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GPT 5.6 Sol online chat tool
Model settings
What does memory do?
It includes completed, reusable turns from this conversation in your next request, so you can ask follow-up questions without repeating the context.
What happens when it is off?
Only your new message and any system prompt are sent. Existing messages stay visible; turning memory back on includes eligible earlier turns again.
Is this long-term memory?
No. This is conversation context, not memory across separate conversations. Failed, stopped, truncated, or non-reusable replies are excluded. More history can use more input tokens and increase cost.
Advanced parameters
0 or more characters(0/No fixed limit)
No fixed character limit; the full request is subject to request-size and model-context limits. This is not a token count.
0 – 2
0 – 1
-2 – 2
-2 – 2
Conversation
How can I help you today?
No response data yet
Ready
Recorded API Hop samples
Recorded on October 22, 2026 through API Hop. Each result is one request for the named model and mode, not a success-rate, speed or production-quality guarantee. Model replies below remain in their original language.
Payment webhook retry review
gpt-5.6-sol · code-review ·
Verbatim excerpt
**Observed Facts**
- Every failed POST is retried three times.
- No idempotency key is stored.
- The balance is debited before the response is sent.
Full model reply (unmodified)
**Observed Facts**
- Every failed POST is retried three times.
- No idempotency key is stored.
- The balance is debited before the response is sent.
**Conditional Risks**
- If the debit succeeds but the response fails or is lost, a retry may debit the balance again.
- If each retry independently performs the debit, one logical payment could produce multiple charges.
- If retries overlap, concurrent requests could each debit the balance because no stored idempotency record distinguishes the original request from duplicates.
- The maximum number of duplicate debits is unclear because “retried three times” does not specify whether that means three additional attempts or three attempts total.
**Proposed Changes**
- Require a unique idempotency key for each logical payment, and reuse that same key on every retry.
- Persist the idempotency key and payment outcome so repeated POSTs return the previously recorded result without another debit.
- Make recording the idempotency result and applying the debit atomic: both succeed together, or neither succeeds.
- Define the retry policy explicitly, including whether “three times” means three retries after the initial attempt.
- Do not rely on successfully sending the response to determine whether the debit occurred; retries must consult the stored outcome.
**Unexecuted Acceptance Tests**
1. **Lost response after debit:** Submit a payment, simulate failure after the debit but before the response is received, and retry three times with the same idempotency key. Verify exactly one debit occurs and every retry returns the same recorded outcome.
2. **Concurrent duplicate requests:** Send multiple simultaneous POSTs with the same idempotency key. Verify exactly one request applies the debit and all requests receive a consistent result.
3. **Failure before debit:** Simulate a failure before either the debit or idempotency outcome is committed, then retry with the same key. Verify the balance is ultimately debited no more than once and no incomplete record incorrectly reports success.
The reply distinguishes observed facts, conditional risks and unexecuted tests. No code or payment system was run. Input usage is reproduced exactly as reported by the API and is much larger than the visible prompt. Billing was not independently verified; these token counts are not a cost figure.
Complete request and input
POST /v1/chat-completions
{
"model": "gpt-5.6-sol",
"messages": [
{
"role": "user",
"content": "Review this fictional payment-webhook policy: every failed POST is retried three times, no idempotency key is stored, and the balance is debited before the response is sent. Identify duplicate-charge risks, propose minimal changes and three acceptance tests. Use only these facts; do not assume access to code, tools or production systems. Return at most 600 words. Separate observed facts, conditional risks, proposed changes and unexecuted acceptance tests."
}
],
"stream": false,
"max_completion_tokens": 4096
}
These are evaluation prompts for GPT 5.6 Sol, not recorded model outputs. They analyze only supplied text; they do not execute code, inspect production systems or prove long-context performance. Check quoted evidence and acceptance criteria, then review response metadata and usage before production use.
Review this fictional payment-webhook policy: every failed POST is retried three times, no idempotency key is stored, and the balance is debited before the response is sent. Identify duplicate-charge risks, propose minimal changes and three acceptance tests. Use only these facts; do not assume access to code, tools or production systems.
