,047,576 ctxView model →
- GPT-4o miniAzure OpenAI · $0.150/M in · $0.600/M out · 128,000 ctxView model →
- GPT-4o mini (2024-07-18)Azure OpenAI · $0.150/M in · $0.600/M out · 128,000 ctxView model →
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 (Azure AI Foundry, last verified Oct 22, 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. Consider alternatives if you need strict structured output — no JSON-schema enforcement, expect retry loops.
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.
Third-party evals — verify the marketing.
Cross-check our number against the rest of the ecosystem.