Persistent Context
Keep project files, instructions, previous outputs, and supporting evidence together instead of rebuilding the context for every conversation.
Frontier AI Model
Try GPT‑5.6 in a Cost‑Aware AI Agent Workspace
Use GPT-5.6 for complex research, coding, tool-driven execution, polished business documents, and long-running knowledge work inside Buda’s cloud-native AI workspace.
1.05M Context Window
Up to 128K Output Tokens
Sol · Terra · Luna
Tool-Ready Reasoning
GPT-5.6 is OpenAI’s latest frontier model family. It includes GPT-5.6 Sol for the most demanding professional work, GPT-5.6 Terra for balanced everyday intelligence, and GPT-5.6 Luna for fast, cost-sensitive workloads. All three models support a 1.05-million-token context window and up to 128,000 output tokens.
Inside Buda, GPT-5.6 becomes more than a model you prompt once. You can use it within persistent workspaces, multi-step agent workflows, document-based context, reusable processes, and human-reviewed execution.
Models
Available
Sol
Frontier
Terra
Balanced
Luna
Fast
Context
1.05M
What Do GPT-5.6 Benchmarks Show?
GPT-5.6 is designed to produce more useful work from every token. OpenAI reports gains across long-running professional workflows, coding agents, browsing, computer use, cybersecurity, science, and artifact creation.
The practical value is not simply a better answer to one prompt. GPT-5.6 is built to stay focused across longer workflows, use tools more efficiently, inspect intermediate results, revise its approach, and produce work that is closer to ready for use.
| Benchmark or capability | OpenAI-reported result | Why it matters for Buda users |
|---|---|---|
| Agents’ Last Exam | GPT-5.6 Sol scored 52.7%, compared with 46.9% for GPT-5.5 and 40.5% for Claude Fable 5 | Stronger performance on long-running professional workflows involving research, analysis, tools, and multiple decisions |
| Artificial Analysis Coding Agent Index | GPT-5.6 Sol scored 80, compared with 76.4 for GPT-5.5 | Useful for coding agents, implementation work, debugging, and repository-level engineering tasks |
| BrowseComp | GPT-5.6 Sol scored 90.4%, reaching 92.2% with the tested multi-agent configuration | Supports deeper web research, source collection, competitive analysis, and evidence-based reporting |
| OSWorld 2.0 | GPT-5.6 Sol scored 62.6%, compared with 47.5% for GPT-5.5 | Indicates stronger ability to operate software interfaces and complete computer-based workflows |
| Terminal-Bench 2.1 | GPT-5.6 Sol scored 88.8%, reaching 91.9% in the tested multi-agent configuration | Relevant for command-line work, technical operations, testing, and long-horizon software tasks |
| Knowledge-work outputs | OpenAI reports improvements in presentations, documents, spreadsheets, financial models, and template adherence | Helps agents produce more polished and reusable business deliverables |
Why Use GPT-5.6 in Buda?
Access to a strong model is only one part of an effective AI workflow.
Complex work usually involves more than asking a question. The model may need to read source files, preserve context, search for information, use tools, create an artifact, check the result, and pass important decisions to a human.
Buda gives GPT-5.6 a workspace in which that work can continue.
Multi-Step Agent Workflows
Use GPT-5.6 as part of a longer process that can research, analyze, draft, review, and prepare the next action.
Model-Aware Execution
Choose a faster model for repeated work and bring in a stronger GPT-5.6 tier when the workflow reaches a difficult or high-impact step.
Reusable Workflows
Turn successful instructions and processes into reusable agent Skills rather than repeatedly creating prompts from scratch.
Human Review
Let agents prepare the work while humans retain control over approvals, customer-facing outputs, financial decisions, and production changes.
Context
Reasoning
Tools
Validation
Human Review
Approved Output
Use GPT-5.6 When the Workflow Must Finish, Not Just Answer
End-to-End Research
Use GPT-5.6 when an agent needs to search across multiple sources, compare evidence, resolve contradictions, and turn the findings into a clear deliverable.
Best for- Market research
- Competitor analysis
- Customer research synthesis
- Technical investigation
- Evidence-based reports
Research
Running
Sources
Evidence
Report
Coding and Technical Operations
Use GPT-5.6 for workflows that extend beyond generating a code snippet. It can support planning, implementation, debugging, testing, validation, and technical documentation.
