Model detail
GPT-5.6 Luna
GPT-5.6 Luna is a fast, cost-efficient model in OpenAI's GPT-5.6 series. It is suited for high-volume, latency-sensitive tasks such as chat, classification, and lightweight agentic workflows, providing capable reasoning for its price tier.
Model specs
- Context length
- 1.05M
- Max output
- 128K
- I/O modalities
- Text / Image
- Released
- 2026-07
02
API endpoints
One MixRoute gateway, OpenAI-compatible
-
OpenAI-compatible
/v1/chat/completionsPOST
03
Tiered pricing
Unit: /1M Tokens
| Tier | Input /1M Tokens | Output /1M Tokens | Cache read /1M Tokens | Cache write /1M Tokens |
|---|---|---|---|---|
| standard Length ≤ 272K | $0.2000 | $1.2000 | $0.0200 | $0.2500 |
| long_context Length > 272K | $0.4000 | $1.8000 | $0.0400 | $0.5000 |
05
Selection summary
Quickly judge whether it fits your workload
What is this model good for?
High-volume text tasks with tools and structured output
Use GPT-5.6 Luna for high-volume chat, classification, and lightweight agent workflows that need tool calling or structured output. Start with a representative traffic slice to confirm quality and latency for your workload.
model: "gpt-5.6-luna".
What should you check before using it?
Keep requests below the 272K pricing boundary where practical
Inputs above 272K move to the higher tier: input rises from $0.20 to $0.40 and output from $1.20 to $1.80 per 1M tokens. Measure request lengths and split work where practical before production.
Why use it through MixRoute?
Keep your OpenAI-style integration and change the route
MixRoute exposes Luna through the OpenAI-compatible chat completions endpoint, so existing OpenAI-style clients can usually keep their request format. Change the base URL and model ID, then validate tools and streaming.
model: "gpt-5.6-luna".
06
Rate limits & capacity
Baseline quotas; the Console is authoritative
Requests per minute (RPM)
5,000 / min
Per-API-key request-rate ceiling for this model; exceeding it returns 429
Tokens per minute (TPM)
4M tokens / min
Combined input + output tokens; varies by plan tier and can be raised on request
Automatic failover on 429
On provider rate limiting, traffic automatically shifts to another available channel; no client-side retry logic needed
07
Capabilities & compatibility
Unconfirmed capabilities stay "Unknown," no guesses
Tool calling
Supports function and tool calls
tools definition; the response returns tool_calls to execute and feed results back
Structured output
Can require JSON-formatted output
response_format: { type: "json_schema" } forces valid JSON output
Streaming
Supports streamed responses
stream: true streams tokens incrementally, so chat UIs render as they arrive
Prompt caching
Depends on the provider and routing conditions
Vision input
Official capability data is unconfirmed; listed as unknown for now
OpenAI SDK
Call it through MixRoute's compatible endpoint
base_url to https://api.mixroute.ai/v1; everything else follows the OpenAI style
08
Token cost estimator
Live estimate from this page's pricing, not an actual bill
Share of the same prefix read repeatedly, up to 100%
09
FAQ
Cache read is $0.0200 / 1M tokens, only one tenth of the $0.2000 input price, but cache write costs $0.2500, 25% more than input itself. That means a prompt written once and then discarded actually costs more. The test is how many times the same prefix gets read again — content sent on every request, like system prompts, tool definitions, and fixed knowledge, is worth caching; one-off long documents are not. Before rolling it out, measure what share of your requests are repeated prefixes, then decide whether to enable it.
One endpoint, a testable decision
Test this model and alternate routes with the same request format
Start from a real workload, then let quality, total cost, and failure conditions decide whether to send production traffic.