GPT-6 Sol is OpenAI’s GPT-6 model designed for complex coding, advanced reasoning and agentic AI workflows.

OpenAI released GPT-6 Sol on September 22, 2026, alongside GPT-6 Luna. The model sits between efficiency-focused GPT-6 Luna and the more powerful GPT-6 Astra in OpenAI’s current GPT-6 family.

OpenAI describes GPT-6 Sol as being built to power complex coding and agentic workflows. It offers a 1,050,000-token context window, up to 128,000 output tokens, reasoning support, text and image input, structured outputs, function calling and access to a wide range of tools.

Standard short-context API pricing starts at:

• $2 per million input tokens
• $0.20 per million cached input tokens
• $2.50 per million cache-write tokens
• $10 per million output tokens

Those specifications make GPT-6 Sol particularly relevant to developers building coding assistants, autonomous workflows, research systems and AI agents for small business.

This guide explains what GPT-6 Sol is, how much it costs, how its API works, which tools it supports, where it is available and when you should choose it instead of GPT-6 Luna or GPT-6 Astra.

What Is GPT-6 Sol?

GPT-6 Sol is a reasoning model in OpenAI’s GPT-6 family.

Its primary role is to provide strong intelligence for demanding tasks without requiring the higher cost of GPT-6 Astra.

OpenAI’s current model guidance separates the GPT-6 family into three broad tiers:

GPT-6 Astra — OpenAI’s most capable model for the hardest end-to-end work.

GPT-6 Sol — strong reasoning for demanding coding and agentic tasks.

GPT-6 Luna — efficient and repeatable work at scale.

That positioning makes Sol an important middle ground.

A developer may not need GPT-6 Astra for every sophisticated task, but GPT-6 Luna may not provide enough capability for a difficult multi-step coding or research workflow.

GPT-6 Sol is intended to fill that gap.

GPT-6 Sol Release Date

OpenAI released GPT-6 Sol on September 22, 2026.

The official API model identifier is:

gpt-6-sol

OpenAI released the model alongside GPT-6 Luna through the Responses API and Chat Completions API. Both models accept text and image inputs and generate text output.

GPT-6 Sol was also introduced in ChatGPT Work and Codex, where it is separate from the models used in regular ChatGPT conversations.

GPT-6 Sol Specifications

Here are the major specifications currently published by OpenAI:

Feature	GPT-6 Sol
Release date	September 22, 2026
Model ID	gpt-6-sol
Context window	1,050,000 tokens
Maximum output	128,000 tokens
Short-context input price	$2 / 1M tokens
Cached input	$0.20 / 1M tokens
Cache writes	$2.50 / 1M tokens
Output price	$10 / 1M tokens
Knowledge cutoff	April 20, 2026
Text input	Supported
Image input	Supported
Reasoning	Supported
Function calling	Supported
Structured outputs	Supported
Fine-tuning	Not currently supported

These specifications come from OpenAI’s current GPT-6 Sol model documentation.

GPT-6 Sol Pricing

GPT-6 Sol uses token-based API pricing.

For standard requests within OpenAI’s short-context pricing range, the current rates are:

Token Type	Price per 1M Tokens
Input	$2.00
Cached input	$0.20
Cache writes	$2.50
Output	$10.00

Cached input therefore costs 10% of the normal input-token rate, while cache writes are billed at 1.25 times the uncached input rate.

GPT-6 Sol Cost Example

Suppose an application sends:

200,000 input tokens

and receives:

20,000 output tokens.

Using the standard short-context rates:

Input:

0.2 × $2 = $0.40

Output:

0.02 × $10 = $0.20

Estimated total token cost:

$0.60

This simplified example excludes tool-call fees, cache behavior, data-residency premiums and other processing options.

GPT-6 Sol Long-Context Pricing

GPT-6 Sol supports more than one million tokens of context, but very large prompts cost more.

OpenAI states that prompts containing more than 272,000 input tokens are priced at:

• 2× the normal input and cache rates
• 1.5× the normal output rate

for the full request.

That means long-context standard rates effectively become:

Long-Context Usage	Price per 1M Tokens
Input	$4
Cached input	$0.40
Cache writes	$5
Output	$15

Developers planning to use the full 1.05-million-token context window should therefore calculate costs using the long-context tier.

Batch, Flex and Fast Mode Pricing

OpenAI also offers different processing tiers.

For GPT-6 Sol:

Batch and Flex processing are priced at 50% of Standard rates.

Fast mode is priced at 2× the applicable rates.

This creates several optimization options.

Batch processing can make sense when the work does not require immediate responses.

