- 1M context
- Multimodal
- Reasoning
- Responses API
GPT-6 Luna on QuickSilver Pro
GPT-6 Luna through one OpenAI-compatible API. Supported features: text input, text output, image input, streaming, reasoning, tool calling and JSON Schema.
- 1M context
- 128K max output
- Vision
- Tools
- Reasoning · on by default
- Streaming
- JSON Schema output
You pay · input / output per 1M
$0.10 / $0.50
Prompts over 200K tokens: $0.20 / $0.75
OpenAI list source: openai.com/api/pricing
At a glance
- Context
- 1M tokens
- Input / 1M
- $0.10
- Output / 1M
- $0.50
- Thinks by default
- Yes
- Max output
- 128K
- Latency · live
- —
High-volume chat and lightweight agent workflows.
When is GPT-6 Luna the right call?
Curated from catalog capabilities and published prices, not a benchmark.
When to use
Use GPT-6 Luna when your application needs these capabilities: text input, text output, image input, streaming, reasoning, tool calling and JSON Schema.
- Long documents and codebases, up to 1M tokens of context
- Agents that call tools, with strict JSON Schema output
- Prompts that include images
- Multi-step reasoning, on by default
When to use something else
Choose a different model if your workflow requires capabilities beyond those listed here. Test your workload before committing to a model.
Quickstart (curl)
Get an API key →curl https://api.quicksilverpro.io/v1/chat/completions \
-H "Authorization: Bearer $QSP_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "gpt-6-luna",
"messages": [{"role": "user", "content": "Hello!"}]
}'import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.quicksilverpro.io/v1",
api_key=os.environ["QSP_KEY"],
)
response = client.chat.completions.create(
model="gpt-6-luna",
messages=[{"role": "user", "content": "Hello!"}],
)
print(response.choices[0].message.content)import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://api.quicksilverpro.io/v1",
apiKey: process.env.QSP_KEY,
});
const response = await client.chat.completions.create({
model: "gpt-6-luna",
messages: [{ role: "user", content: "Hello!" }],
});
console.log(response.choices[0].message.content);curl https://api.quicksilverpro.io/v1/responses \
-H "Authorization: Bearer $QSP_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "gpt-6-luna",
"input": "Hello!"
}'import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.quicksilverpro.io/v1",
api_key=os.environ["QSP_KEY"],
)
response = client.chat.completions.create(
model="gpt-6-luna",
messages=[{"role": "user", "content": "Hello!"}],
)
print(response.choices[0].message.content)OpenAI-compatible. One-line migration via base_url.
Gateway behavior
Compatibility notes for this model.
prompt- Prompts up to 1M tokens. Above 200K prompt tokens the request bills at $0.20 input / $0.75 output per 1M.
max_tokens- Up to 128K output tokens.
- /v1/chat/completions: Supported
- /v1/responses: Supported
- streaming: Supported
- tools: Supported
- image input: Supported
- JSON Schema: Supported
- reasoning: Supported
Where the price comes from
- Provider reference
- No provider list price is compared for this model.openai.com/api/pricing
- Catalog verified
- Prompt cache · not compared
- $0.01 read · $0.125 5m write
- Long-context tier
- $0.20 / $0.75 over 200K
usage.cost = prompt_tokens × $0.10/1M + completion_tokens × $0.50/1M
FAQ
Common questions
$0.10 input / $0.50 output per million tokens.
1M context tokens and up to 128K output tokens. Input and output must fit within the context window.
APIs: Chat Completions and Responses. Capabilities: text input, text output, image input, streaming, reasoning, tool calling and JSON Schema. Use model="gpt-6-luna" with base_url=https://api.quicksilverpro.io/v1.
Above 200K prompt tokens, input costs $0.20 and output costs $0.75 per million tokens.