Technical specifications of GPT-6 Sol
| Item | GPT-6 Sol |
|---|---|
| Provider | OpenAI |
| Model ID | gpt-6-sol |
| Model family | GPT-6 |
| Input | Text, image |
| Output | Text |
| Context window | 1,050,000 tokens |
| Maximum output | 128,000 tokens |
| Knowledge cutoff | April 20, 2026 |
| Reasoning effort | none, low, medium (default), high, xhigh, max |
| Function calling | Supported |
| Structured outputs | Supported |
| Fine-tuning | Not supported |
| Responses API tools | Web search, file search, image generation, code interpreter, hosted shell, apply patch, skills, computer use, MCP, tool search |
| API endpoints | Responses, Chat Completions, Batch, Realtime, and others listed by OpenAI |
The official model page specifies a 1.05M-token context window, 128K maximum output, six reasoning-effort settings, text/image input, and text output. It also lists function calling, structured outputs, and the supported Responses API tools. Chat Completions function calling is available when reasoning_effort is set to none.
What Is GPT-6 Sol?
GPT-6 Sol is an OpenAI GPT-6 model positioned between the flagship GPT-6 Astra and more efficiency-oriented GPT-6 Luna. OpenAI's current model guidance describes Sol as the option for strong reasoning on demanding tasks, while Astra targets the highest level of capability and Luna targets efficient, repeatable work at scale.
GPT-6 Sol is specifically designed around a balance of reasoning capability, throughput, and cost. OpenAI highlights complex coding and agentic workflows as its primary focus rather than positioning Sol solely as a lightweight conversational model.
Main Features of GPT-6 Sol
- 1.05M-token context window: GPT-6 Sol can process very large amounts of contextual information within a single request, which is useful for repository-scale coding, long technical documents, and multi-step workflows.
- Configurable reasoning: The model supports six reasoning levels—
none,low,medium,high,xhigh, andmax—allowing developers to trade inference depth against latency and resource usage.mediumis the documented default. - Agentic tool use: Through the Responses API, GPT-6 Sol supports web search, file search, code interpreter, hosted shell, computer use, MCP, tool search, image generation, and other tools.
- Advanced coding workflows: OpenAI positions Sol specifically for complex coding and agentic workflows, making it relevant to repository analysis, debugging, refactoring, tool-driven development, and automated engineering tasks.
- Multimodal input: GPT-6 Sol accepts both text and image inputs while producing text output. This enables workflows involving screenshots, diagrams, visual debugging, and document analysis.
- Multiple API interfaces: GPT-6 Sol is available through both the Responses API and Chat Completions API. OpenAI recommends the Responses API when built-in tools and function calling are required.
Benchmark performance of GPT-6 Sol
OpenAI's current public GPT-6 Sol model documentation does not provide a general benchmark table comparable to the model's full technical specification page, so unsupported benchmark figures should not be presented as official Sol scores.
One official evaluation source, the GPT-6 Astra System Card, reports GPT-6 Sol on HealthBench. On its length-adjusted reporting, Sol scored 60.8 on HealthBench Professional, 53.2 on HealthBench, 30.1 on HealthBench Hard, and 96.2 on HealthBench Consensus. The same source notes that the shorter default answers affected some HealthBench results, so these figures should be interpreted with the evaluation methodology rather than as a universal model-quality score.
Independent evaluation from Artificial Analysis reports a GPT-6 Sol maximum Intelligence Index score of 48 in its September 2026 release analysis and notes that Sol's cost per Intelligence Index task was about half that of GPT-5.6 Sol. This is an external benchmark, not an OpenAI-reported score, and should be kept separate from official OpenAI measurements.
GPT-6 Sol vs GPT-6 Astra vs GPT-6 Luna
| Model | Positioning | Context | Max output | Reasoning |
|---|---|---|---|---|
| GPT-6 Astra | Highest-capability GPT-6 model | 1.05M | 128K | low–max |
| GPT-6 Sol | Complex coding and agentic workflows | 1.05M | 128K | none–max |
| GPT-6 Luna | Efficient, high-volume workloads | 1.05M | 128K | none–max |
The distinction is primarily about workload positioning rather than context capacity: all three current GPT-6 flagship models have a 1.05M-token context window and 128K maximum output according to OpenAI's model catalog. OpenAI recommends Astra for the most demanding reasoning and coding, Sol for strong reasoning on demanding tasks, and Luna for efficient work at scale.
GPT-6 Sol Limitations
GPT-6 Sol has several documented constraints developers should account for:
- No fine-tuning: OpenAI currently lists fine-tuning as unsupported for GPT-6 Sol.
- Text output only: Although it accepts image input, its documented output modality is text.
- Audio and video are not supported as model modalities: OpenAI lists audio and video as unsupported input/output modalities for this model.
- Tool-dependent workflows should use Responses API: OpenAI recommends the Responses API for built-in tools and function calling. Chat Completions supports function calling for Sol when
reasoning_effortis set tonone. - Reasoning level affects behavior: Developers should benchmark different
reasoning.effortsettings on their own workload rather than assuming the highest setting is always preferable.
GPT-6 Sol Use Cases
Large-Scale Software Engineering
The combination of a 1.05M-token context window, configurable reasoning, and agentic tools makes GPT-6 Sol suitable for repository-level development tasks such as code analysis, refactoring, debugging, test generation, and multi-file changes.
Agentic Workflows
GPT-6 Sol can combine reasoning with tools including web search, file search, code interpreter, hosted shell, computer use, MCP, and tool search through the Responses API. This makes it suitable for applications where the model needs to reason through multiple steps and interact with external tools.
Long-Context Document Analysis
The million-token context class can support large technical specifications, research collections, source repositories, contracts, or other long-form inputs where retaining substantial context is important.
Visual Software Debugging
Because GPT-6 Sol accepts image input, developers can incorporate screenshots, diagrams, charts, and other visual material into coding or analytical workflows.
Enterprise Knowledge Work
The model's long context, structured outputs, function calling, and tool ecosystem can be used for document-heavy enterprise workflows, internal knowledge systems, research assistants, and automated analysis pipelines.
How to Access GPT-6 Sol API with CometAPI
CometAPI currently lists GPT-6 Sol (gpt-6-sol) in its model catalog.
Step 1: Create a CometAPI API Key
Create or sign in to a CometAPI account and generate an API key from the API Keys section.
Step 2: Select GPT-6 Sol
Use the model ID:
gpt-6-sol
CometAPI provides an OpenAI-compatible interface, allowing developers to use the existing OpenAI SDK structure while changing the API base URL and model identifier.
Step 3: Send Your Request
Set the CometAPI base URL to:
https://api.cometapi.com/v1
Then specify gpt-6-sol as the model. The same integration approach can be used to switch among supported models through CometAPI without creating separate provider integrations for every model. CometAPI itself documents both Chat Completions and Responses endpoints for the model.