Best for- Repository-level analysis
- Architecture review
- Code migration planning
- Debugging and testing
- Production-readiness checks
Repo
Passing
Diff
Tests
Risk
Documents, Presentations, and Spreadsheets
GPT-5.6 is designed to produce more polished knowledge-work outputs and follow reference formats more accurately.
Best for- Executive presentations
- Financial models
- Research reports
- Strategy documents
- Structured spreadsheets
- Template-based deliverables
Deck
Draft
Outline
Model
Template
GPT-5.6 Is a Model Family, Not One Fixed Level of Cost
GPT-5.6 includes three model tiers. This makes it possible to match model capability to the importance, frequency, and complexity of each task.
All three models support text and image input, reasoning controls, function calling, web search, file search, and computer-use capabilities through supported OpenAI interfaces.
| GPT-5.6 model | Positioning | Best use |
|---|---|---|
| GPT-5.6 Sol | Flagship frontier model | Complex professional work, difficult coding, deep research, computer use, design, and high-impact decisions |
| GPT-5.6 Terra | Balanced intelligence and cost | Everyday agent work, document analysis, structured drafting, routine research, and workflow execution |
| GPT-5.6 Luna | Fastest and most affordable tier | High-volume classification, extraction, summaries, triage, and repeated low-risk tasks |
How Buda Routes Work to the Right Model
Buda is not just another chatbot interface. It is a cloud-native AI workspace built around persistent context, multi-step workflows, reusable agent processes, and human-supervised execution.
Instead of using the most expensive model for every action, structure the workflow in layers.
| Workflow layer | Recommended model type | Why |
|---|---|---|
| Intake and triage | GPT-5.6 Luna or another fast model | Suitable for classification, extraction, routing, and repeated lightweight work |
| Everyday agent execution | GPT-5.6 Terra | Balances reasoning quality, speed, and cost for routine professional workflows |
| Complex analysis | GPT-5.6 Sol | Better suited to difficult research, coding, planning, and multi-source reasoning |
| Final review and judgment | GPT-5.6 Sol with higher reasoning | Use when missing an issue or producing a weak conclusion would be expensive |
| Human approval | Human reviewer | Required for high-impact decisions, external publishing, financial actions, and production changes |
A research agent, for example, might use a faster model to organize sources, Terra to summarize and structure the evidence, and Sol to resolve contradictions and write the final recommendation.
The strongest model does not need to perform every step. It should perform the steps where additional intelligence creates the most value.
When Should You Use GPT-5.6 Sol, Terra, or Luna?
| Task | Use Luna | Use Terra | Use Sol |
|---|---|---|---|
| Classify incoming files | |||
| Extract structured information | |||
| Summarize routine documents | |||
| Draft standard internal content | |||
| Conduct multi-source research | |||
| Analyze a large codebase | |||
| Build or review a production interface | |||
| Create an executive-ready deliverable | |||
| Resolve conflicting evidence | |||
| Review a high-impact decision | |||
| Run repeated high-volume agent steps |
Use Luna when throughput and cost matter most.
Use Terra when you need dependable intelligence across everyday professional work.
Use Sol when the workflow requires deeper reasoning, stronger tool use, more polished output, or higher confidence before a human makes a decision.
GPT-5.6 vs GPT-5.5: What Changed?
GPT-5.6 is designed to improve both capability and efficiency rather than increasing intelligence through token usage alone.
| Area | GPT-5.5 | GPT-5.6 |
|---|---|---|
| Model structure | Primarily a flagship model tier | Sol, Terra, and Luna tiers for different workload levels |
| Context window | Up to approximately 1 million API tokens | 1.05 million tokens across Sol, Terra, and Luna |
| Maximum output | Model-dependent | Up to 128,000 tokens |
| Tool orchestration | Supports tool-based workflows | Adds stronger programmatic tool use and multi-agent options through supported APIs |
| Coding | Strong frontier coding model | Higher reported coding-agent and terminal-workflow performance |
| Knowledge work | Supports professional documents and analysis | Improved presentation, document, spreadsheet, and template-following quality |
| Design | Capable frontend generation | Stronger visual judgment, rendered-result inspection, and interface refinement |
| Cost choices | Fewer capability tiers | Three tiers for frontier, balanced, and high-volume work |
GPT-5.6 and Claude Fable 5: Which Should You Use?
