OpenAI model
GPT-6 Sol
A GPT-6 reasoning model built for complex coding and agentic workflows that combine long context, tool use, and multi-step execution.
- Context window
- 1.05M
- tokens
- Max output
- 128K
- tokens
- Input
- $2.00
- per 1M tokens
- Cached input
- $0.20
- per 1M tokens
- Output
- $10.00
- per 1M tokens
01 / Overview
What GPT-6 Sol Is
GPT-6 Sol is an OpenAI reasoning model built specifically for complex coding and agentic workflows, where completing the task often requires more than generating a single text response.
A Model for Workflows That Continue Beyond the First Answer
Agentic applications introduce a different type of model workload. The model may need to understand a goal, inspect a large working context, decide which tool to use, evaluate the tool result, revise its plan, and continue until the larger task is complete.
GPT-6 Sol is designed around that pattern. Its positioning emphasizes complex coding and agentic execution rather than simple high-volume transformations. That makes it particularly relevant for software-engineering agents, codebase-level tasks, technical automation, research-assisted development, and workflows that combine reasoning with external tools.
OpenAI now identifies GPT-6.1 Sol as the newer Sol model, so GPT-6 Sol should be evaluated with that lifecycle context in mind. It remains useful as a documented GPT-6 model with its own API behavior, pricing, reasoning controls, and supported toolset.
- Designed for complex coding and agentic workflows.
- Supports reasoning from
nonethroughmax. - Provides a 1.05M-token working context.
- Supports function calling, structured outputs, and Responses API tools.
- Uses text and image input with text output.
- Provider
- OpenAI
- Family
- GPT-6
- Positioning
- Coding & agentic workflows
- Knowledge cutoff
- Apr 20, 2026
- Input modalities
- Text, Image
- Output modality
- Text
- Default reasoning
- Medium
02 / Use cases
Where GPT-6 Sol Fits Best
GPT-6 Sol is most relevant when the application expects the model to reason through a technical problem and take multiple controlled actions rather than simply return a short answer.
Complex Tasks with State, Tools, and Iteration
Coding agents are an obvious fit because software work naturally involves iteration. A model may inspect source files, search for an implementation detail, run code, review an error, apply a patch, and then verify the result. Each step depends on the state created by the previous one.
The same pattern appears outside pure coding. An agent can search for information, read files, use a computer interface, invoke application functions, or work through an MCP integration. What matters is that the model can coordinate reasoning and execution across several steps.
GPT-6 Sol should therefore be tested using complete workflows rather than isolated prompts. A model that writes a good code snippet is not necessarily equally strong at navigating a repository, choosing tools, recovering from failures, and finishing a task end to end.
- Multi-file coding and debugging tasks.
- Software agents that execute, inspect, and revise work.
- Tool-driven research and technical automation.
- Long-running workflows with intermediate state and results.
- Applications that require structured outputs after several reasoning steps.
- 01
Coding agents
Inspect repositories, reason about implementation changes, use tools, and iterate toward a verified result.
- 02
Tool orchestration
Select and coordinate search, code, shell, file, computer-use, or application-defined tools.
- 03
Technical investigation
Work through logs, source code, documentation, and evidence before proposing or applying a solution.
- 04
Multi-step automation
Maintain task state across several actions instead of treating each model call as an isolated response.
03 / Pricing
GPT-6 Sol Pricing
The GPT-6 Sol model page lists $2.00 per million input tokens, $0.20 per million cached input tokens, $2.50 per million cache-write tokens, and $10.00 per million output tokens.
Agent Cost Includes More Than One Model Turn
For an agentic workflow, the token price of a single request is only one part of the total cost. A complete task may involve several model turns, tool responses added back into context, repeated instructions, code or file contents, and a final generated artifact.
That means the useful economic metric is often cost per completed workflow. A seemingly inexpensive step can become costly if the agent repeatedly re-reads large context or takes unnecessary iterations. Conversely, prompt caching can reduce repeated input cost when substantial prefixes remain stable across requests.
Output usage also deserves attention. Coding agents can generate patches, explanations, plans, and structured results over multiple turns, so output-token spend can accumulate even when each individual response is moderate.
- $2.00 per 1M standard input tokens.
