GPT 5.6 Sol API: Online text generation, tutorials and integration guide

What Is ChatGPT-5.6 SOL

The Sol Model in the GPT-5.6 Family for Complex Professional Work

The ChatGPT-5.6 SOL introduced here corresponds to OpenAI's official GPT-5.6 Sol. It is the flagship model in the GPT-5.6 family, officially released on July 9, 2026, alongside Terra and Luna. We position it as a general-purpose model that can combine reference materials, code, and tools to handle complex tasks, with a focus on project development, business analysis, and multi-step work. OpenAI's official release notes

A General-Purpose Model with a 1.05 Million Token Context and Image Input

The official GPT-5.6 Sol API provides a context window of 1,050,000 tokens and a maximum output of 128,000 tokens. It supports text and image input and produces text output. As a multimodal model, its image understanding can be used to read screenshots, charts, and interface references; generating images, executing code, or exporting files requires the corresponding tools and runtime environment. Official model specifications

As a multimodal model, it has many more application scenarios than a non-multimodal model, such as OCR, frontend testing, VQA, and multimodal document parsing.

Turn Thinking Off or Adjust Reasoning Effort to Match Task Difficulty

GPT-5.6 Sol supports six reasoning effort settings: none, low, medium, high, xhigh, and max. The official default is medium. Of these, none is for requests without additional reasoning, while the other levels adjust the reasoning effort the model invests in solving a problem. Official reasoning levels

We recommend starting business-scenario testing with medium: compare none or low for formatting and simple extraction; compare high for multi-file code analysis and complex reference-material processing; and, for difficult problems that require repeated checking, assess whether xhigh or max delivers sufficient benefit. Based on our experience with multiple models, xhigh and max are sometimes not the best choice.

RequestBeam's ChatGPT-5.6 SOL Online Tool and API

Through RequestBeam, we hope to help you understand how ChatGPT-5.6 SOL actually performs on your own tasks more quickly. You only need to sign in, create an API Key, and add a small amount of credit to start trying the online tool, without writing API integration code first or purchasing a fixed plan for a short-term evaluation. This also reduces the hassle of registering and managing accounts across multiple model platforms yourself.

ChatGPT-5.6 SOL Core Features and Domain Strengths

Project Programming: Understand Existing Code and Make Multi-File Changes

We think Sol is worth using for feature development, bug fixes, and refactoring in existing or new projects. One of our customers shared their experience generating a dashboard and refactoring a project of about 20,000 lines, and felt they needed to correct the model repeatedly less often.

We pay more attention to the actual tasks behind this feedback: whether the model can leave existing code outside the intended changes untouched, and whether it can handle dependencies between multiple files. When using ChatGPT-5.6 SOL for development tasks, we recommend breaking the work into small tasks to keep its context as small as possible.

Long-Running Work: Continue Migrations and Correct Problems Based on Test Feedback

We would use Sol's ability to keep working for migration and modification tasks with a clearly defined test suite. One of our customers shared that they had repeatedly used Sol Ultra to convert a Python library to Golang, reporting that the entire process took about 10+ hours and ultimately passed multiple tests of their own.

We think this case illustrates the value of sustained work, while also showing that completing a sustained task can take a long time. For language migrations, interface replacements, and batch module modifications, we recommend retaining intermediate results by module and using both existing tests and real usage scenarios for acceptance.

Webpage and Interface Generation: Turn Brief Requirements into Inspectable Interactive Prototypes

We think Sol can be used to prepare initial drafts of landing pages, product demos, and admin interfaces. A customer shared their experience using ChatGPT-5.6 SOL for frontend development. They had been using the ChatGPT series for frontend interface development and felt that the GPT-5.6 family's frontend performance had improved, although its aesthetics might still lag behind contemporary Claude models.

We think webpage and interface generation should be assessed according to the business scenario. If your main work involves admin interfaces without particularly demanding color and aesthetic requirements, ChatGPT-5.6 SOL can handle your tasks fairly well. However, if you want to create a particularly striking webpage, you will need to pair it with other models to help complete the task.

Documents and Office Deliverables: Prepare Presentations and Business Files from Reference Materials

We focus on whether Sol, when equipped with file-processing tools, can organize materials into office deliverables that remain editable. On OpenAI's release page, Model ML disclosed that it had tested Sol on 20 customer workflows and hundreds of presentations, and praised its page readability, data visualization, and reduction in rework.

For business reports, product proposals, and recurring analysis materials, we recommend providing source data, reference templates, and delivery requirements together, so the model can retain sources as it organizes the content. During acceptance, pay attention to the consistency of numbers, whether key conclusions have supporting evidence, and whether the charts and page structure are easy for readers to understand.

Writing and Content Refinement: Revise Existing Drafts over Multiple Rounds

We would more strongly recommend Sol for refining outlines, drafts, and arguments. One of our customers shared their experience using SOL to revise a short drama, discussing line by line whether dialogue changed character relationships, and checking pacing, foreshadowing, and what the audience should know at different stages. They felt that the model could still follow the overall narrative goal while discussing local details, but also noted that SOL's writing was not as creative and divergent as contemporary Claud models.

