GPT-6 Luna API

Call GPT-6 Luna through Wokey and review published catalog pricing, upstream-native API forms, context, and model capabilities.

Model ID
gpt-6-luna
Vendors
openai
Supply status
Currently callable

Catalog pricing snapshot

Prices are per 1M tokens; request-time pricing controls settlement.

MeterWokeyOfficial referenceSavings
Input$0.09$0.110%
Output$0.45$0.510%
Cache read$0.009$0.0110%
Cache write$0.1125$0.12510%

Official price source: https://developers.openai.com/api/docs/models/gpt-6-luna

Measured on Wokey

Last 28 days (2026-10-13 to 2026-11-09), updated every Monday · Real requests from 200+ accounts

Time to first token, short prompts
6.5 s (Median · prompts under 8K tokens · p90 15 s)
Time to first token, agent-size prompts
9.0 s (Median · prompts of 32K–128K tokens)
Prompt cache hit rate
89% (Share of input tokens read from cache)

Most-used clients: Codex 51%, Claude Code 34%, pi 10%. Share of requests from identified clients; 51% of requests had an identified client.

How this is measured

Time to first token runs from the Wokey gateway receiving the request to the first streamed content (text, reasoning summary or refusal). It includes queueing, retries on another account and the model's thinking time, so it is longer than the upstream's own first-token latency. Only successful streamed requests count, banded by prompt size (input plus cache reads and writes).

Cache hit rate = cache-read tokens ÷ input tokens over successful requests. It depends on the client and how it is used; the cache hit-rate pages break it down by client.

Enterprise accounts and Wokey's own test accounts are excluded. A model is shown once at least 20 users used it in the last 28 days and no single user sent more than 50% of its requests, counting accounts registered through one referral chain as one user; each first-token figure needs at least 500 requests.

Cache hit rate by client

Model capabilities

Context
1,050,000 tokens
Max output
128,000 tokens
Streaming
Supported
Tools
Supported
Vision
Supported
Supported API forms
/v1/chat/completions · /v1/responses

Send a request

/v1/chat/completions

curl https://api.wokey.ai/v1/chat/completions \
  -H "Authorization: Bearer $WOKEY_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"gpt-6-luna","messages":[{"role":"user","content":"Hello"}]}'

/v1/responses

curl https://api.wokey.ai/v1/responses \
  -H "Authorization: Bearer $WOKEY_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"gpt-6-luna","input":"Hello"}'

Use GPT-6 Luna in your tools

The gateway is https://api.wokey.ai (OpenAI-compatible clients usually take https://api.wokey.ai/v1), and the model ID is gpt-6-luna. Open the guide for the client you use.

Frequently asked questions

How much does the GPT-6 Luna API cost, and how much does it save versus the official API?

Wokey lists input at $0.09 per 1M tokens and output at $0.45 per 1M tokens; the official references are $0.1 and $0.5. That is 10% lower for input and 10% lower for output. Request-time pricing controls settlement, and the vendor pricing source is linked on this page.

What is the GPT-6 Luna API model ID?

The model ID for GPT-6 Luna on Wokey is gpt-6-luna. Use it exactly as written in the request body's model field or in your client's model setting (for example ANTHROPIC_MODEL in Claude Code, or model in the Codex CLI config.toml); GET https://api.wokey.ai/v1/models also lists it.

Which API forms does GPT-6 Luna support, and how do I call it through Wokey?

GPT-6 Luna supports /v1/chat/completions (Chat Completions requests) and /v1/responses (Responses requests). Set the API base URL to https://api.wokey.ai, authenticate with a Wokey API key, and use the canonical model ID gpt-6-luna; you can start with /v1/responses. The Send a request section includes a curl example for every supported API form.

How do I use GPT-6 Luna in Codex CLI?

Codex CLI calls GPT-6 Luna over the Responses protocol. Add a model_provider to ~/.codex/config.toml with base_url = "https://api.wokey.ai", wire_api = "responses", and env_key = "OPENAI_API_KEY"; set model to gpt-6-luna and put your Wokey API key in OPENAI_API_KEY. The Codex CLI guide has the full config.

What are the context window and maximum output for GPT-6 Luna?

The catalog context window is 1,050,000 tokens and the maximum output is 128,000 tokens. These are separate limits, and a request must also satisfy the upstream constraints in effect when it is processed.

How are cache reads and cache writes priced for GPT-6 Luna?

Cache read: Wokey lists $0.009 per 1M tokens and the official reference is $0.01 per 1M tokens. Cache write: Wokey lists $0.1125 per 1M tokens and the official reference is $0.125 per 1M tokens.

How does calling GPT-6 Luna through Wokey differ from OpenRouter?

Wokey lists GPT-6 Luna at $0.09 input / $0.45 output per 1M tokens, against an official reference of $0.1 / $0.5. OpenRouter states it adds no markup on inference and charges its fee when you buy credits (5.5% by card). Wokey carries a curated set of models and has no per-provider routing parameters, so OpenRouter fits better if you need a wider catalog. The OpenRouter alternative page has the full comparison.

How do GPT-6 Luna and GPT-6 Astra differ in price and context?

In the published price comparison, GPT-6 Luna lists input/output at $0.09 / $0.45 with a 1,050,000-token context window; GPT-6 Astra lists $0.55 / $2.75 with a 1,050,000-token context window. This is a factual price-and-capacity comparison, not a quality ranking; also compare their supported API forms and capabilities.

How fast is GPT-6 Luna on Wokey?

Measured on real Wokey gateway requests from 2026-10-13 to 2026-11-09: the median time to first token was 6.5 s for prompts under 8K tokens (p90 15 s) and 9.0 s for 32K–128K-token agent prompts. 89% of input tokens were read from the prompt cache. The most-used clients were Codex, Claude Code and pi. Times run from the gateway receiving the request and include queueing, retries and thinking.

Related models and documentation

Get API key · gpt-6-luna