OpenAI flagship model
GPT-5.6 Sol
OpenAI's flagship GPT-5.6 model for complex professional work, long-context reasoning, and tool-driven production workflows.
- Context window
- 1.05M
- tokens
- Max output
- 128K
- tokens
- Input
- $4.00
- per 1M tokens
- Cached input
- $0.40
- per 1M tokens
- Output
- $20.00
- per 1M tokens
01 / Overview
What GPT-5.6 Sol Is
GPT-5.6 Sol is OpenAI's flagship model in the GPT-5.6 family, intended for complex professional work where model capability is a primary requirement rather than a secondary optimization.
The Default Flagship Tier
Sol roughly corresponds to the unsuffixed model tier used in earlier GPT-5 families. OpenAI also routes the gpt-5.6 alias to GPT-5.6 Sol, making it the default flagship representation of the GPT-5.6 generation.
The model is designed for workloads that combine reasoning, long instructions, large context, and tool use. Those characteristics make it relevant for tasks such as complex analysis, software and technical work, multi-stage research, agentic application flows, and professional workflows where a weaker response can create more downstream work than the token savings are worth.
Sol should therefore be evaluated around the value of a successful result, not only its per-token price. If stronger reasoning reduces retries, human correction, or workflow failures, the effective cost difference between model tiers may be smaller than the raw API rates suggest.
- Flagship GPT-5.6 tier for complex professional work.
- The
gpt-5.6alias routes togpt-5.6-sol. - Supports configurable reasoning and long-context workloads.
- Designed for sophisticated tool-driven and multi-step application flows.
- Provider
- OpenAI
- Family
- GPT-5.6
- Tier
- Flagship
- Knowledge cutoff
- Feb 16, 2026
- Input modalities
- Text, Image
- Output modality
- Text
- Default reasoning
- Medium
02 / Use cases
Where GPT-5.6 Sol Makes Sense
Sol is most relevant when the cost of a weak answer is higher than the additional inference cost of using the flagship model.
Use the Stronger Model Where Complexity Accumulates
Complexity in production rarely comes from one difficult instruction. It often accumulates across system rules, retrieved evidence, prior conversation state, tool results, output constraints, and several decisions that must remain consistent with one another.
This is where a flagship model can be valuable. Sol can be evaluated for workflows in which the model must interpret a large amount of information, plan or reason across several steps, call tools, and still produce a controlled output that downstream software can use.
A practical model-selection strategy is not to send every request to the most capable model. Instead, identify the parts of the product where failure, rework, or poor judgment is expensive, and test whether Sol produces a measurable quality advantage there.
- Complex professional analysis with multiple constraints.
- Coding, technical planning, and software-engineering workflows.
- Agentic systems that coordinate tools and intermediate results.
- Research and document-heavy tasks that require synthesis across large context.
- 01
Complex analysis
Synthesize multiple sources, constraints, and objectives into a coherent professional result.
- 02
Technical work
Handle architecture, coding, debugging, migration planning, and other multi-step engineering tasks.
- 03
Agentic workflows
Coordinate function calls, search, code execution, files, and other tools across several steps.
- 04
High-value decisions
Support workflows where poor reasoning creates significant review, correction, or downstream operational cost.
03 / Pricing
GPT-5.6 Sol Pricing
GPT-5.6 Sol currently costs $4.00 per million input tokens, $0.40 per million cached input tokens, and $20.00 per million output tokens under OpenAI's published text-token pricing.
Evaluate Cost Per Successful Task
Sol is more expensive than the lower GPT-5.6 tiers, so the most useful economic metric is often not cost per request but cost per successful task.
A cheaper model may be preferable when the task is routine and highly repeatable. For complex work, however, retries, manual correction, failed tool sequences, or incomplete outputs can outweigh the initial token savings. Measuring those outcomes gives a more realistic picture of whether Sol's higher price is justified.
