GPT-6 Luna

Azure AI Foundry chat

GPT-6 Luna is an Azure AI Foundry chat model.It supports a 922,000-token context windowwith up to 128,000 output tokens.Input is priced at $0.1000/M tokens and output at $0.500/M tokens. Capabilities include function calling, vision, reasoning, prompt caching. Route GPT-6 Luna 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 GPT-6 Luna spend

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

~3K in / ~400 out · 5K req/day
3,000
0922,000
400
0128,000
5,000
01,000,000
cached @ $0.0100/M
Per request
$0.000500
in $0.000300 · out $0.000200
Per day
$2.50
5,000 requests
Per month
$76.09
152,188 requests

Estimate uses $0.1000/M input · $0.5000/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 $0.1000/M
Output $0.500/M
Blended (3:1 in:out) $0.200/M
Cross-model comparable price
Cached input $0.0100/M

Context & limits

Context window
922,000 tokens
Max input
922,000 tokens
Max output
128,000 tokens
Modalities
vision, pdf, text

Capabilities

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

Strengths & caveats — when to pick GPT-6 Luna

Data-driven from GPT-6 Luna's percentile within chat peers.

Where it's strong

  • +parallel tool calls — only 18% of chat models on Future AGI advertise this
  • +PDF input — only 18% of chat models on Future AGI advertise this

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 GPT-6 Luna 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.
# GPT-6 Luna 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/gpt-6-luna",
    messages=[{"role": "user", "content": "Hello, GPT-6 Luna!"}],
)

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/gpt-6-luna",
    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: gpt-6-luna
    provider: azure-ai-foundry
    weight: 80
fallbacks:
  - model: claude-opus-4-6
    provider: anthropic
  - model: claude-opus-4-6-20260205
    provider: anthropic
guardrails: [pii, prompt-injection, secrets]
cache: { exact: true, semantic: true }

Same model on other providers

gpt-6-luna is also available via 3 other routes. Pricing, regions, and capabilities can differ — compare before routing production traffic.

ProviderInput / 1MOutput / 1MVerified
OpenAI$0.1000/M$0.500/MOct 22, 2026
Azure OpenAI$0.1000/M$0.500/MOct 22, 2026
GitHub Copilot$0.1000/M$0.500/MOct 22, 2026

Explore similar models

Grouped by Chatbot Arena tier (GPT-6 Luna sits at 1441 ELO). Side-by-side links appear where a comparison page exists; otherwise, open the model profile.

FAQ

How much does GPT-6 Luna cost?

Input is priced at $0.1000 per 1M tokens and output at $0.500 per 1M tokens (Azure AI Foundry, last verified Oct 22, 2026).

What is the context window of GPT-6 Luna?

GPT-6 Luna supports a 922,000-token context window with up to 128,000 output tokens.

Does GPT-6 Luna support function calling?

Yes — GPT-6 Luna supports function (tool) calling, including parallel tool calls.

Is GPT-6 Luna good for production?

GPT-6 Luna is well-suited for parallel tool calls — only 18% of chat models on Future AGI advertise this and PDF input — only 18% of chat models on Future AGI advertise this.

How can I route to GPT-6 Luna 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 GPT-6 Luna

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