Based on our current experience with multiple models, if writing is your main focus, ChatGPT-5.6 SOL may not be your best choice. You could try Claude Opus 5, which may deliver better results.

ChatGPT-5.6 SOL Benchmarks and Performance

In this section, we retain the evaluation version, reasoning level, and runtime environment. RequestBeam's pricing; actual integration costs should be based on console prices and request usage.

Terminal Tasks: 37.88% in an Independent Terminal-Bench 4.0 Evaluation

On Vals AI's Terminal-Bench 4.0 leaderboard, GPT-5.6 Sol Max scores 37.88%, with an average cost of USD 7.98 per task. This test uses mini-swe-agent, with three independent runs on each of 66 tasks, and reports the average pass rate. On the same leaderboard, GPT-6 Astra Max scores 59.60%, with an average cost of USD 9.58 per task. Vals AI leaderboard and methodology · Sol evaluation configuration

We recommend using Sol for tasks with clear requirements. If your task calls for discussion and design, you need to switch to a model at the level of Astra or Claude Opus 5.5.

Project Programming: CursorBench 4.0 Rises from 35.7% at High to 41.7% at Max

CursorBench 4.0 uses multi-file tasks from real Cursor sessions, covering investigation, modification, and refactoring. GPT-5.6 Sol performs as follows at different reasoning levels. Original CursorBench 4.0 leaderboard

We pay more attention to the change from High to Max: the score increases by 6 percentage points, while the task cost is nearly 2.9 times the original. For everyday project development, we recommend High for ordinary tasks, and Max when there are more constraints and greater complexity. If your task is especially complex, you may need to switch to Astra.

Professional Knowledge Work: 1611 Elo on GDPval-AA v2.1

On Artificial Analysis's current comparison page, GPT-5.6 Sol Max scores 1611 Elo on GDPval-AA v2.1. This evaluation uses 220 tasks across 44 occupations and 9 industries. Models use shell and web-browsing tools to submit documents, presentations, and spreadsheets, which are then scored through anonymous pairwise comparisons. We interpret this score as a signal of deliverable quality within that evaluation. Sol's GDPval-AA score · Evaluation methodology

For teams already using Sol to prepare reports, presentations, and business spreadsheets, we recommend retaining existing acceptance samples and then comparing whether a new model reduces missing information, numerical errors, and manual rework. A model's ranking on programming leaderboards can differ from its ranking for office deliverables, so we recommend deciding whether to switch separately for each type of work product.

Cross-Application Automation: About 60% Goal Completion on AutomationBench-AA

In Artificial Analysis's current comparison, Sol Max scores about 60% on AutomationBench-AA. This measures the average proportion of task goals completed: if a task violates any business constraint specified by the evaluation, the task receives a score of zero. Therefore, 60% does not mean that six out of ten complete business workflows can already run without human intervention. Sol's automation score · AutomationBench-AA scoring explanation

We recommend starting with workflows whose results are easy to verify, such as retrieving customer information, summarizing support tickets, and generating handling suggestions. After connecting multiple business systems, we recommend checking separately whether queries are accurate, records refer to the correct entities, and the final state meets requirements. For reasoning levels, we recommend starting with high and then lowering or raising the thinking level according to the business situation.

Overall Index and Generation Speed: 47 Points and About 81 Tokens per Second

Artificial Analysis's Intelligence Index v4.3.2 records GPT-5.6 Sol Max at 47 points, with a weighted cost of about USD 1.99 per task. Its measurements of the OpenAI API show an output speed of about 81.3 tokens per second. We distinguish between output speed after generation begins, the wait before the response text appears, and the time taken to complete the entire task. Artificial Analysis model evaluation

For products that require frequent interaction, we recommend first comparing response times for Medium and High. For complex analysis that can run in the background, assess whether Max improves the quality of the final deliverable. If your business scenario is particularly time-sensitive, we recommend using models in the Luna or Sonnet series.

ChatGPT-5.6 SOL Compared with GPT-5.5, GPT-5.6 Terra, GPT-6 Astra, and Claude Opus 5.5

Compared with GPT-5.5: At the Same High Level, Sol's Overall Performance and Task Cost Merit Evaluation

In Artificial Analysis v4.3.2's comparison at the same reasoning level, Sol High and GPT-5.5 High have overall index scores of 42 and 37, weighted costs per task of USD 0.81 and USD 1.54, and GDPval-AA v2.1 scores of 1505 and 1329 Elo, respectively. Sol High versus GPT-5.5 High

Based on these results, we recommend that teams still using GPT-5.5 give priority to upgrading to ChatGPT-5.6 SOL. If your business is already running reliably, you need to find a small scenario for testing before switching.

