Mistral Large 4

Mistral AI chat

Mistral Large 4 is a Mistral AI chat model.It supports a 1,048,576-token context window.Input is priced at $0.680/M tokens and output at $2.09/M tokens. Capabilities include function calling, vision, reasoning, prompt caching. Route Mistral Large 4 via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.

Pricing source: litellm Last verified: Oct 22, 2026 View source ↗
Cost calculator

Estimate Mistral Large 4 spend

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

~3K in / ~400 out · 5K req/day
3,000
01,048,576
400
016,000
5,000
01,000,000
cached @ $0.0680/M
Per request
$0.002876
in $0.002040 · out $0.000836
Per day
$14.38
5,000 requests
Per month
$438
152,188 requests

Estimate uses $0.6800/M input · $2.09/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 $0.680/M
Output $2.09/M
Blended (3:1 in:out) $1.03/M
Cross-model comparable price
Cached input $0.0680/M

Context & limits

Context window
1,048,576 tokens
Max input
1,048,576 tokens
Max output
—
Modalities
vision, text

Capabilities

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

Strengths & caveats — when to pick Mistral Large 4

Data-driven from Mistral Large 4's percentile within chat peers.

Where it's strong

  • +long-context tasks — context window in the top 2% of peers

Watch out for

  • No major caveats flagged from public spec.

Benchmark scores

Reported public benchmark numbers. Each row links to the source.

Captured Oct 20, 2026
Captured Oct 20, 2026
Captured Oct 19, 2026
Try it

Call Mistral Large 4 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 Large 4 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-large-4",
    messages=[{"role": "user", "content": "Hello, Mistral Large 4!"}],
)

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-large-4",
    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-large-4
    provider: mistral
    weight: 80
fallbacks:
  - model: claude-opus-5
    provider: azure-ai-foundry
  - model: gemini-3-7-flash
    provider: vertex-ai
guardrails: [pii, prompt-injection, secrets]
cache: { exact: true, semantic: true }

Explore similar models

Grouped by Chatbot Arena tier (Mistral Large 4 sits at 1433 ELO). Side-by-side links appear where a comparison page exists; otherwise, open the model profile.

FAQ

How much does Mistral Large 4 cost?

Input is priced at $0.680 per 1M tokens and output at $2.09 per 1M tokens (Mistral AI, last verified Oct 22, 2026).

What is the context window of Mistral Large 4?

Mistral Large 4 supports a 1,048,576-token context window.

Does Mistral Large 4 support function calling?

Yes — Mistral Large 4 supports function (tool) calling.

Is Mistral Large 4 good for production?

Mistral Large 4 is well-suited for long-context tasks — context window in the top 2% of peers.

How can I route to Mistral Large 4 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 Large 4

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