Fast mode can make sense when latency is more important than token cost.

Standard processing provides the normal balance.

GPT-6 Sol Context Window

GPT-6 Sol has a 1,050,000-token context window.

That allows the model to work with extremely large amounts of information during a single workflow.

Potential uses include:

• Large code repositories
• Extensive documentation
• Research libraries
• Long business reports
• Multiple contracts
• Technical manuals
• Long conversation histories
• Customer records
• Knowledge bases
• Multi-document analysis

A million-token context window does not mean that every workflow should automatically include a million tokens.

Large prompts cost more and may introduce unnecessary information.

The goal should be to provide the model with the relevant context, not simply the maximum possible context.

GPT-6 Sol Maximum Output

GPT-6 Sol supports up to 128,000 output tokens.

That gives it enough output capacity for extensive tasks such as:

• Large code changes
• Long technical documentation
• Comprehensive reports
• Structured transformations
• Multi-file output
• Detailed analysis

Most everyday tasks will require far fewer output tokens.

GPT-6 Sol Reasoning Levels

GPT-6 Sol supports several reasoning-effort settings:

none

low

medium

high

xhigh

max

The default is medium.

This allows developers to balance reasoning depth against speed and cost.

For example:

A straightforward information-extraction task may require little additional reasoning.

A complicated debugging problem or multi-step research workflow may benefit from a higher reasoning level.

Developers should evaluate real workloads rather than automatically selecting the maximum setting for every request.

What Tools Does GPT-6 Sol Support?

One of GPT-6 Sol’s strongest features is its tool support.

Through OpenAI’s Responses API, GPT-6 Sol currently supports:

• Web search
• File search
• Image generation
• Code interpreter
• Hosted shell
• Apply patch
• Skills
• Computer use
• MCP
• Tool search

It also supports function calling and structured outputs.

These capabilities are particularly important for agentic AI.

A model that only generates text can suggest what should happen.

A model connected to tools can potentially help perform the workflow.

GPT-6 Sol for AI Agents

AI agents are one of the main use cases OpenAI explicitly highlights for GPT-6 Sol.

Traditional AI interaction usually looks like:

Prompt → response

An agentic workflow may instead look like:

Goal → plan → search → tool call → analysis → action → verification → result

For example, a business research agent could:

1. Receive a research objective.
2. Search current sources.
3. Retrieve company documents.
4. Analyze the information.
5. Run calculations.
6. Produce a report.
7. Create follow-up actions.

GPT-6 Sol’s reasoning capability, large context window and tool support make it suitable for this type of workflow.

For a broader explanation of business agents, see our guide to AI agents for small business.

Async Tool Calling in GPT-6

GPT-6 introduces support for asynchronous tool calling.

OpenAI says a GPT-6 model can continue reasoning, call other tools or work on independent parts of a request while an external tool is still running.

This can make sophisticated agents more efficient.

Imagine an AI research agent that needs to query three systems.

Instead of waiting for one tool to finish before beginning anything else, parts of the workflow may be handled in parallel.

The application still executes and manages external tools, but the model can coordinate work more flexibly.

Mid-Turn Steering

GPT-6 also supports mid-turn steering.

This means a user or application can provide additional instructions while the model is already working.

For example:

“Also prioritize companies with fewer than 500 employees.”

Or:

“Exclude European suppliers.”

Or:

“Use the revised budget instead.”

For long-running agents, this is valuable because requirements frequently change while work is underway.

GPT-6 Sol for Coding

Coding is one of GPT-6 Sol’s primary intended workloads.

OpenAI explicitly describes the model as built for complex coding and agentic workflows.

Potential coding applications include:

• Repository analysis
• Debugging
• Refactoring
• Test generation
• Code review
• Multi-file changes
• Dependency upgrades
• Large migrations
• Documentation
• Tool-assisted development
• Agentic software engineering

The model’s supported tools are particularly relevant here.

GPT-6 Sol can work with capabilities such as code interpreter, hosted shell and apply patch through supported Responses API workflows.

These features allow applications to move beyond simply asking the model to generate a code snippet.

GPT-6 Sol vs Claude Opus 5.5 for Coding

Another current model targeting advanced coding and agentic workflows is Anthropic’s Claude Opus 5.5.

The models differ in pricing, ecosystems, tool support and implementation details.

Rather than assuming one model will always outperform the other, developers should test their real codebase and workflow.

If you are evaluating both ecosystems, read our complete Claude Opus 5.5 guide.

A dedicated Sol-vs-Claude comparison can then be built as a separate page rather than overloading this pillar article.

