GPT 5.6 Luna
gpt-5.6-lunaCompare gpt-5.6-luna API pricing, supported endpoints, capabilities and access options on Modelsell.
- Starting price
- View full pricing
- Context
- 1.1M
- Maximum input window
- Max output
- 128K
- Maximum tokens per response
- Modalities
- →
- Knowledge cutoff
- Feb 2026
- Released
- Jul 2026
Pricing by Supplier
| Tier | Input/ 1M tokens | Output/ 1M tokens | Cache Read/ 1M tokens | Cache Write (5m)/ 1M tokens |
|---|---|---|---|---|
standardInput ≤ 272K tokens | $0.12 | $0.72 | $0.012 | $0.15 |
long_ | $0.24 | $1.08 | $0.024 | $0.3 |
- Input/ 1M tokens
- $0.12
$0.2 - Output/ 1M tokens
- $0.72
$1.2 - Cache Read/ 1M tokens
- $0.012
$0.02 - Cache Write (5m)/ 1M tokens
- $0.15
$0.25
- Input/ 1M tokens
- $0.24
$0.4 - Output/ 1M tokens
- $1.08
$1.8 - Cache Read/ 1M tokens
- $0.024
$0.04 - Cache Write (5m)/ 1M tokens
- $0.3
$0.5
| Tier | Input/ 1M tokens | Output/ 1M tokens | Cache Read/ 1M tokens | Cache Write (5m)/ 1M tokens |
|---|---|---|---|---|
standardInput ≤ 272K tokens | $0.12 | $0.72 | $0.012 | $0.15 |
long_ | $0.24 | $1.08 | $0.024 | $0.3 |
- Input/ 1M tokens
- $0.12
$0.2 - Output/ 1M tokens
- $0.72
$1.2 - Cache Read/ 1M tokens
- $0.012
$0.02 - Cache Write (5m)/ 1M tokens
- $0.15
$0.25
- Input/ 1M tokens
- $0.24
$0.4 - Output/ 1M tokens
- $1.08
$1.8 - Cache Read/ 1M tokens
- $0.024
$0.04 - Cache Write (5m)/ 1M tokens
- $0.3
$0.5
| Tier | Input/ 1M tokens | Output/ 1M tokens | Cache Read/ 1M tokens | Cache Write (5m)/ 1M tokens |
|---|---|---|---|---|
standardInput ≤ 272K tokens | $0.12 | $0.72 | $0.012 | $0.15 |
long_ | $0.24 | $1.08 | $0.024 | $0.3 |
- Input/ 1M tokens
- $0.12
$0.2 - Output/ 1M tokens
- $0.72
$1.2 - Cache Read/ 1M tokens
- $0.012
$0.02 - Cache Write (5m)/ 1M tokens
- $0.15
$0.25
- Input/ 1M tokens
- $0.24
$0.4 - Output/ 1M tokens
- $1.08
$1.8 - Cache Read/ 1M tokens
- $0.024
$0.04 - Cache Write (5m)/ 1M tokens
- $0.3
$0.5
| Tier | Input/ 1M tokens | Output/ 1M tokens | Cache Read/ 1M tokens | Cache Write (5m)/ 1M tokens |
|---|---|---|---|---|
standardInput ≤ 272K tokens | $0.1 | $0.6 | $0.01 | $0.125 |
long_ | $0.2 | $0.9 | $0.02 | $0.25 |
- Input/ 1M tokens
- $0.1
$0.2 - Output/ 1M tokens
- $0.6
$1.2 - Cache Read/ 1M tokens
- $0.01
$0.02 - Cache Write (5m)/ 1M tokens
- $0.125
$0.25
- Input/ 1M tokens
- $0.2
$0.4 - Output/ 1M tokens
- $0.9
$1.8 - Cache Read/ 1M tokens
- $0.02
$0.04 - Cache Write (5m)/ 1M tokens
- $0.25
$0.5
Capabilities / Supported modalities
Provider & data privacy
o200k_basePerformance
Benchmarks
Scores on standardized evaluations. Higher percentages are better — and rank percentile shows
Metrics sourced fromArtificial Analysis 2026-09-30·GPT 5.6 Luna
This model is not included in the current benchmark snapshot.
Missing models or measurements are not zero scores.
Benchmark charts preserve the source model selection and reasoning settings. Missing models or measurements are not zero scores, and benchmark cost or speed is not this site’s service commitment.
