OpenAI: GPT-5.6 Luna

openai/gpt-5.6-luna

Modalities: text+image+file->text
In / out price: $0.2 / $1.2
Context: 1.1M
Released: Jul 9, 2026
Knowledge cutoff: 2026-02-16

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...

VisionToolsReasoningStructured output1M context

Supported parameters

include_reasoning
Include reasoning
Include a reasoning summary in the response.
max_completion_tokens
Max completion tokens
Same idea as max_tokens. Some newer models use this field instead.
max_tokens
Max tokens
Upper bound on generated tokens. Output stops at the limit.
reasoning
Reasoning
Enable or configure reasoning. The fields differ by model.
reasoning_effort
Reasoning effort
How much compute to spend reasoning. Often low, medium, or high.
response_format
Response format
For example json_object or json_schema, to constrain the shape.
seed
Seed
A fixed seed helps reproduce sampling. It does not guarantee byte-identical output.
structured_outputs
Structured outputs
Generate a parseable structure that follows a JSON Schema.
tool_choice
Tool choice
Whether to call a tool: auto, none, or a named function.
tools
Tools
Function definitions the model may call via tool_calls.
verbosity
Verbosity
Ask the model for a shorter or longer answer.

Models OpenAI

OpenAI: GPT-5.6 Luna

openai/gpt-5.6-luna

Try this model

Modalities: text+image+file->text
In / out price: $0.2 / $1.2
Context: 1.1M
Released: Jul 9, 2026
Knowledge cutoff: 2026-02-16

VisionToolsReasoningStructured output1M context

Supported parameters

include_reasoning
Include reasoning
Include a reasoning summary in the response.
max_completion_tokens
Max completion tokens
Same idea as max_tokens. Some newer models use this field instead.
max_tokens
Max tokens
Upper bound on generated tokens. Output stops at the limit.
reasoning
Reasoning
Enable or configure reasoning. The fields differ by model.
reasoning_effort
Reasoning effort
How much compute to spend reasoning. Often low, medium, or high.
response_format
Response format
For example json_object or json_schema, to constrain the shape.
seed
Seed
A fixed seed helps reproduce sampling. It does not guarantee byte-identical output.
structured_outputs
Structured outputs
Generate a parseable structure that follows a JSON Schema.
tool_choice
Tool choice
Whether to call a tool: auto, none, or a named function.
tools
Tools
Function definitions the model may call via tool_calls.
verbosity
Verbosity
Ask the model for a shorter or longer answer.