GPT-5.6 and Claude Fable 5 are both frontier models intended for demanding professional work. Neither model is universally better for every workflow.
OpenAI’s published evaluations show GPT-5.6 Sol ahead of Fable 5 on Agents’ Last Exam, the Artificial Analysis Coding Agent Index, BrowseComp, and several computer-use and terminal evaluations. OpenAI also emphasizes GPT-5.6’s lower estimated cost, faster completion, and lower token usage on selected tests. Fable 5 remains competitive or stronger on some other evaluations.
| Choose GPT-5.6 when you prioritize | Choose Fable 5 when you prioritize |
|---|---|
| Tool-heavy agent workflows | Claude-based workflows already tested on Fable |
| Coding and computer use | Anthropic-specific model behavior |
| Frontend and interface generation | Existing long-context Claude prompt stacks |
| Presentations, documents, and spreadsheets | Workflows standardized around Anthropic models |
| Multiple cost and capability tiers | A single premium reasoning layer |
| Programmatic tool orchestration | Compatibility with an established Claude workflow |
GPT-5.6 on Buda: Comparison Table
| Category | GPT-5.6 on Buda |
|---|---|
| Positioning | Frontier model family for professional AI agent workflows |
| Available tiers | Sol, Terra, and Luna, subject to workspace availability |
| Context window | Up to 1.05 million tokens |
| Maximum output | Up to 128,000 tokens |
| Best use | Research, coding, tool-driven execution, knowledge work, design, and complex decisions |
| Not best for | Using the highest-capability tier for every lightweight or repeated task |
| Buda workflow role | Reasoning and execution layer inside a persistent, model-aware AI workspace |
| Human role | Set direction, review evidence, approve consequential actions, and decide what ships |
| Cost logic | Use Luna for volume, Terra for balanced work, and Sol for the hardest steps |
| Key advantage | Match intelligence and cost to each stage of the workflow |
FAQ
GPT-5.6 is OpenAI’s frontier model family released on July 9, 2026. It includes GPT-5.6 Sol, Terra, and Luna, which provide different balances of intelligence, speed, and cost.
Sol is the flagship model for the most complex professional work. Terra balances intelligence and cost for everyday tasks. Luna is the fastest and most affordable option for high-volume or cost-sensitive workloads.
GPT-5.6 is best for complex coding, multi-source research, tool-driven agent workflows, computer use, frontend creation, business documents, presentations, spreadsheets, scientific analysis, and long-running professional tasks.
GPT-5.6 Sol, Terra, and Luna each support a 1.05-million-token context window through the OpenAI API. They can produce up to 128,000 output tokens.
OpenAI lists standard API pricing at $5 per million input tokens and $30 per million output tokens for Sol, $2.50 and $15 for Terra, and $1 and $6 for Luna. Long-context requests may use higher pricing. Buda plans and credit consumption may differ from direct API rates.
No. Sol should be used when deeper intelligence materially improves the outcome. Terra or Luna may be more efficient for extraction, classification, summaries, standard drafts, and repeated agent steps.
OpenAI reports stronger GPT-5.6 performance across multiple coding, browsing, computer-use, knowledge-work, and agentic evaluations. GPT-5.6 also introduces Sol, Terra, and Luna tiers, making it easier to balance capability and cost.
It depends on the workflow. OpenAI’s published results show GPT-5.6 Sol ahead on several agentic, coding, browsing, and computer-use benchmarks, often with lower estimated cost or latency. Fable 5 remains strong and may perform better on other evaluations or established Claude workflows.
Use GPT-5.6 Where Better Intelligence Creates Real Value
Give routine work to faster models. Use GPT-5.6 Terra for balanced everyday execution. Bring in GPT-5.6 Sol when the workflow reaches complex research, coding, tool use, polished deliverables, or an expensive decision.
Buda turns GPT-5.6 from a standalone model into part of a persistent, cost-aware, human-supervised AI agent workspace.