GPT-6 Luna API: Endpoint & 25% Discount
Up to 25%GPT-6 Luna is a multimodal chat model that accepts text and images and replies in text.
What is GPT-6 Luna?
GPT-6 Luna is a multimodal chat model that accepts text and images and replies in text, built by OpenAI and served on LLM API under the id gpt-6-luna. It is reachable over the same OpenAI-compatible endpoint as every other model in the catalogue, so switching to it is a one-line change. The prices, context window and provider routing below come straight from the live catalogue and are refreshed every week.
Can you self-host GPT-6 Luna?
No. We found no public weights for GPT-6 Luna on Hugging Face, so there is nothing to download, quantize or serve on your own GPUs: no VRAM budget, no vLLM or GGUF build to plan for. The only way to run it is over an API.
Skip the deploy — use LLM.API as your endpoint
No GPUs, no quantization trade-offs. Same model, OpenAI-compatible, up to 25% below list price.
Providers
LLM.API routes GPT-6 Luna to the providers below, with discounted effective rates versus list price.
List price by provider
InputOutputProvider list prices; the LLM.API discount applies on top.
| Provider | Pricing | Context | Capabilities |
|---|---|---|---|
| in $0.1; out $0.5 per 1M tokens | 1.1M tokens | vision, tools, streaming, reasoning, web search, JSON, structured | |
| in $0.1; out $0.5 per 1M tokens | 1.1M tokens | vision, tools, streaming, reasoning, web search, JSON, structured | |
| in $0.1; out $0.5 per 1M tokens | 1.1M tokens | vision, tools, streaming, reasoning, web search, JSON, structured |
Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 11 Oct 2026.
Try this model
Test GPT-6 Luna right here — free to start.
Suggestions for your first prompt
Code snippet
Call GPT-6 Luna through the OpenAI-compatible API — POST /v1/chat/completions.
Switching from OpenAI? Change 2 lines.
- base_url="https://api.openai.com/v1"
- api_key="YOUR_OPENAI_KEY"
+ base_url="https://api.llmapi.ai/v1"
+ api_key="YOUR_LLMAPI_KEY"Everything else stays the same — SDK, message format, tools, streaming. Set model to gpt-6-luna and the call runs unchanged.
curl https://api.llmapi.ai/v1/chat/completions \
-H "Authorization: Bearer $LLMAPI_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "gpt-6-luna",
"messages": [
{"role": "user", "content": "Give me three crisp launch checklist items."}
],
"reasoning_effort": "none"
}'from openai import OpenAI
client = OpenAI(
api_key="YOUR_LLMAPI_KEY",
base_url="https://api.llmapi.ai/v1",
)
resp = client.chat.completions.create(
model="gpt-6-luna",
messages=[
{"role": "system", "content": "You are a precise product assistant."},
{"role": "user", "content": "Give me three crisp launch checklist items."},
],
reasoning_effort="none",
)
print(resp.choices[0].message.content)import OpenAI from "openai";
const client = new OpenAI({
apiKey: process.env.LLMAPI_KEY,
baseURL: "https://api.llmapi.ai/v1",
});
const resp = await client.chat.completions.create({
model: "gpt-6-luna",
messages: [
{ role: "user", content: "Give me three crisp launch checklist items." },
],
});
console.log(resp.choices[0].message.content);stream = client.chat.completions.create(
model="gpt-6-luna",
messages=[{"role": "user", "content": "Write a haiku about latency."}],
stream=True,
)
for chunk in stream:
delta = chunk.choices[0].delta.content
if delta:
print(delta, end="", flush=True){
"model": "gpt-6-luna",
"messages": [
{"role": "system", "content": "You are a precise product assistant."},
{"role": "user", "content": "Give me three crisp launch checklist items."}
],
"reasoning_effort": "none"
}ENDPOINTS
Supported endpoints & features
Base URL https://api.llmapi.ai/v1 · model ID gpt-6-luna · OpenAI-compatible.
POST /v1/chat/completionsChat Completions — supported
| Feature | Status | How to set it |
|---|---|---|
| Function calling (tools) | Supported | tools, tool_choice |
| Parallel tool calls | Not supported | parallel_tool_calls |
| JSON mode | Supported | response_format: {"type": "json_object"} |
| Structured outputs | Supported | response_format: {"type": "json_schema", …} |
| Streaming | Supported | stream: true |
| Vision (image input) | Supported | image_url content parts |
| Web search | Supported | web_search |
| Reasoning effort | none · low · medium · high · xhigh | reasoning_effort: "none" |
ERRORS
Errors & fallback routing
| Code | What it means | What to do |
|---|---|---|
401 | Missing or invalid API key. | Send Authorization: Bearer YOUR_LLMAPI_KEY and check the key is active. |
429 | Rate limited, or the upstream provider is throttling the request. | Back off and retry with jitter; the gateway also retries the request on another provider where one is available. |
5xx | Upstream provider error or timeout. | Retry; fallback routing sends the retry to the next healthy provider for this model. |
When a provider fails or throttles, the request is routed to the next provider serving this model — the ones listed in the providers table above.
Why run GPT-6 Luna on LLM.API?
Unified AI Routing
Reach GPT-6 Luna and sibling models through one OpenAI-compatible endpoint.
Cost Control
Production: Compare provider price points and keep spend visible as you scale GPT-6 Luna.
Reliability Layer
Retry and route across configured providers when a single upstream blips.
Observability
Trace prompts, tokens, and errors for GPT-6 Luna alongside the rest of your stack.
Drop-in SDKs
Keep using familiar OpenAI client patterns with base URL https://api.llmapi.ai/v1.
Model Breadth
Swap GPT-6 Luna for chat, media, or embedding alternatives without rewriting auth.
When to Use — When NOT to Use
Use it if...
- You want GPT-6 Luna over the same OpenAI-compatible chat completions endpoint as the rest of the catalogue
- You want one API key and one bill instead of a separate account per model vendor
- You want provider routing and failover handled for you rather than wired by hand
- Your prompts mix text and images in a single request (GPT-6 Luna)
Avoid if...
- You must run everything on your own hardware with no cloud inference at all
- You need the weights themselves for fine-tuning — this model is served, not published
- You need a contractual latency or throughput guarantee before benchmarking it yourself
- Your task is embeddings, OCR or media generation rather than chat completions
COMMUNITY
What developers say about GPT-6 Luna
Summarised from public developer discussions and write-ups. Opinions are the sources’, not LLM.API’s.
- Developers on Reddit and Hacker News praise the model's significant price reduction, noting that at $0.10 per million input tokens, it offers exceptional value for high-volume tasks and worker subagent pipelines.
- Early users report mixed coding performance, highlighting concise, direct answers and fast speeds, but also noting serious issues with instruction drift, unauthorized subagent launches, and accidental code deletions.
- Technical reviews indicate that while Luna is highly cost-effective, it fails to match the quality of GPT-6 Astra, showing lower benchmark scores and a higher frequency of factual errors.
SOURCES
COMPARE
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