Analyze this synthetic trace: [L1] 10:00:00 request A accepted; [L2] 10:00:01 worker started A; [L3] 10:00:31 client timeout; [L4] 10:00:33 worker completed A; [L5] 10:00:35 client submitted A again. Separate observations from hypotheses, cite line IDs, list missing evidence and propose two checks. Do not claim to have inspected a real system or executed a fix.
A fictional two-person team runs 2,000 reports daily. Completion within 30 minutes is acceptable. It has a database but no queue service; jobs must recover after restart and avoid duplicate delivery. Compare a database-backed batch with a dedicated queue. Give a decision table, a conditional recommendation, three unknowns and one condition that reverses your choice. Do not invent benchmarks, costs or tested guarantees.
Information checked on: October 8, 2026.
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.
Terminal-Bench 4.0Snapshot
Mean pass rate%
050100
GPT-5.6 Sol Max37.88%
GPT-6 Astra Max59.60%
Average cost / taskUSD / task
0510
GPT-5.6 Sol Max$7.98
GPT-6 Astra Max$9.58
mini-swe-agent; 66 tasks, three independent runs per task. Mean pass rate. External evaluation costs, not API Hop prices.Vals AI
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
Reasoning level
Score
Average cost per task
Medium
31.1%
USD 1.77
High
35.7%
USD 2.85
Extra High
37.7%
USD 4.40
Max
41.7%
USD 8.23
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.
Sol reasoning effortSnapshot
CursorBench 4.0 score%
050100
Sol Medium31.1%
Sol High35.7%
Sol Extra High37.7%
Sol Max41.7%
Average cost / taskUSD / task
0510
Sol Medium$1.77
Sol High$2.85
Sol Extra High$4.40
Sol Max$8.23
Real Cursor sessions with multi-file tasks. High to Max: +6 percentage points at about 2.9 times the cost. External evaluation costs, not API Hop prices.CursorBench 4.0
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.
Professional work deliverySnapshot
GDPval-AA v2.1Elo
GPT-5.6 Sol Max1611
220 tasks across 44 occupations and nine industries; shell and web tools; anonymous pairwise evaluation of deliverables. Elo is a relative rating, not a percentage.Artificial Analysis
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.
Cross-application automationSnapshot
AutomationBench-AA goal completion%
GPT-5.6 Sol Max≈ 60%
Average proportion of task goals completed, not a workflow pass rate. A task violating a business constraint receives zero.Artificial Analysis
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.
Quality, cost and output speedSnapshot
Intelligence Index v4.3.2Points
GPT-5.6 Sol Max47
Weighted cost / taskUSD / task
GPT-5.6 Sol Max≈ $1.99
OpenAI API output speedToken/s
GPT-5.6 Sol Max≈ 81.3
Output speed measured on OpenAI API after generation begins; not API Hop throughput, time to first token or total task time. External evaluation costs, not API Hop prices.Artificial Analysis
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.
Sol Max vs Terra MaxSnapshot
Intelligence Index v4.3.2Points
03060
Sol Max47
Terra Max42
Weighted cost / taskUSD / task
0510
Sol Max$1.99
Terra Max$1.40
GDPval-AA v2.1Elo
Sol Max1611
Terra Max1453
AutomationBench-AA goal completion%
050100
Sol Max≈ 60%
Terra Max≈ 60%
External evaluation costs, not API Hop prices. Automation scores are approximate goal completion, not workflow pass rates.Artificial Analysis
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.
Sol Max vs Astra HighSnapshot
Intelligence Index v4.3.2Points
03060
Sol Max47
Astra High51
Weighted cost / taskUSD / task
0510
Sol Max$1.99
Astra High$1.73
GDPval-AA v2.1Elo
Sol Max1611
Astra High1485
AutomationBench-AA goal completion%
050100
Sol Max≈ 60%
Astra High≈ 67%
Astra High here, not the Astra Max configuration in Vals AI. External evaluation costs, not API Hop prices. Automation is approximate goal completion, not a pass rate.Artificial Analysis
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.
