1.05M-Token Context
Keep repository files, specifications and earlier findings in one request. The model supports a 1,050,000-token context window and up to 128,000 output tokens; large prompts still need retrieval discipline and a cost check.
curl --request POST "https://gptproto.com/v1/chat/completions" \
--header "Authorization: Bearer $GPTPROTO_API_KEY" \
--header "Content-Type: application/json" \
--data '{
"model": "gpt-6-sol",
"messages": [
{
"role": "user",
"content": "Hello"
}
]
}'Chat, coding agents & document work. Priced per 1M tokens — input, cached input and output are billed separately. GPTProto is 20% below official rates.
OpenAI · ≈ 148M tokens/mo (48M cached)
OpenRouter costs include its ~5.5% credit purchase fee. GPTProto applies a per-model discount (10–30% off) and your bonus credits are also spent at discounted rates — savings compound. Estimates assume a 60% cache hit rate.
Use GPT-6 Sol when an application needs to reason across files, tool results and several stages of a task. Start with a representative coding or agent workflow, then compare its completed-task rate, latency and total cost with other available models.
Keep repository files, specifications and earlier findings in one request. The model supports a 1,050,000-token context window and up to 128,000 output tokens; large prompts still need retrieval discipline and a cost check.
Choose none, low, medium, high, xhigh or max according to the job. Start with the default medium setting, then test lower effort for routine work and higher effort for harder tasks.
Use GPT-6 Sol for code changes, debugging and plans that require several steps. Keep tool execution and verification in your application; the model proposes actions, while your workflow determines what runs.
OpenAI documents text and image input with text output. Use image understanding for screenshots or diagrams where supported by the selected GPTProto route; this model does not directly output images, audio or video.
GPT-6 Sol is OpenAI's model for demanding coding and agentic work. Its published model ID is gpt-6-sol. It accepts text and image input, returns text, and supports streaming and structured outputs upstream. It is a hosted proprietary model, not an open-source download.
Sol sits between Luna and the flagship Astra tier. Choose it when coding or multi-step work needs more reasoning than a routine, high-volume task. Test the same work and acceptance criteria across tiers; compare completed tasks, latency and total tokens, not just list prices.
| Published specification | GPT-6 Sol |
|---|---|
| OpenAI model ID | gpt-6-sol |
| Context window | 1,050,000 tokens |
| Maximum output | 128,000 tokens |
| Input / output | Text and image / text |
| Reasoning effort | none, low, medium (default), high, xhigh, max |
| Streaming / structured outputs | Supported upstream |
| Fine-tuning | Not supported |
The context window is a capacity limit, not a reason to send every repository file on each call. Retrieve relevant files and reserve output budget for the patch. For long tasks, log token usage, tool calls and whether the result passed your tests; retries change the true cost.
Repository-scale coding. Provide files and interfaces relevant to a change, then check the patch with your tests. This suits cross-file edits, reviews and debugging.
Agentic work. Your application handles search, file access and tool execution. OpenAI recommends Responses for tool calling; Chat Completions supports function calling only with reasoning_effort: "none". Check the route and parameters GPTProto exposes before migrating an agent.
Long documents and visual references. Analyze specifications or screenshots alongside written requirements. Use a separate model when the workflow needs image output.
All three models publish 1,050,000-token context and 128,000-token maximum output. These are OpenAI upstream standard rates, not GPTProto prices; use the live widget for GPTProto billing.
| Model | Start here when… | OpenAI list input / output per 1M tokens | Practical check |
|---|---|---|---|
| GPT-6 Luna | The workload is focused, repetitive and sensitive to cost or response time. | $0.10 / $0.50 | Measure whether it completes enough tasks without escalation. |
| GPT-6 Sol | Coding, reasoning or multi-step agent tasks need more headroom. | $2 / $10 | Compare accepted results and tool loops against Luna and Astra. |
| GPT-6 Astra | The hardest cross-tool, research or computer-use tasks justify the flagship tier. | $10 / $50 | Check whether its higher success rate reduces total cost per completed task. |
OpenAI publishes separate cached-input, cache-write and long-context rates. Above 272,000 input tokens, higher long-context rates apply to the entire request upstream. Check GPTProto's live billing rules before budgeting a long agent run.
Test your prompt and tool schema with the model ID on this page. Confirm that the selected route supports the needed combination of reasoning and tools. Compare low and medium effort on representative tasks, keep a fallback in your application, and track cost per completed task.
Guides, comparisons, and updates related to this model.
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