New GPT‑5.6 Series is here – and it has some big improvements to show
Artificial intelligence is moving beyond the idea of a single “best” model. Different tasks require different balances of intelligence, speed, reliability and cost.
July 22, 2026
Reading time: appx. 10 minutes
That is the thinking behind OpenAI’s new GPT‑5.6 family, now coming to Pixic PRO users from 17 July 2026.
The series includes three models:
- GPT‑5.6 Sol — maximum intelligence and reasoning
- GPT‑5.6 Terra — balanced performance for everyday professional work
- GPT‑5.6 Luna — speed and cost efficiency at scale
Together, the models are designed to support everything from demanding software development and scientific research to fast summarisation and high-volume automation.
What makes GPT‑5.6 different?
OpenAI describes GPT‑5.6 as a major step forward in both capability and efficiency. The family is designed to perform strongly across:
- Coding and software engineering
- Knowledge work and business analysis
- Scientific and life-sciences research
- Cybersecurity
- Web research
- Computer-use tasks
- Design and creative direction
- Long-horizon, multi-step agentic workflows
The main difference between the three models is not simply that one is “better” than another. Each is designed for a different operating profile.
Think of them as three specialists:
- Sol is the expert brought in for the hardest problems.
- Terra is the dependable professional for daily work.
- Luna is the fast, efficient engine for volume and speed.
GPT‑5.6 Sol: the flagship model
What is GPT‑5.6 Sol?
GPT‑5.6 Sol is the most capable model in the GPT‑5.6 family. It is designed for tasks that require deeper reasoning, multiple steps, complex context and the use of tools.
Sol is especially suited to work where a quick answer is not enough. It can be used for analysing a large codebase, researching a difficult scientific question, navigating several sources or completing a complex task through multiple stages.
OpenAI describes Sol as its strongest model yet for accelerating AI research and reports leading results across coding, knowledge work, science and agentic workflows.
Where GPT‑5.6 Sol performs best
Sol is the best choice for:
Advanced coding
Use Sol for:
- Building complex applications
- Debugging difficult problems
- Refactoring large codebases
- Understanding unfamiliar repositories
- Writing and reviewing production code
- Working with terminal-based development environments
- Planning and implementing multi-file changes
Scientific and technical research
Sol is well suited to:
- Reviewing scientific literature
- Comparing competing technical explanations
- Analysing research workflows
- Exploring life-science questions
- Supporting hypothesis development
- Working through complex technical documentation
It should support researchers and professionals—not replace expert review, especially in regulated or high-stakes fields.
Complex web research
Sol performs particularly well when a task requires:
- Searching across multiple sources
- Comparing conflicting information
- Following a chain of evidence
- Extracting details from difficult pages
- Producing a structured, well-supported answer
Computer-use and agentic workflows
Sol is designed for tasks involving multiple steps, tools and decisions. Examples include:
- Completing structured research projects
- Interacting with software interfaces
- Preparing reports from several data sources
- Managing a multi-stage operational workflow
- Planning and executing technical tasks
When should users choose Sol?
Choose GPT‑5.6 Sol when:
- The task is complex or ambiguous
- The cost of an error is high
- Deep reasoning is more important than response speed
- The model must use tools or complete multiple steps
- The work involves substantial code, research or technical context
Sol is the “maximum quality” option in the GPT‑5.6 family.
GPT‑5.6 Terra: the balanced professional model
What is GPT‑5.6 Terra?
GPT‑5.6 Terra is designed to balance capability, speed and efficiency. It is intended for users who need strong reasoning and professional-quality results without always requiring the maximum level of computation provided by Sol.
Terra is a practical choice for regular business, technical and creative work. It offers a strong middle ground: more capable than a lightweight model, while generally more efficient than a flagship model.
Where GPT‑5.6 Terra performs best
Terra is well suited to:
Everyday coding
Use Terra for:
- Writing functions and scripts
- Creating prototypes
- Explaining code
- Fixing common bugs
- Generating tests
- Working with APIs
- Creating database queries
- Building internal tools
Business and knowledge workflow
Terra can help with:
- Preparing reports
- Analysing documents
- Structuring business information
- Creating presentations and briefs
- Comparing options
- Drafting project plans
- Summarising meetings
- Turning unstructured notes into clear documents
Marketing and content creation
Terra is a strong fit for:
- Blog drafts
- Campaign concepts
- Product descriptions
- Social media content
- Email sequences
- Audience research
- Content calendars
- Brand messaging
Workflow automation
Terra can support repeatable workflows such as:
- Sorting and categorising information
- Processing customer requests
- Generating standard responses
- Extracting data from documents
- Creating structured summaries
- Producing first drafts for human review
When should users choose Terra?
