GPT-5: Features, Benchmarks, Pricing, and Comparison
OpenAI released the GPT-5.6 model family on July 9, 2026. The family includes GPT-5.6 Sol for complex reasoning and professional workflows, GPT-5.6 Terra as a balanced option, and GPT-5.6 Luna for faster, cost-sensitive workloads.
We examine their specifications, published benchmark results, pricing, availability, and differences from previous GPT models.
GPT-5 model comparison
The three GPT-5.6 models use the same published context window and knowledge cutoff. Their main differences are cost, intended workload, and benchmark performance.
Feature | GPT-5.6 Sol | GPT-5.6 Terra | GPT-5.6 Luna |
|---|---|---|---|
Primary role | Complex professional work | Balanced capability and cost | High-volume, cost-sensitive workloads |
Earlier-tier equivalent | Standard or flagship tier | Mini tier | Nano tier |
API model ID | gpt-5.6-sol | gpt-5.6-terra | gpt-5.6-luna |
General API alias | gpt-5.6 routes to Sol | None listed | None listed |
Context window | 1,050,000 tokens | 1,050,000 tokens | 1,050,000 tokens |
Maximum output | 128,000 tokens | 128,000 tokens | 128,000 tokens |
Knowledge cutoff | February 16, 2026 | February 16, 2026 | February 16, 2026 |
Text input and output | Supported | Supported | Supported |
Image input | Supported | Supported | Supported |
Native image output | Not supported | Not supported | Not supported |
Source: OpenAI
What Is Different in GPT-5.6?
Three capability tiers
GPT-5.6 replaces the earlier standard, mini, and nano naming pattern with Sol, Terra, and Luna:
- Sol is intended for more difficult coding, research, science, computer use, and professional tasks.
- Terra balances capability and cost for everyday production workloads.
- Luna is designed for faster, higher-volume applications where cost is a major constraint.
The smaller models are not simply fallback versions. They have the same published context window and maximum output size as Sol, although their benchmark results and intended workloads differ.
Larger reasoning settings
GPT-5.6 supports different levels of reasoning effort depending on the product. In standard ChatGPT, these appear as Medium, High, and Extra High for eligible users.
In supported ChatGPT Work, Codex, and API environments, GPT-5.6 also introduces:
max: Gives the model more time and computational budget to examine alternatives, perform checks, and revise its work.ultra: Uses multiple agents working in parallel before combining their results.
ultra is not simply a higher single-model reasoning level. It is a multi-agent execution mode. OpenAI states that it uses four agents by default, which can improve some complex-task results but also increases total token consumption.
GPT-5.6 Capabilities
Coding and software engineering
GPT-5.6 can generate, review, explain, refactor, and debug code. It is also designed for agentic software-development workflows in which the model can inspect a repository, edit files, run tests, diagnose errors, and iterate on its implementation.
Possible coding uses include:
- Bug fixing
- Feature implementation
- Repository analysis
- Test generation
- Code review
- Refactoring
- Documentation
- Front-end development
- Command-line workflows
- Tool-assisted debugging
Generated changes should still be reviewed and tested. Performance depends on repository access, the execution environment, tool reliability, test coverage, and the specificity of the instructions.
Design and prototyping
GPT-5.6 can translate natural-language instructions into interface structures, front-end code, visualizations, and interactive prototypes.
When computer-use tools are available, the model may also inspect the rendered output, identify visual or functional problems, and make further adjustments.
Potential uses include:
- HTML, CSS, and JavaScript prototypes
- Interface layouts
- Interactive demonstrations
- Dashboards
- Front-end components
- Presentation layouts
- Document and spreadsheet formatting
These outputs do not remove the need for accessibility testing, browser testing, security review, or professional design evaluation.
Professional documents
In supported environments, GPT-5.6 can help create and revise:
- Presentations
- Reports
- Spreadsheets
- Financial models
- Research summaries
- Structured business documents
- Template-based deliverables
The model may use uploaded files and connected workplace sources when the relevant product, permissions, and integrations are available. GPT-5.6 does not automatically have access to Slack, Google Drive, Microsoft 365, Notion, or other platforms. Those connections must be enabled separately.
Research and knowledge work
GPT-5.6 can search, organize, compare, and summarize information when it has access to suitable search or document-retrieval tools.
Its 1.05-million-token context window allows large collections of material to be submitted in a single request. However, a large context window does not guarantee that every detail will be retrieved or interpreted correctly.
Important research findings should be checked against primary sources. This is particularly important when sources conflict, information changes quickly, or the task involves legal, medical, scientific, or financial decisions.
Computer use
GPT-5.6 supports computer-use tools in compatible Responses API environments. This can allow the model to interact with graphical interfaces, inspect applications, and complete multi-step browser or desktop workflows.
Computer-use performance depends on:
- Interface stability
- Screen resolution
- Authentication state
- Tool permissions
- Page loading behavior
- Changes to the application
- Confirmation requirements for important actions
A benchmark result does not guarantee reliable operation on every website or application.
