GPT-6 Sol
⧉GPT-6 Sol is the GPT-6 family model built for complex coding and agentic workflows: the same 1,050,000-token context and up to 128,000 output tokens as Astra, reasoning_effort in six steps from none to max, image input and tool calling. Fully OpenAI-SDK compatible - set model to gpt-6-sol - and served on both /chat/completions and /responses. Pay-as-you-go, failed calls never billed; cached input bills at one tenth, and we do not add the long-context surcharge the official API applies.
Live Test · Playground
Try out GPT-6 Sol right here (available after login).
Input
Advanced
This model is served over a ChatGPT-subscription upstream that does not accept temperature / top_p / max_tokens (they are silently ignored). Use reasoning_effort below for thinking depth, and prompt wording for length.
Conversation
About GPT-6 Sol
GPT-6 Sol is the model in OpenAI's GPT-6 family built for complex coding and agentic workflows, served on NezhaGate through the OpenAI-compatible API: the same 1,050,000-token context as GPT-6 Astra, up to 128,000 output tokens per call, reasoning_effort in six steps (none / low / medium / high / xhigh / max), image input and tool calling, on both /chat/completions and /responses. Set model to gpt-6-sol and keep your SDK and request shape. Pay-as-you-go, failed calls are never billed, and cached input settles at one tenth of the input rate.
Use cases
Multi-file refactors, cross-module debugging, whole features written from a spec: coding work that needs sustained reasoning goes to Sol for much less than Astra.
Codex, Cursor and Claude Code speak the OpenAI-compatible endpoint, so switching to GPT-6 Sol is a one-field change to model.
1,050,000 tokens holds a whole repository plus its docs in one call, with no chunking and stitching of your own.
reasoning_effort runs from none to max: keep simple edits on none or low for speed and raise the hard problems to xhigh or max.
How to choose
Pick GPT-6 Sol for complex coding and agentic workflows; step up to GPT-6 Astra for the hardest reasoning, and use GPT-6 Luna for simple high-volume, low-latency work. All three belong to the GPT-6 family and share a 1,050,000-token context, so switching is a one-field change.
FAQ
How is it different from GPT-6 Astra?
Which reasoning_effort levels are supported?
What are the context and output limits?
Can I use /v1/responses?
Am I charged for failed calls?
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