GPT-6 Sol for Customer Service

GPT-6 Sol can also be used in sophisticated customer-support systems.

An AI customer-service workflow might:

1. Understand the customer’s request.
2. Retrieve account information.
3. Search approved documentation.
4. Decide which policy applies.
5. Draft a response.
6. Use a tool when necessary.
7. Escalate unusual situations.

For simple classification and repetitive requests, GPT-6 Luna may be more cost effective.

Sol may become valuable when the problem requires deeper reasoning or complicated tool coordination.

For implementation ideas, see our guide to AI customer service agents for small business.

GPT-6 Sol for AI Receptionists

AI receptionists combine voice technology with reasoning, scheduling and business integrations.

A phone workflow might involve:

• Understanding caller intent
• Looking up information
• Checking a calendar
• Scheduling an appointment
• Creating a lead
• Escalating complicated requests

A system could use a lower-cost model for predictable steps and GPT-6 Sol when more complicated reasoning is required.

Businesses exploring phone automation can read our AI receptionist for small business guide.

GPT-6 Sol for Research

GPT-6 Sol can support research workflows involving:

• Web search
• File retrieval
• Large context
• Structured output
• Data analysis
• Tool use

The model can therefore serve as the reasoning layer behind research agents that collect and synthesize information across multiple sources.

Applications still need to verify important facts.

Having access to web search does not automatically guarantee that every generated conclusion is correct.

GPT-6 Sol for Large Documents

The 1.05-million-token context window makes GPT-6 Sol relevant to long-document tasks.

Examples include:

• Contract collections
• Research papers
• Financial reports
• Technical documentation
• Policy libraries
• Business records
• Large internal knowledge bases

However, requests above 272K input tokens enter the higher long-context pricing tier.

Developers should therefore use retrieval and document-selection strategies where appropriate instead of automatically sending entire archives into every request.

Does GPT-6 Sol Support Images?

Yes.

GPT-6 Sol accepts:

• Text input
• Image input

and generates text output.

That enables tasks involving:

• Screenshots
• Charts
• Interfaces
• Diagrams
• Documents
• Photographs
• Visual debugging

Audio and video input are not listed as supported modalities on the GPT-6 Sol model page.

Does GPT-6 Sol Generate Images?

GPT-6 Sol’s direct model output is text, but image generation is available as a supported tool through the Responses API.

This distinction matters.

Sol itself is not simply an image-generation model.

Instead, an agent powered by Sol can call an image-generation tool when the workflow requires it.

Does GPT-6 Sol Support Computer Use?

Yes.

Computer use is listed among the tools supported by GPT-6 Sol through the Responses API.

Computer-use capabilities enable agentic systems to interact with graphical interfaces where appropriate.

Because computer-use workflows can take actions rather than merely provide information, developers should use suitable approvals, permissions and safeguards.

Does GPT-6 Sol Support MCP?

Yes.

OpenAI currently lists MCP as a supported GPT-6 Sol tool.

MCP can help applications connect AI systems with external tools and data sources through supported integrations.

This is especially relevant to enterprise and agent-development workflows.

Does GPT-6 Sol Support Structured Outputs?

Yes.

Structured outputs are supported.

This allows developers to require predictable schemas rather than free-form prose.

For example, a customer-service workflow might return:

customer_intent
priority
department
recommended_action
requires_human

Structured outputs can make AI easier to connect to downstream software.

GPT-6 Sol API

The model identifier is:

gpt-6-sol

OpenAI supports GPT-6 Sol through:

• Responses API
• Chat Completions
• Batch API

among the listed endpoints.

OpenAI specifically recommends the Responses API when using built-in tools and function calling with GPT-6 Sol.

Chat Completions supports function calling only when reasoning_effort is set to none.

For modern agent workflows, the Responses API is therefore the more natural interface.

Is GPT-6 Sol Available on Amazon Bedrock?

Yes.

OpenAI’s current Amazon Bedrock documentation says GPT-6 Sol and GPT-6 Luna are available through Bedrock Runtime and Mantle in the us-east-1 region.

The Bedrock model identifier for Sol is:

openai.gpt-6-sol

This gives AWS customers another deployment path for the model.

GPT-6 Sol Data Residency

OpenAI says EU data residency for GPT-6 Sol is available only with Standard processing.

Regional-processing endpoints add a 10% premium where available.

Organizations with residency requirements should therefore account for both processing-mode restrictions and pricing differences.

GPT-6 Sol Knowledge Cutoff

OpenAI currently lists GPT-6 Sol’s knowledge cutoff as:

April 20, 2026.