Compared with GPT-5.6 Terra: Allocate Models and Reasoning Budgets According to Task Difficulty

At the same Max level, Sol and Terra have overall index scores of 47 and 42, weighted costs per task of USD 1.99 and USD 1.40, and GDPval-AA v2.1 scores of 1611 and 1453 Elo, respectively. However, the comparison page shows both at about 60% on AutomationBench-AA. On that basis, we consider their performance close on this automation metric. Sol Max versus Terra Max

We recommend evaluating Terra's cost for organizing fixed-format materials, batch extraction, and simple business assistants. For work that requires combining multiple materials, assessing interdependent constraints, and producing a complete report, focus on testing Sol. If the model's output does not meet your expectations, you can adjust its thinking level. If your task is quite difficult, we recommend switching to Astra.

Compared with GPT-6 Astra: Consider Upgrading for Complex Execution Tasks, but Compare Document Workflows Separately

In Artificial Analysis's comparison, Astra High and Sol Max have overall index scores of 51 and 47, weighted costs per task of USD 1.73 and USD 1.99, and AutomationBench-AA scores of about 67% and 60%, respectively. The ranking is reversed on GDPval-AA v2.1, however: Astra High scores 1485 Elo, while Sol Max scores 1611 Elo. Astra High versus Sol Max

We recommend prioritizing tests of Astra High for complex tool operations, cross-system execution, and engineering tasks that require ongoing investigation. Teams already using Sol to generate reports, presentations, and spreadsheets should continue comparing the quality of deliverables. Based on our current production experience, SOL is better suited to bounded tasks, while Astra is better suited to more open-ended tasks.

Compared with Claude Opus 5.5: Focus on Comparing Opus High for Complex Projects

On CursorBench 4.0, Opus 5.5 High scores 56.0%, with an average cost of USD 3.97 per task. Sol Max scores 41.7%, at a corresponding cost of USD 8.23. On this basis, we think Opus High merits priority evaluation for this set of Cursor project tasks; the cost conclusion should also be limited to the corresponding tasks and reasoning levels. CursorBench head-to-head results

We recommend that our customers use Claude Opus 5.5 in scenarios requiring creativity and divergent thinking, and then hand implementation and delivery to ChatGPT 5.6 SOL.

ChatGPT-5.6 SOL API Frequently Asked Questions

How do I call the ChatGPT-5.6 SOL API through RequestBeam?

We recommend first signing in to RequestBeam and creating an API Key, then reading our documentation or asking AI to help you configure the connection.

We recommend completing a minimal generation test through the online tool first, then integrating according to RequestBeam's current ChatGPT-5.6 SOL API documentation. This lets you verify whether the model meets your business needs before integration and helps avoid mistakenly carrying over endpoint paths and parameters from other providers into RequestBeam.

How Should Reasoning Parameters Be Set, and What Is the Difference between Max and Ultra?

With Responses, reasoning intensity is configured through an object such as reasoning: {"effort":"high"}; the corresponding field in OpenAI Chat Completions is the top-level reasoning_effort. We recommend following the parameter documentation for your chosen endpoint and starting tests with configurations supported by the current service. OpenAI reasoning parameters · Chat Completions parameters

max is a reasoning effort level supported by Sol, while Ultra involves parallel collaboration among multiple agents. We do not recommend entering ultra directly as the reasoning.effort of an ordinary request, and we do not equate community experiences with Sol Ultra to a single Max request. For similar parallel processing, use an application or orchestration capability that supports multiple agents. OpenAI's explanation of Max and Ultra

Why Have Tokens Been Consumed without a Complete Response Text?

We recommend first checking the output budget and completion status. For reasoning models, Responses' max_output_tokens covers both reasoning tokens and visible response text; the budget may be exhausted before the actual answer. If the response returns status: "incomplete" and incomplete_details.reason is max_output_tokens, you can increase the budget or lower the reasoning level if quality allows. OpenAI output budget explanation

We also recommend checking your response-parsing code. Responses' output is a list of content items that may contain messages and tool calls, so you cannot always treat the first item as the response text. If the model returns a tool call, the application must execute the tool and send back its results before the task can continue. When streaming is enabled, continuously process text deltas and completion events. RequestBeam response structure and streaming explanation

Why Does Ordinary Chat Work but Codex Reports That Compact Is Unavailable?

We recommend checking ordinary Responses requests and context compression separately. RequestBeam's current Codex documentation states that five GPT models, including Sol, do not yet have a verified Compact channel. Ordinary chat returning a result does not mean that POST https://api.requestbeam.com/openai/responses/compact is already available. Adjusting the local token threshold or switching between HTTP and WebSocket will not automatically provide this capability. RequestBeam Codex integration guide

For long-running project tasks, we recommend saving requirements, completed changes, and next steps at each stage, limiting the history carried into each round, and starting a new session before reaching the context limit. When you need automatic compression or stateful continuation that depends on previous_response_id, first confirm that the selected service explicitly supports the corresponding capability before making your business workflow depend on it. RequestBeam session capability explanation