Output usage deserves particular attention because generated tokens carry the highest standard rate. Long plans, reports, code, or reasoning-heavy responses can make output cost a meaningful portion of total inference spend.
- $4.00 per 1M standard input tokens.
- $0.40 per 1M cached input tokens.
- $20.00 per 1M output tokens.
- Tool-specific charges may apply separately depending on the tools used.
1M tokens · USD
- Input
- $4.00
- Cached input
- $0.40
- Output
- $20.00
Example: 10K input + 2K output
- Input cost
- $0.0400
- Output cost
- $0.0400
- Estimated total
- $0.0800
04 / Context
A 1.05M-Token Window for Large Working Sets
GPT-5.6 Sol supports a 1,050,000-token context window with up to 128,000 output tokens, giving complex workflows room for extensive instructions, documents, history, and tool results.
Large Context Enables Richer Workflows, Not Free Storage
The value of a million-token window is not simply that an application can send more text. It gives developers more flexibility in how they construct a working set for tasks that genuinely depend on large amounts of information.
A professional workflow might combine a system policy, several source documents, a project history, retrieved records, and intermediate tool output. Sol can receive these elements together, reducing the need to aggressively compress every workflow into a small prompt.
But context quality remains critical. Sending irrelevant or duplicated material can increase cost and distract from the information that actually determines the answer. The best implementation should track what occupies the context window and test whether each category of context improves the outcome.
- 1,050,000-token total context window.
- Maximum output of 128,000 tokens.
- Suitable for large technical, research, and document-based working sets.
- Requests above 272K input tokens use OpenAI's higher long-context pricing.
Context window
1,050,000
Max output
128,000
A production context may contain system instructions, project history, retrieved documents, tool results, user input, and other information required to complete the task.
05 / Reasoning
Tune Reasoning to the Difficulty of the Work
GPT-5.6 Sol supports six reasoning-effort levels, so applications can vary the amount of reasoning used instead of paying the same computational cost for every task.
Flagship Capability Still Needs Configuration
Sol supports none, low, medium, high, xhigh, and max, with medium as the default. This range matters because even a flagship model may not need deep reasoning for routine operations.
For a straightforward transformation or lookup-oriented task, lower reasoning can be a useful latency and cost baseline. For architecture decisions, complex debugging, multi-step analysis, or agentic workflows with branching tool use, higher effort may improve reliability.
The useful question is not "What is the strongest setting?" but "At what reasoning level does this workload reach the quality threshold we need?" EidoStack evaluations can compare the same prompt set across effort levels to make that tradeoff visible.
Professional routine
Well-defined transformations, summaries, and structured tasks can establish a lower-effort baseline.
Complex professional work
Architecture, difficult debugging, research synthesis, planning, and agentic execution are candidates for higher reasoning effort.
06 / Capabilities
A Broad Toolset for Agentic Applications
GPT-5.6 Sol supports text and image input, streaming, function calling, structured outputs, and a broad range of Responses API tools for building action-oriented workflows.
Capability Beyond Prompt and Response
Sol can participate in workflows that do more than generate text. Function calling lets an application expose controlled actions. Structured outputs allow downstream systems to receive predictable data. Responses API tools can extend a run with search, file retrieval, code execution, shell operations, computer use, MCP integrations, and other supported capabilities.
OpenAI's current model page also lists image generation, Apply Patch, Skills, and Tool Search among supported Responses API tools. This breadth is particularly relevant for agentic systems where the model must decide what action to take, inspect the result, and continue toward a larger objective.
Fine-tuning is currently not supported for GPT-5.6 Sol, so adaptation should be designed around prompting, context, tools, retrieval, and application-level orchestration.
- Text input and output with image input.
- Streaming, function calling, and structured outputs.
- Web search and file search.
- Image generation and code interpreter.
- Hosted shell, Apply Patch, Skills, computer use, MCP, and Tool Search.
- Fine-tuning is currently not supported.