That means users should not assume the model’s built-in knowledge contains events occurring after that date.

Current-information workflows can use tools such as web search or connected retrieval sources.

Is GPT-6 Sol Available in ChatGPT?

GPT-6 Sol is available in ChatGPT Work and Codex for eligible users.

OpenAI’s September 22 release notes explicitly state that GPT-6 Sol and GPT-6 Luna in Work and Codex are separate from the models available in regular Chat.

Availability depends on the user’s plan, workspace configuration and rollout access.

So users should not assume that seeing “GPT-6” elsewhere in ChatGPT means they are specifically using GPT-6 Sol.

Is GPT-6 Sol Free?

For the API, OpenAI’s current GPT-6 Sol rate-limit table shows the Free tier as not supported.

API developers therefore need an eligible paid usage tier.

ChatGPT Work and Codex availability is determined separately by the user’s plan and workspace settings.

GPT-6 Sol vs GPT-6 Luna

GPT-6 Luna and GPT-6 Sol share several important specifications.

Both have:

• 1.05M context windows
• 128K maximum outputs
• Reasoning support
• Image input
• Function calling
• Structured outputs
• Web search
• File search
• Computer use

But their positioning and pricing are very different.

Feature	GPT-6 Sol	GPT-6 Luna
Main use	Complex coding and agents	Efficient high-volume tasks
Input price	$2 / 1M	$0.10 / 1M
Output price	$10 / 1M	$0.50 / 1M
Cached input	$0.20 / 1M	$0.01 / 1M
Context	1.05M	1.05M
Max output	128K	128K

If you are choosing between them, read our full GPT-6 Luna vs GPT-6 Sol comparison.

For a deeper Luna overview, see What Is GPT-6 Luna?.

GPT-6 Sol vs GPT-6 Astra

GPT-6 Astra sits above Sol in OpenAI’s current model lineup.

OpenAI describes Astra as its most capable model for the hardest end-to-end work, including complex reasoning, coding, computer use, research and document creation.

Standard short-context pricing is also substantially higher:

Model	Input / 1M	Output / 1M
GPT-6 Sol	$2	$10
GPT-6 Astra	$10	$50

Astra therefore costs five times as much per standard short-context input and output token.

The decision should depend on whether Astra’s additional capability produces enough value for the workload.

GPT-6 Sol vs GPT-5.6 Sol

GPT-6 Sol also offers significantly lower API pricing than GPT-5.6 Sol’s current model-page pricing.

GPT-5.6 Sol is listed at:

• $4 per million input tokens
• $20 per million output tokens

GPT-6 Sol is listed at:

• $2 per million input tokens
• $10 per million output tokens

That represents a 50% reduction in both listed standard input and output prices.

OpenAI’s launch announcement also described Sol and Luna as benefiting from improved caching and inference efficiency.

When Should You Use GPT-6 Sol?

GPT-6 Sol is particularly well suited when a task requires more reasoning than a lightweight model but does not necessarily justify OpenAI’s most expensive model.

Strong candidates include:

Complex Coding

Use Sol when the model must understand and modify substantial software systems.

Multi-Step AI Agents

Use Sol when workflows involve several tools and complex decisions.

Research

Use Sol when information must be gathered, interpreted and synthesized.

Professional Knowledge Work

Use Sol for demanding analysis involving documents, data and multiple constraints.

Computer-Use Workflows

Use Sol when an agent needs to reason while interacting with supported software interfaces.

When Should You Use GPT-6 Luna Instead?

Luna is often more appropriate when:

• The task is repetitive.
• The expected output is clear.
• Volume is high.
• Cost matters heavily.
• Outputs are easy to validate.

Examples include:

• Classification
• Extraction
• Basic routing
• Routine summaries
• Content tagging
• Simple workflow steps

The best architecture may use both models.

A system could process routine work with Luna and escalate harder cases to Sol.

When Should You Use GPT-6 Astra Instead?

Astra may make sense when:

• The task is exceptionally difficult.
• Failure is expensive.
• Maximum capability matters more than cost.
• The workflow involves the hardest end-to-end reasoning.

OpenAI positions Astra for its highest-capability use cases.

How to Reduce GPT-6 Sol API Costs

Several strategies can help control costs.

Use Prompt Caching

Repeated context can be cheaper when cached.

Route Simple Tasks to Luna

Do not use Sol for tasks that a much cheaper model handles reliably.

Use Batch Processing

Batch processing costs 50% of Standard rates.

Avoid Unnecessary Long Context

Crossing 272K input tokens causes the entire request to use higher long-context rates.

Reduce Output Length

Output tokens cost more than input tokens.

