OpenAI Advanced reasoning & agents
GPT-6 Sol
Spend more compute when the task has a long chain of consequences.
GPT-6 Sol is OpenAI's workhorse route for complex coding, long-horizon planning and multi-step agent workflows. TokenHot lists text and image input, a 1.05M context value, reasoning, function calling and structured output. Choose it when accepted quality and execution stability matter more than the lowest per-call cost.
- Reasoning
- 1.05M context
- Agent workflows
The model can plan and return tool arguments, but it does not own your shell, browser, files or permissions. Compare Sol with Luna on the same acceptance set before turning a positioning distinction into a routing rule.
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USD /1M tokens
Pricing
| Group | Input | Output | Cache Read | Cache Write |
|---|---|---|---|---|
| default | $2 | $10 | $0.2 | $2.5 |
| openai-0.2 | $0.4 | $2 | $0.04 | $0.5 |
Public list prices are shown in USD. Final charges may vary by account group and usage tier.
Overview
What is GPT-6 Sol?
Use GPT-6 Sol for tasks with multiple dependencies: a repository change that must pass tests, a long research packet with conflicts, or an agent run that needs several validated tool calls.
The catalog places Sol and Luna in the same family with different cost and quality trade-offs. Keep the task definition, model ID and validation record together so the decision remains measurable.
Maintain the chain
Keep the issue, code, tests and constraints in one task record. Ask for a checkpoint after each risky change.
Make dependencies explicit
Require a plan that names prerequisites, decisions and rollback points before tools are called.
Return artifacts, not confidence
Ask for links, diffs, logs or structured outcomes. A fluent explanation is not evidence that a tool action happened.
From brief to deliverable
Put GPT-6 Sol to work
Engineering leads
Plan a project-level change
Give the model the product requirement, current architecture and acceptance tests. Ask for a staged plan that keeps risky migrations reversible.
- Tools you need
- Repository access, issue tracker and test runner when executing.
- Keep in mind
- A long plan must still be validated step by step; do not merge from a model summary alone.
View a task brief
Use the supplied a specification, repository map, constraints, test commands and release window. to plan plan a project-level change. Return a staged implementation plan with checkpoints and rollback triggers., state the limits of the evidence and identify the checks required before accepting the result. Do not claim that an output or interaction was verified without direct evidence.
Research, strategy and operations teams
Reconcile a large evidence pack
Use dated documents and explicit comparison dimensions to produce a decision memo that retains conflicts and unknowns.
- Tools you need
- Document retrieval and a human reviewer for consequential decisions.
- Keep in mind
- The model can organize evidence but cannot establish that a source is authentic or current.
View a task brief
Use the supplied named source files, dates, decision criteria and required memo sections. to plan reconcile a large evidence pack. Return a source-linked recommendation with open questions., state the limits of the evidence and identify the checks required before accepting the result. Do not claim that an output or interaction was verified without direct evidence.
Platform and agent teams
Coordinate a multi-step tool run
Ask for a machine-readable plan, bounded tool arguments and a final artifact contract. Execute only after schemas and permissions pass.
- Tools you need
- An orchestrator, schema validator, permissions service and logs.
- Keep in mind
- The application owns execution, side effects and retries; the model cannot confirm work it did not perform.
View a task brief
Use the supplied task request, tool schemas, permissions, stop states and output contract. to plan coordinate a multi-step tool run. Return an auditable plan and a validated run record., state the limits of the evidence and identify the checks required before accepting the result. Do not claim that an output or interaction was verified without direct evidence.
Capabilities in context
Key features
| Feature | What you get | Why it matters |
|---|---|---|
| Context capacity | 1.05M tokens shown in the catalog | Use the window for project context, but set a deliberate output budget and retrieval boundary. |
| Modalities | Text and image input; text output | Analyze visual evidence while keeping generated media on a separate route. |
| Agent controls | Reasoning, function calling and structured output are listed | Build validation and permission gates around tool calls instead of treating a capability flag as execution. |
| Cost position | Higher cost than GPT-6 Luna in the catalog narrative | Use Sol where accepted quality or stability outweighs the cost of routing every request here. |
| Version identity | TokenHot model ID: gpt-6-sol | Pin the route in logs and compare against a fixed fixture set after updates. |
Route capabilities and model identity come from TokenHot's current catalog. Sol/Luna positioning is a task-selection hypothesis that should be measured on your workload.
Audience & fit
Where GPT-6 Sol fits
Teams shipping complex software
Use Sol for project-level changes where the cost of missed dependencies or repeated repair is high.
Knowledge teams with long evidence packs
Require citations, conflict handling and an explicit recommendation boundary before decisions are reviewed.
Agent platform builders
Use structured contracts and observability. Keep side effects behind your application's permissions and approval model.
Cost context
Plan the cost of a useful result
Price the whole run
Include retries, tool calls, schema validation and human review. The accepted artifact is the unit that matters.
Compare on a fixture set
Run representative hard and easy tasks with the same context, tools and acceptance criteria before changing the default route.
Keep output budgets explicit
A large context does not require a large answer. Set an output limit that matches the artifact and track truncation or refusal states.
Use the current pricing table for available rates and account groups. Model IDs and prices come from TokenHot’s catalog; provider pricing and subscriptions are separate.
API
Code examples
curl --location --request POST 'https://api.tokenhot.ai/v1/chat/completions' \
--header 'Authorization: Bearer <token>' \
--header 'Content-Type: application/json' \
--data-raw '{
"model": "gpt-6-sol",
"messages": [
{
"role": "user",
"content": "What model are you"
}
]
}'FAQ
Frequently asked questions
What is GPT-6 Sol best suited for?
GPT-6 Sol is suited to the input, output, and capability types listed on this page. Test production workloads before choosing it for a critical system.
How is GPT-6 Sol priced?
Pricing is shown in the pricing section above. Actual cost depends on usage volume, account group, and the request payload.
How can I call GPT-6 Sol?
Use the model ID shown above with one of the supported API protocols. Code examples are provided when usage data is available.
What is GPT-6 Sol best at?
It is positioned in the catalog for complex coding and agentic workflows. Confirm the benefit on representative tasks rather than routing every request by model name.
Does Sol execute tools or code?
Your application must provide and authorize tools. The model can return plans or function arguments, while your system performs and records the action.
How should I choose Sol versus Luna?
Compare accepted quality, correction rate, latency and total cost on the same tasks. Sol is a candidate for depth; Luna is a candidate for high-volume efficiency.
Can I rely on the 1.05M context for any request?
It is the current catalog context value. Effective endpoint and output limits can reduce the usable budget, so verify them in your request path.
Get started
Build with GPT-6 Sol
API