GPT 6 Sol
gpt-6-solCompare gpt-6-sol API pricing, supported endpoints, capabilities and access options on Modelsell.
- Starting price
- Input / Output · 1M
- Context
- 1.1M
- Maximum input window
- Max output
- 128K
- Maximum tokens per response
- Modalities
- →
- Knowledge cutoff
- Apr 2026
- Released
- Sep 2026
Pricing by Supplier
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 6 Sol
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 6 Sol
让模型理解项目，再开始改代码
GPT-6 Sol 适合需要持续阅读、推理和执行的编程任务。它可以分析需求与现有实现的关系，拆解跨文件改动，解释错误日志，并提出能够验证的修复方案。对于需要在代码、文档和界面之间来回对照的项目工作，它也支持图片输入，便于结合截图理解问题。
任务越具体，改动越可控
可以把功能目标、相关代码、限制条件和验收方式一并交给模型。例如实现一个新接口时，除了给出返回格式，还说明权限要求、兼容性、错误处理和已有调用方。这样模型能围绕实际工程边界制定方案，而不是只生成一段孤立示例。
多步骤任务适合先形成实施顺序，再逐步处理：读取相关实现、完成最小改动、运行验证、根据结果修正。应用接入工具后，模型可以参与这些操作；每一步都应以工具返回的实际状态为依据，保留失败信息供后续分析。
推理与工具的接入方式
GPT-6 Sol 支持不同推理强度，包括无需推理的模式。内置工具与带推理的函数调用适合使用 Responses API；通过 Chat Completions 调用函数时，需要将 reasoning_effort 设为 none。选择接口时先考虑工作流是否需要工具，再设置相应参数，避免任务已规划好却无法真正执行。
Use cases and prompting
接口开发示例
“为现有商品服务增加分页查询接口。先阅读路由、权限校验和数据库查询约定，再提出最小改动方案。要求兼容现有客户端，空结果返回空数组，非法页码给出明确错误。实现后验证权限、分页边界与排序稳定性，并说明实际运行结果。”
同时提供相关文件或让应用开放必要的读取与执行工具。
模型一直重新设计整个模块怎么办？
明确允许修改的范围和必须保留的接口，并要求先解释当前行为，再提出最小变更。
Chat Completions 的函数调用为什么不工作？
GPT-6 Sol 在该接口下要求 reasoning_effort=none。需要推理与工具协作时，使用 Responses API 更合适。
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-6-sol
What is gpt-6-sol?
Compare gpt-6-sol API pricing, supported endpoints, capabilities and access options on Modelsell.
How do I call gpt-6-sol?
Create an API key with access to gpt-6-sol, 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-6-sol 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-6-sol?
The model catalog lists a context window of 1050000 tokens. Check the selected endpoint for request limits.
What is the maximum output of gpt-6-sol?
The model catalog lists a maximum output of 128000 tokens. Your request settings may set a lower limit.
How should I evaluate gpt-6-sol for my project?
Start with the use cases and prompting guidance on this page, then evaluate the model with representative inputs from your project.