- $0.20 per 1M cached input tokens.
- $2.50 per 1M cache-write tokens.
- $10.00 per 1M output tokens.
- Tool-specific operations may have separate charges.
1M tokens · USD
- Input
- $2.00
- Cached input
- $0.20
- Cache writes
- $2.50
- Output
- $10.00
Example: 10K input + 2K output
- Input cost
- $0.0200
- Output cost
- $0.0200
- Estimated total
- $0.0400
04 / Context
A 1.05M-Token Context for Codebases and Agent State
GPT-6 Sol provides a 1,050,000-token context window with up to 128,000 output tokens, giving agentic workflows substantial room for code, instructions, tool results, documents, and accumulated task state.
Context Is Part of the Agent Architecture
For coding and agentic systems, context is not just conversation history. It can contain repository files, system rules, issue descriptions, prior edits, test output, shell results, retrieved documentation, structured application state, and the model's current objective.
A large context window gives developers flexibility in how these pieces are assembled. It can reduce the need to aggressively truncate information during complicated workflows, especially when relationships between files or previous tool results matter.
But larger context does not automatically produce better agent behavior. Repeated logs, irrelevant files, stale state, and duplicated instructions can consume tokens while making the current task harder to reason about.
Effective GPT-6 Sol implementations should therefore measure context composition, not only total size. The goal is to retain the information the agent needs for continuity while avoiding unnecessary context growth across long workflows.
- 1,050,000-token total context window.
- Up to 128,000 output tokens.
- Suitable for large code and document working sets.
- Useful for retaining intermediate agent state and tool results.
- Requests above 272K input tokens use higher pricing multipliers.
Context window
1,050,000
Max output
128,000
An agentic context may include system instructions, repository files, task history, retrieved documentation, tool output, application state, and the current user request.
05 / Reasoning
Reasoning from None to Max
GPT-6 Sol supports none, low, medium, high, xhigh, and max reasoning effort, with medium as the default.
Reasoning Configuration Changes API Behavior
Reasoning effort is especially important for GPT-6 Sol because it influences both task strategy and API integration behavior.
For straightforward coding transformations or simple tool decisions, a lower effort can provide a useful baseline. More difficult debugging, architectural analysis, multi-step coding, or workflows with uncertain intermediate state may benefit from higher effort.
OpenAI also documents an important distinction between APIs: the Responses API should be used for built-in tools and function calling. Chat Completions supports function calling for GPT-6 Sol only when reasoning_effort is set to none.
For agentic products, that means reasoning configuration cannot be considered independently from API architecture. Evaluations should reflect the exact API and reasoning settings that production will use.
Focused coding step
Test lower effort for well-scoped transformations, targeted edits, and predictable technical tasks.
Agentic problem solving
Test higher effort for debugging, planning, tool coordination, and workflows with several dependent decisions.
06 / Capabilities
A Toolset Designed for Agents
GPT-6 Sol supports the core capabilities required for agentic applications: streaming, function calling, structured outputs, image input, and a broad Responses API toolset.
Move from Reasoning to Execution
The model supports web search and file search for information retrieval, image generation for visual output, code interpreter for executable analysis, and hosted shell for command-line workflows.
Apply Patch is particularly relevant for coding agents because it gives a workflow a structured mechanism for making code changes. Computer use extends the model into graphical interfaces, while MCP, Skills, and Tool Search provide additional ways to connect the model to external capabilities.
These features make GPT-6 Sol suitable for systems where the model is expected to act within an application environment instead of only describing what should be done.
The model accepts text and image input and generates text output. Direct audio and video modalities are not supported. Fine-tuning is currently listed as unsupported.
- Streaming responses.
- Function calling and structured outputs.
- Web search and file search.
- Image generation and code interpreter.
- Hosted shell and Apply Patch.
- Skills and computer use.
- MCP integrations and Tool Search.
- Fine-tuning is currently not supported.
- Supported
Text and vision
Accept text and image input and generate text output.
- Supported
Function calling
Connect model decisions to application-defined actions through supported API configurations.
- Supported
Coding tools
Use code interpreter, hosted shell, and Apply Patch for technical workflows.