- Supported
Text and vision
Accept text and image input and generate text output.
- Supported
Structured integration
Use function calling and structured outputs for application-controlled workflows.
- Supported
Search and files
Use web search and file search through the Responses API.
- Supported
Code and shell
Use code interpreter, hosted shell, and Apply Patch where appropriate.
- Supported
Agentic tools
Use computer use, MCP, Skills, Tool Search, and other supported workflow tools.
- Supported
Image generation
Access the supported image-generation tool through the Responses API.
07 / Evaluation
Strengths and Limitations
GPT-5.6 Sol is designed for capability-first professional workloads, but using the flagship tier everywhere is not automatically the most efficient architecture.
Strengths
Flagship capability
Sol is the top GPT-5.6 tier for complex professional work and is the target of the unsuffixed gpt-5.6 alias.
Complex workflow fit
Strong reasoning, large context, structured outputs, and tool support make it suitable for multi-stage application workflows.
Large working context
A 1.05M-token context window provides substantial room for documents, project state, retrieved evidence, and tool results.
Broad Responses API support
Search, files, code execution, shell, computer use, MCP, image generation, and additional tools can participate in a workflow.
What to consider
Higher inference cost
Sol is a capability-first tier, so simpler high-volume workloads may be more economical on Terra or Luna.
Long-context multipliers
Input above 272K tokens triggers higher pricing for the entire request, which can materially affect large-context workloads.
Current pricing is promotional
OpenAI states that the published Sol pricing is promotional and available at least through November 21, 2026.
Fine-tuning is unavailable
The current OpenAI model documentation lists fine-tuning as unsupported for GPT-5.6 Sol.
Evaluate flagship capability
Test GPT-5.6 Sol on the work that actually matters
Use representative production prompts, real context, and your intended reasoning settings to measure response quality, token usage, cost, and context consumption before choosing Sol for an application.
Start FreeConnect your own provider API key and evaluate GPT-5.6 Sol under the same conditions your production workflow will use.
Common Questions
What is GPT-5.6 Sol?
GPT-5.6 Sol is OpenAI's flagship model in the GPT-5.6 family for complex professional work. It is the capability-focused tier above GPT-5.6 Terra and GPT-5.6 Luna.
Is GPT-5.6 Sol the same as GPT-5.6?
OpenAI states that the gpt-5.6 alias routes requests to GPT-5.6 Sol. Sol roughly corresponds to the unsuffixed flagship tier used in earlier GPT-5 families.
How much does GPT-5.6 Sol cost?
Current published pricing is $4.00 per 1M input tokens, $0.40 per 1M cached input tokens, and $20.00 per 1M output tokens. OpenAI states that this pricing is promotional and available at least through November 21, 2026.
What is the context window of GPT-5.6 Sol?
GPT-5.6 Sol has a 1,050,000-token context window and supports up to 128,000 output tokens.
What reasoning levels does GPT-5.6 Sol support?
GPT-5.6 Sol supports none, low, medium, high, xhigh, and max reasoning effort. Medium is the default.
Does GPT-5.6 Sol support images?
Yes. The model accepts image input in addition to text input and produces text output. Audio and video are not supported as direct model modalities.
What tools does GPT-5.6 Sol support?
OpenAI currently 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.
What workloads are a good fit for GPT-5.6 Sol?
Sol is a strong candidate for complex professional analysis, software-engineering tasks, research synthesis, agentic workflows, long-context document work, and other high-value tasks where stronger model capability can reduce retries or human correction.
Can GPT-5.6 Sol be fine-tuned?
No. The current OpenAI model documentation lists fine-tuning as unsupported for GPT-5.6 Sol.
Model information
Last updated
The specifications and prices on this page are based on the official OpenAI documentation for GPT-5.6 Sol. Current Sol pricing is promotional and OpenAI states that it is available at least through November 21, 2026. Provider pricing, limits, tools, and availability may change and should be rechecked before production decisions.