Generate only what the application actually needs.

Measure With Evals

Model decisions should be based on tested quality rather than assumptions about model names.

GPT-6 Sol Advantages

The major advantages include:

• Strong reasoning for demanding work
• 1.05M context window
• 128K maximum output
• Broad tool support
• Advanced coding use cases
• Computer use
• Web and file search
• Code interpreter
• Hosted shell
• Structured outputs
• Image input
• Agentic workflow support
• Lower pricing than GPT-6 Astra

GPT-6 Sol Limitations

Important limitations include:

• More expensive than GPT-6 Luna
• Long-context requests cost more
• AI-generated information can still be wrong
• Free API usage is not supported
• Fine-tuning is not currently supported
• Audio and video are not direct supported input modalities
• Current events may require web search because of the April 20, 2026 knowledge cutoff

Complex agent workflows also require permission controls, monitoring and human oversight where mistakes could have significant consequences.

Frequently Asked Questions About GPT-6 Sol

What is GPT-6 Sol?

GPT-6 Sol is an OpenAI reasoning model designed for complex coding and agentic workflows.

When was GPT-6 Sol released?

GPT-6 Sol was released on September 22, 2026.

How much does GPT-6 Sol cost?

Standard short-context API pricing is currently $2 per million input tokens and $10 per million output tokens.

What is GPT-6 Sol’s context window?

GPT-6 Sol has a 1,050,000-token context window.

What is GPT-6 Sol’s maximum output?

The maximum output is 128,000 tokens.

What is the GPT-6 Sol API model name?

The model identifier is:

gpt-6-sol

Is GPT-6 Sol good for coding?

Yes. OpenAI specifically positions GPT-6 Sol for complex coding and agentic workflows.

Does GPT-6 Sol support reasoning?

Yes. Reasoning-effort options include none, low, medium, high, xhigh and max. Medium is the default.

Does GPT-6 Sol support images?

Yes. GPT-6 Sol accepts image input as well as text.

Does GPT-6 Sol support web search?

Yes. Web search is a supported Responses API tool.

Does GPT-6 Sol support file search?

Yes. File search is supported.

Does GPT-6 Sol support computer use?

Yes. Computer use is listed among its supported tools.

Does GPT-6 Sol support Code Interpreter?

Yes. OpenAI lists Code Interpreter as a supported tool.

Does GPT-6 Sol support image generation?

Image generation is available as a tool through supported Responses API workflows.

Is GPT-6 Sol cheaper than GPT-6 Astra?

Yes. Standard short-context Sol pricing is $2 input and $10 output per million tokens, compared with $10 and $50 for Astra.

Is GPT-6 Sol more expensive than GPT-6 Luna?

Yes. Luna costs substantially less and is positioned for focused high-volume work.

Is GPT-6 Sol available in ChatGPT?

GPT-6 Sol is available in ChatGPT Work and Codex for eligible users. OpenAI states that these models are separate from the models used in regular Chat.

Is GPT-6 Sol available through Amazon Bedrock?

Yes. OpenAI documents GPT-6 Sol availability through Amazon Bedrock in supported regions.

What is the GPT-6 Sol knowledge cutoff?

OpenAI lists the knowledge cutoff as April 20, 2026.

Does GPT-6 Sol support fine-tuning?

OpenAI currently lists fine-tuning as not supported for GPT-6 Sol.

GPT-6 Sol: The Bottom Line

GPT-6 Sol is OpenAI’s GPT-6 model for developers who need strong reasoning without paying GPT-6 Astra prices for every request.

Its major specifications include:

• 1,050,000-token context window
• 128,000-token maximum output
• $2 per million standard short-context input tokens
• $10 per million standard output tokens
• Text and image input
• Structured outputs
• Function calling
• Web search
• File search
• Code interpreter
• Hosted shell
• Computer use
• MCP and other agent tools

Its clearest use cases are complex coding, sophisticated AI agents, research and tool-heavy professional workflows.

But not every request needs Sol.

For high-volume, predictable work, GPT-6 Luna can be dramatically cheaper.

If you are choosing between the two models, read our complete GPT-6 Luna vs GPT-6 Sol comparison.

For businesses building autonomous workflows, continue with AI agents for small business.

For customer automation, see AI customer service agents for small business.

For phone-based agents, read AI receptionist for small business.

And if you are comparing OpenAI with another current frontier coding model, see our Claude Opus 5.5 guide.

The core model-selection principle is simple:

Use Luna when efficiency matters most. Use Sol when the task needs stronger reasoning. Use Astra when maximum capability justifies the additional cost.