Sol Max vs Claude Opus 5.5 HighSnapshot
CursorBench 4.0 score%
050100
Sol Max41.7%
Claude Opus 5.5 High56.0%
Average cost / taskUSD / task
0510
Sol Max$8.23
Claude Opus 5.5 High$3.97
Real Cursor multi-file project tasks; conclusions apply to these tasks and effort settings. External evaluation costs, not API Hop prices.CursorBench 4.0
Why use GPT 5.6 Sol through API Hop?
Usage-based billing and model cost optimization
Charges follow current API Hop rates and actual usage. Top up on demand.
Unified AI models and data APIs, with multi-model access in one place
One API key connects multiple AI models and data services, reducing integration costs and letting you switch with one click. Business applications need both AI and data.
Call logs and application access controls, with clarity on every call
Every API call is recorded in your dashboard. Create separate API keys for different applications.
Evaluate outputs against your task
Use the documented model ID and supported parameters through API Hop. Evaluate outputs against your own acceptance criteria, then inspect usage records and response metadata before production use. A model name or example does not guarantee each result.
After creating an API key in the console, follow the documentation to get started with GPT 5.6 Sol.
Prepare your account and test online
Sign in, confirm your balance, and create an API key.
Try it in the Playground
Explore the model's capabilities in the Playground to find the best way to use it.
Integrate GPT 5.6 Sol into your business
Follow our developer documentation to integrate GPT 5.6 Sol into your business.
Future model upgrades
To switch to a newer model, first confirm that its exact ID is returned by the model catalog for your API key. If it supports the same endpoint and request fields, update the model ID, then test response formats, streaming, output limits and billing before sending production traffic. For asynchronous image or video tasks, also verify job submission, status polling and result retrieval.
How do I call the GPT 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 GPT 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.
API Hop documents both Chat Completions and OpenAI-compatible Responses for gpt-5.6-sol, not Claude Messages. Use messages for Chat and input for Responses, and parse the matching response format. Do not infer support for Compact, a stateful previous_response_id, or every native tool from ordinary Responses availability.
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
For Chat Completions, also check finish_reason and usage. Narrow the task or adjust the allowance; partial output is not a completed technical answer.
0
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
Can the GPT 5.6 Sol Playground analyze images, PDFs, or linked repositories?
The conversation tool accepts text without file or image upload controls. Sol has native image-input capability, and the Responses guide describes image and PDF input formats subject to model and channel support. Those inputs are separate from this text chat; a pasted link does not automatically load a file or repository.
Is conversation memory persistent across separate sessions?
No. It sends eligible completed turns from the current conversation with your next request. Disabling it keeps history visible but excludes it from the new input; failed, stopped, truncated, or non-reusable replies are excluded. More history can increase input usage. This control does not enable Responses persisted reasoning or stateful continuation.
How should I troubleshoot 401, 402, 403, or 429 errors?
Check authentication for 401, balance for 402, model permissions for 403, and rate or allowance limits for 429. Confirm gpt-5.6-sol against the model catalog for your key. The numeric platform code is separate from the HTTP status; inspect the complete error for upstream failures and use protocol-specific handling for errors inside an SSE stream.
Can GPT 5.6 Sol return JSON or several proposed plans?
Ask for several alternatives in one answer and define required fields, types, and unknown-value behavior for JSON. The text Playground does not enforce a schema merely because the prompt asks for one. Responses has a documented text.format configuration, subject to model support; validate syntax, schema, and factual values before downstream use.
Can GPT 5.6 Sol execute a migration or run tools here?
No. This text Playground does not run commands, modify files, or access your database. An API-based agent needs supported tool definitions, an execution environment, permissions, result feedback, and approval gates. A returned function call is a request for an action, not proof that a migration or deployment succeeded.
Should I immediately retry a slow, interrupted, or stopped request?
Check available output, request records, and usage first. Reasoning, large inputs, and long outputs affect duration. Stopping browser waiting or losing the stream does not establish that upstream work was cancelled or unprocessed. Resolve outstanding results and apply a bounded error-specific retry policy to reduce duplicate work and charges.