About GPT 5.6 Luna
把重复的信息处理交给模型
GPT-5.6 Luna 适合数量较多、目标清楚的日常任务，例如商品信息整理、评论归类、工单摘要、表单字段提取和简短回复生成。它能理解文字与图片，并输出文本或符合约定结构的结果，方便加入已有业务流程。
处理重复任务时，应把判断规则写得具体。评论分类需要定义各个类别，允许无法判断的情况；商品字段提取需要说明名称、规格和缺失值的处理方式。提供少量容易混淆的示例，可以帮助模型理解边界，比堆叠抽象要求更有效。
图片输入适合补充页面截图、商品图或含文字的表单。对于重要字段，最好让结果同时保留输入编号与依据，方便程序校验和人工抽查。结构化输出可以固定字段类型和可选值，减少后续解析负担。模型也支持函数调用，应用可以据此查询外部信息或衔接下一步处理。
推理投入可按任务选择，固定提取和直接改写可以先尝试 none 或较低投入，再用真实样例检查效果。需要搜索、计算或文件检索时，可在 Responses API 中配置相应工具。将复杂争议与日常标准任务区分开处理，有助于让高频流程保持简单，也方便把需要深入判断的情况交给后续环节。
Use cases and prompting
评论分类提示词
“请为每条商品评论提取主题与处理建议。主题只能选质量、物流、服务、使用方法或其他；允许多选。输出评论编号、主题、简短摘要、是否需要人工跟进和原文依据。不要把建议当作用户已经提出的要求，无法判断的字段填 null。”
常见问题
- 分类结果不一致怎么办？补充类别定义和边界样例，尤其说明多主题与含糊评论如何处理。
- 如何让结果便于程序接收？定义固定字段、枚举和缺失值规则，使用结构化输出。
- 可以只靠图片提取精确信息吗？提供清晰图片，对关键数值保留复查环节，能附原始文字时一起提供。
API access
API documentation
Code samples
/v1/chat/completions| Parameter | Type | Default / range | Description |
|---|---|---|---|
temperature | number | = 10 ~ 2 | Sampling temperature; lower is more deterministic |
top_p | number | = 10 ~ 1 | Nucleus sampling probability mass |
max_tokens | integer | >= 1 | Maximum number of tokens in the response |
frequency_penalty | number | = 0-2 ~ 2 | Penalises repetition of frequent tokens |
presence_penalty | number | = 0-2 ~ 2 | Encourages introducing new topics |
stop | array | — | Up to 4 strings that stop generation |
seed | integer | — | Deterministic sampling seed (best-effort) |
n | integer | = 1>= 1 | Number of completions to generate |
stream | boolean | = false | Stream tokens via Server-Sent Events |
response_format | object | — | Force JSON object or schema-conforming output |
tools | array | — | Tool / function declarations the model may call |
tool_choice | string | autononerequired | Tool-choice policy or specific tool name |
logprobs | boolean | = false | Return per-token log probabilities |
top_logprobs | integer | 0 ~ 20 | Number of top log probabilities returned per token |
logit_bias | object | — | Per-token logit bias map |
user | string | — | End-user identifier for abuse monitoring |
Replace <YOUR_API_KEY> with the API key from your token settings.
Authentication
All requests must include Authorization: Bearer <TOKEN> header. Anthropic-formatted endpoints accept the x-api-key header instead.
Generate tokens from the Tokens page; you can scope them to specific models, groups, IPs, and rate-limits.
Supported parameters
| Parameter | Type | Default / range | Description |
|---|---|---|---|
temperature | number | = 10 ~ 2 | Sampling temperature; lower is more deterministic |
top_p | number | = 10 ~ 1 | Nucleus sampling probability mass |
max_tokens | integer | >= 1 | Maximum number of tokens in the response |
frequency_penalty | number | = 0-2 ~ 2 | Penalises repetition of frequent tokens |
presence_penalty | number | = 0-2 ~ 2 | Encourages introducing new topics |
stop | array | — | Up to 4 strings that stop generation |
seed | integer | — | Deterministic sampling seed (best-effort) |
n | integer | = 1>= 1 | Number of completions to generate |
stream | boolean | = false | Stream tokens via Server-Sent Events |
response_format | object | — | Force JSON object or schema-conforming output |
tools | array | — | Tool / function declarations the model may call |
tool_choice | string | autononerequired | Tool-choice policy or specific tool name |
logprobs | boolean | = false | Return per-token log probabilities |
top_logprobs | integer | 0 ~ 20 | Number of top log probabilities returned per token |
logit_bias | object | — | Per-token logit bias map |
user | string | — | End-user identifier for abuse monitoring |
Rate limits
| Supplier | RPM | TPM | RPD |
|---|---|---|---|
| Azure | Unlimited | Unlimited | Unlimited |
| GPT官+AZ 混合 | Unlimited | Unlimited | Unlimited |
| GPT特价 | Unlimited | Unlimited | Unlimited |
| OpenAI | Unlimited | Unlimited | Unlimited |
No restriction
Frequently asked questions about gpt-5.6-luna
What is gpt-5.6-luna?
Compare gpt-5.6-luna API pricing, supported endpoints, capabilities and access options on Modelsell.
How do I call gpt-5.6-luna?
Create an API key with access to gpt-5.6-luna, then use the exact model ID and a supported endpoint from the API access section. Request fields depend on the selected endpoint.
How is gpt-5.6-luna priced?
Pricing depends on the selected provider group and the model billing unit. The current input, output, request, or media prices are shown on this page before sign-up.
What is the context window of gpt-5.6-luna?
The model catalog lists a context window of 1050000 tokens. Check the selected endpoint for request limits.
What is the maximum output of gpt-5.6-luna?
The model catalog lists a maximum output of 128000 tokens. Your request settings may set a lower limit.
How should I evaluate gpt-5.6-luna for my project?
Start with the use cases and prompting guidance on this page, then evaluate the model with representative inputs from your project.