Choose GPT‑5.6 Terra when:
- The task requires reliable reasoning but is not extremely complex
- Users want a strong balance between quality and efficiency
- The work is repeated regularly
- The task involves business, content, coding or document workflows
- Sol’s maximum reasoning level would be unnecessary
Terra is likely to be the best default model for many everyday professional tasks.
GPT‑5.6 Luna: the fast and efficient model
What is GPT‑5.6 Luna?
GPT‑5.6 Luna is the fastest and most cost-efficient model in the family. It is designed for high-volume, well-defined tasks where quick responses and efficient processing are especially important.
Luna is not intended to replace Sol on the most difficult reasoning problems. Instead, it makes advanced AI more practical for frequent and scalable tasks.
OpenAI reports that Luna nearly matches GPT‑5.5’s peak performance at less than half of the estimated cost.
Where GPT‑5.6 Luna performs best
Luna is a strong choice for:
Text processing
Use Luna for:
- Summarisation
- Rewriting
- Proofreading
- Translation
- Formatting
- Keyword extraction
- Topic classification
- Sentiment analysis
High-volume automation
Luna is particularly useful when many tasks need to be processed quickly, such as:
- Customer-support messages
- Product reviews
- Internal documents
- Survey responses
- Knowledge-base articles
- Email categorisation
- Frequently asked questions
Fast content generation
Luna can help create:
- Short social media posts
- Headlines
- Product variations
- Meta descriptions
- Simple email drafts
- Content ideas
- Brief summaries
- Short-form marketing copy
Simple technical tasks
Luna can be used for:
- Small scripts
- Basic formulas
- Simple code explanations
- Data formatting
- Template generation
- Straightforward transformations
When should users choose Luna?
Choose GPT‑5.6 Luna when:
- The task is clearly defined
- Speed is a priority
- Large volumes of requests must be processed
- The workflow is repetitive
- The task does not require extended reasoning
- Cost efficiency is more important than maximum performance
Luna is the practical choice for fast, scalable AI assistance.
GPT‑5.6 models compared
| Model | Main strength | Best for | Recommended when |
|---|---|---|---|
| GPT‑5.6 Sol | Maximum intelligence and reasoning | Advanced coding, research, science, cybersecurity and complex agents | Quality and depth matter most |
| GPT‑5.6 Terra | Balanced capability and efficiency | Business, coding, content, analysis and automation | Users need strong everyday performance |
| GPT‑5.6 Luna | Speed and cost efficiency | Summaries, classification, simple generation and high-volume tasks | Speed and scale are the priority |
A simple way to decide:
- Use Sol to solve the hardest problems.
- Use Terra for most professional day-to-day work.
- Use Luna when the task is fast, repetitive or high-volume.
GPT‑5.6 benchmark results
Benchmarks provide useful comparisons, but they are not the whole story. Real-world performance can vary depending on the prompt, tools, reasoning level, available context and workflow design.
The figures below are based on results reported by OpenAI and referenced in the GPT‑5.6 announcement.
Coding and software engineering
Artificial Analysis Coding Agent Index
GPT‑5.6 Sol with maximum reasoning scored 80 on the Artificial Analysis Coding Agent Index.
OpenAI reported that this result placed Sol at the top of the index at launch and 2.8 points ahead of Fable 5.
The benchmark evaluates coding-agent performance across areas such as:
- Implementation
- Terminal use
- Real-world codebases
- Completing software tasks
Terminal-Bench 2.1
| Model | Result |
|---|---|
| GPT‑5.6 Sol | 88.8% |
| GPT‑5.6 Terra | 87.4% |
| GPT‑5.6 Luna | 84.7% |
| GPT‑5.6 Sol Ultra configuration | 91.9% |
These results show a clear family-wide strength in terminal-based coding and software engineering. Sol leads on maximum performance, while Terra and Luna remain competitive for less demanding development workflows.
Web research and computer use
BrowseComp
GPT‑5.6 Sol achieved 92.2% on BrowseComp, setting a new state-of-the-art result according to OpenAI.
BrowseComp is designed to test difficult browsing and research tasks that require an AI system to locate, interpret and connect information from the web.
This makes Sol particularly suitable for:
- Competitive research
- Market research
- Technical investigations
- Multi-source reporting
- Research-heavy business workflows
OSWorld 2.0
Sol achieved 62.6% on OSWorld 2.0, a benchmark focused on computer-use tasks.