- Supported
Search and files
Use web search and file search to retrieve information during a workflow.
- Supported
Computer use
Interact with graphical environments as part of a multi-step task.
- Supported
Extensible agents
Use MCP, Skills, Tool Search, image generation, and other supported Responses API tools.
07 / Evaluation
Strengths and Limitations
GPT-6 Sol remains a capable coding and agentic model, but model selection should account for the availability of the newer GPT-6.1 Sol as well as the actual complexity of the workload.
Strengths
Agentic-first positioning
The model is explicitly designed around complex coding and agentic workflows rather than generic text generation alone.
Broad execution toolset
Search, code execution, shell, patching, computer use, MCP, Skills, and Tool Search support end-to-end workflows.
Large context capacity
A 1.05M-token context window provides room for codebases, documentation, task state, and accumulated tool results.
Wide reasoning range
Reasoning can be configured from none through max, allowing different task classes to use different levels of inference effort.
What to consider
A newer Sol model exists
OpenAI identifies GPT-6.1 Sol as the newer Sol model, so new deployments should evaluate both lifecycle and performance requirements.
Agent cost can compound
Multi-turn workflows can accumulate input, output, tool, and cache costs even when the per-token rate appears straightforward.
API behavior depends on reasoning mode
Chat Completions function calling is limited to reasoning_effort none; built-in tools and broader function-calling workflows should use Responses API.
Fine-tuning is unavailable
The current OpenAI documentation lists fine-tuning as unsupported for GPT-6 Sol.
Evaluate agentic performance
Test GPT-6 Sol on your real coding and agent workflows
Run representative tasks with your actual prompts, context, tools, and reasoning settings, then inspect response quality, token usage, request cost, and context consumption in EidoStack.
Start FreeConnect your own OpenAI API key and evaluate GPT-6 Sol under the same conditions your production workflow will use.
Common Questions
What is GPT-6 Sol?
GPT-6 Sol is an OpenAI reasoning model built for complex coding and agentic workflows. It supports long context, configurable reasoning, function calling, structured outputs, and a broad set of Responses API tools.
Is GPT-6 Sol still the newest Sol model?
No. OpenAI's current documentation identifies GPT-6.1 Sol as the newer Sol model. GPT-6 Sol remains documented and available as its own API model.
How much does GPT-6 Sol cost?
The current GPT-6 Sol model page lists $2.00 per 1M input tokens, $0.20 per 1M cached input tokens, $2.50 per 1M cache-write tokens, and $10.00 per 1M output tokens. Tool-specific fees may apply separately.
What is the context window of GPT-6 Sol?
GPT-6 Sol has a 1,050,000-token context window and supports up to 128,000 output tokens.
What reasoning levels does GPT-6 Sol support?
GPT-6 Sol supports none, low, medium, high, xhigh, and max reasoning effort. Medium is the default.
What is the knowledge cutoff for GPT-6 Sol?
OpenAI lists April 20, 2026 as the knowledge cutoff for GPT-6 Sol.
Does GPT-6 Sol support image input?
Yes. GPT-6 Sol accepts text and image input and generates text output. Direct audio and video modalities are not supported.
What tools does GPT-6 Sol support?
The current OpenAI documentation lists web search, file search, image generation, code interpreter, hosted shell, Apply Patch, Skills, computer use, MCP, and Tool Search as supported Responses API tools.
Can GPT-6 Sol call functions through Chat Completions?
Yes, but OpenAI states that Chat Completions supports function calling for GPT-6 Sol only when reasoning_effort is set to none. The Responses API should be used for built-in tools and broader function-calling workflows.
What workloads are a good fit for GPT-6 Sol?
GPT-6 Sol is suited to complex coding, debugging, software agents, technical investigation, tool orchestration, and other multi-step workflows where the model must reason and act across changing task state.
Can GPT-6 Sol be fine-tuned?
No. The current OpenAI model documentation lists fine-tuning as unsupported for GPT-6 Sol.
Model information
Last updated
The specifications and prices on this page are based on the official OpenAI model documentation for GPT-6 Sol. OpenAI also identifies GPT-6.1 Sol as the newer Sol model. Provider pricing, tools, processing options, model limits, and availability may change and should be checked before production decisions.