Image understanding
GPT-5.6 Sol, Terra, and Luna accept images as input. They can analyze:
- Charts
- Screenshots
- Diagrams
- Scanned documents
- Product images
- Interface layouts
- Photographs
The models return text rather than native image output. In the Responses API, GPT-5.6 can use a separate image-generation tool when that tool is enabled.
Health and scientific questions
GPT-5.6 can summarize evidence, explain scientific concepts, and help organize health-related information. OpenAI also reports results from HealthBench Professional, GeneBench Pro, and LifeSciBench.
These results do not establish clinical reliability. GPT-5.6 should not be used as a substitute for diagnosis, emergency care, treatment decisions, or advice from a licensed professional.
GPT-5.6 Benchmark Results
The following comparison uses figures published by OpenAI for GPT-5.6 and GPT-5.5. Unless otherwise specified, higher scores are better.
Source: OpenAI GPT-5.6 evaluation tables.
The results show several trade-offs:
- Sol records the highest GPT-family score in most of the selected evaluations.
- Terra approaches Sol on several professional and coding tests at half the API token price.
- Luna exceeds GPT-5.5 on SWE-Bench Pro and AutomationBench but scores below it on Terminal-Bench 2.1, BrowseComp, MMMU Pro, Toolathlon, and both selected MRCR tests.
- GPT-5.6 Sol’s 73.8% result on MRCR v2 at 512K–1M is slightly below GPT-5.5’s 74.0%.
- Sol does not lead every cross-company comparison published by OpenAI. For example, other models record higher scores on some versions of SWE-Bench Pro, FrontierMath Tier 4, Toolathlon, and GDPval-AA v2.
These differences make it inaccurate to describe GPT-5.6 as universally superior across every task or benchmark.
GPT-5.6 API Pricing
API pricing is calculated per one million tokens.
Additional pricing rules include:
- Prompts containing more than 272,000 input tokens are billed at twice the standard input rate and 1.5 times the standard output rate for the full request.
- Cache writes cost 1.25 times the uncached input rate.
- Cache reads receive the listed 90% discount.
- Tool calls may have separate charges.
- Multi-agent and Ultra workflows can consume tokens across multiple agents.
- Rate limits depend on the developer’s API usage tier.
Add as preferred sourceLimitations
GPT-5.6 has several important limitations:
- It can produce incorrect or unsupported information.
- Larger context windows do not ensure perfect retrieval.
- Reasoning settings can increase latency and cost.
- Tool use can fail because of permissions, changing interfaces, or integration errors.
- Smaller tiers do not outperform previous models on every benchmark.
- Multi-agent execution may consume substantially more tokens.
- Cybersecurity safeguards may block some legitimate requests.
- The model may occasionally take or attempt actions beyond the user’s intended scope in agentic environments.
- It does not natively accept or produce audio through the listed API model endpoints.
- It analyzes images but does not produce native image output without a separate tool.
- OpenAI has not disclosed the model’s parameter count, complete architecture, or full training dataset.
- It is not a substitute for professional medical, legal, financial, or security judgment.
High-impact workflows should use human review, narrow permissions, confirmation steps, audit logs, and application-specific evaluations.
FAQs
It introduces real-time model routing, larger-context handling, improved multimodal reasoning, safer completion strategies, and more advanced coding capabilities. It is also designed to integrate more seamlessly with tools, APIs, and enterprise workflows.
No. It can analyze and reason about images but does not generate them directly.
Common applications include:
Complex reasoning and problem-solving
Multi-language code generation and debugging
Document summarization and research
Visual content interpretation (charts, photos, diagrams)
Customer support automation
Multi-tool and API-driven workflows
- 50+ ChatGPT Use Cases with Real Life Examples
- LLM Fine-Tuning Guide for Enterprises
- 10+ Large Language Model Examples & Benchmark
Cite this research
Pick the format that matches where you're publishing. Pasting the link version into your CMS preserves the backlink.
@misc{dilmegani2026,
author = {Dilmegani, Cem and Sezer, Sena},
title = {{GPT-5: Features, Benchmarks, Pricing, and Comparison}},
year = {2026},
month = jul,
howpublished = {\url{https://aimultiple.com/gpt-5}},
note = {AIMultiple. Retrieved July 28, 2026}
}Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
Comments 1
Share Your Thoughts
Your email address will not be published. All fields are required. Comments are left in their original language.
Hello, Is it possible to chat gpt-4 in the development of intelligent household utensils that can judge by themselves when to heat or cool food and drinks.
Hello Kiril, I think what you're referring to is asking the latest version of ChatGPT to help you develop smart utensils, which would qualify them IoT devices? In any case, we asked. And it did give us the high-level steps to follow, such as creating concept sketches, collecting the required hardware components, developing the appropriate software, etc. Hope this helps!