OpenAI reports that Sol surpassed Opus 4.8 on this evaluation while using significantly fewer tokens in the tested configuration.
This result is relevant to workflows involving:
- Desktop applications
- Interface navigation
- Multi-step computer tasks
- Digital operations
- Tool-based automation
Agentic and professional workflows
Agents’ Last Exam
The GPT‑5.6 models were also evaluated on Agents’ Last Exam, which measures long-horizon agentic workflows across professional fields.
Reported results include:
| Model | Result |
|---|---|
| GPT‑5.6 Sol | 52.7% |
| GPT‑5.6 Terra | 50.4% |
| GPT‑5.6 Luna | 50.3% |
| GPT‑5.5 | 46.9% |
The results indicate that all three GPT‑5.6 models improve on GPT‑5.5 in this evaluation, with Sol achieving the strongest result.
These capabilities are relevant to tasks where the model must:
- Understand a broad objective
- Break it into smaller steps
- Use tools or information sources
- Maintain context
- Produce a final result after several actions
Professional knowledge work
GDPval-AA v2
GDPval-AA v2 evaluates performance on professional and knowledge-work tasks.
| Model | Result |
|---|---|
| GPT‑5.6 Sol | 1,747.8 Elo |
| GPT‑5.6 Terra | 1,593 Elo |
| GPT‑5.6 Luna | 1,591.8 Elo |
| GPT‑5.5 | 1,493.7 Elo |
Sol leads this comparison by a significant margin. Terra and Luna also exceed the GPT‑5.5 result in the reported evaluation.
This benchmark is relevant to activities such as:
- Creating professional documents
- Analysing information
- Producing structured work
- Following workplace instructions
- Solving practical knowledge tasks
Science and health
LifeSciBench
OpenAI’s reported LifeSciBench results were:
| Model | Result |
|---|---|
| GPT‑5.6 Sol | 59.9% |
| GPT‑5.6 Terra | 56.0% |
| GPT‑5.6 Luna | 51.2% |
| GPT‑5.5 | 50.4% |
MedChemBench
On an internal MedChemBench evaluation, reported results included:
| Model | Result |
|---|---|
| GPT‑5.6 Sol | 48.3% |
| GPT‑5.6 Terra | 35.0% |
| GPT‑5.6 Luna | 30.4% |
| GPT‑5.5 | 35.5% |
These results suggest that Sol is the strongest choice for demanding scientific and life-science workflows. Terra may be suitable for general technical research and structured analysis, while Luna is better reserved for simpler science-related tasks such as summarising or organising information.
Scientific outputs should always be reviewed by qualified experts before being used in research, medical or regulatory decisions.
How to choose the right GPT‑5.6 model
Choose Sol if the task is…
- Complex
- Multi-step
- Research-heavy
- Technically demanding
- Sensitive to errors
- Dependent on tools or computer use
- Related to advanced coding or science
Example: “Analyse this unfamiliar codebase, identify the architectural problems, propose a migration plan and implement the changes.”
Choose Terra if the task is…
- Professional and repeatable
- Moderately complex
- Related to business, content or coding
- Important, but not highly specialised
- A regular part of a team workflow
Example: “Review these customer interviews and prepare a concise product strategy report with key themes and recommendations.”
Choose Luna if the task is…
- Clear and well structured
- Repetitive
- Time-sensitive
- High-volume
- Mostly based on transformation or extraction
Example: “Classify 10,000 support messages by topic and create a two-sentence summary for each.”
A practical Pixic workflow
Users do not always need to choose one model permanently. A productive workflow can combine all three:
- Start with Luna to process, organise or summarise large amounts of information.
- Use Terra to develop a report, draft content or analyse the structured material.
- Bring in Sol for the most important review, complex reasoning or final strategic decision.
This approach can help teams balance quality, speed and efficiency rather than using the most powerful model for every single task.
GPT‑5.6 is coming to Pixic PRO
The new GPT‑5.6 family gives Pixic users more choice:
- Sol for the most challenging work
- Terra for balanced professional performance
- Luna for fast and efficient AI at scale
The models will be available on Pixic from 17 July 2026 for PRO users.
Ready to explore the new GPT‑5.6 models?
Visit Pixic, choose the model that matches your task and start working with the next generation of OpenAI models.
Try GPT‑5.6 on Pixic PRO from 17 July 2026.
Benchmark results are reported under specific evaluation conditions and should be treated as indicative rather than a guarantee of performance for every task.