Extract these synthetic records into JSON with fields id, owner, deadline and evidence: [R1] Issue 41: Ana owns the export fix; no deadline agreed. [R2] Issue 42: review due October 15; owner not assigned; year not stated. Use null for missing values and preserve partial dates as given. Quote the source in evidence. Do not invent a year or owner. Check each field against its record; this is a text-formatting task, not a database operation.
GPT 5.5 API: Pricing, Playground and Examples
Explore GPT 5.5 API pricing and reasoning controls for cross-module code review and technical decisions, with Chat Completions and Responses examples.
GPT 5.5 API pricing and billing
Pricing and availability are currently unavailable. Check account pricing and model availability before submitting a paid request.
GPT 5.5 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. Conversation
How can I help you today?
No response data yet Ready
GPT 5.5 prompt examples
These are evaluation prompts for GPT 5.5, 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.
Use only this document sample: [P1] The export fix passed staging checks. [P2] A Friday release was proposed, not approved. [P3] Mei will review error messages on Tuesday. [P4] No owner is assigned to documentation. Return a table of claim, exact supporting quote, paragraph ID and uncertainty. Is production release approved? Who owns documentation? Mark missing facts unknown. Every quote must match the supplied sample; do not infer production success from staging. This small sample does not test maximum context length.
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.
What is GPT 5.5?
GPT 5.5 model focus and core capabilities
GPT 5.5 is an OpenAI reasoning model for complex coding and professional work. Its native context window is 1,050,000 tokens, with text and image input and text output. It can help connect requirements, source material, and competing constraints when preparing technical decisions.
GPT 5.5 online tools and API on API Hop
API Hop provides a text Playground, unified Chat Completions, and an OpenAI-compatible Responses interface for GPT 5.5. Test a decision brief or code review here before integrating it. The chat tool does not provide file uploads or execution tools; model capabilities and native context size do not establish the inputs or allowances available in this tool.
GPT 5.5 capabilities and domain strengths
GPT 5.5 is a candidate for work where several requirements must remain consistent throughout an answer. Give it the intended outcome, evidence, and acceptance conditions so a recommendation can be checked against the actual problem.
Architecture decisions and constrained reasoning
Describe hard requirements separately from preferences when comparing architectures. For a background reporting service, restart recovery and duplicate delivery may matter more than average daily volume. Ask for the conditions under which each option fails.
A decision brief should identify unknown measurements and the evidence that could reverse its recommendation. GPT 5.5 does not measure your infrastructure here; proposed throughput, recovery behavior, and operating cost need independent validation.
Coding and cross-module review
Supply related code, interface contracts, failure logs, and expected behavior to evaluate a repair. Ask GPT 5.5 to connect the failing path to a minimal change and regression tests instead of rewriting an isolated error message.
An application can provide repository and execution tools in a supported integration. This Playground only returns text, so patches remain proposals until your compiler, tests, and reviewers establish that they preserve existing behavior.
Long-document analysis and evidence alignment
The native 1,050,000-token context window provides room for extensive source material. For a decision spanning design notes and operational reports, ask for disagreements, tentative commitments, and unresolved dependencies rather than only a combined summary.
Actual input allowances depend on the API Hop interface and upstream configuration. Select relevant passages and retain source identifiers; a large context window does not ensure every exception is recalled or that missing evidence becomes known.
Visual understanding and structured results
GPT 5.5 supports image understanding, function calling, and structured outputs natively. These capabilities can support applications that relate diagrams to technical requirements or convert reviewed material into defined fields.
The text tool has no image or PDF upload control. For an API integration, confirm supported content blocks and format settings in the selected interface. Validate extracted values and business rules even when the result satisfies a schema.
How to evaluate GPT 5.5 for your workload
Performance should be evaluated on the decisions and software tasks your application actually handles. Model capability descriptions do not establish API Hop latency, availability, or a guaranteed task completion rate.
| Task input | Checks | Pass criteria | Metrics to record |
|---|---|---|---|
| A reproducible bug with relevant files and regression tests | Compile the patch, reproduce the fix, and review compatibility | The failing case and regression tests pass without unrelated changes | Test outcomes, revisions, first-text time, total duration, token usage |
Evaluate coding with executable checks
Use a fixed set of bugs with known expected behavior, relevant files, and regression tests. Record whether proposed changes compile, fix the issue, and avoid unrelated modifications. Include cases involving compatibility and recovery after interruption.
Separate the quality of a text patch from an agent run with file access and execution feedback. A model-only answer cannot reproduce a benchmark environment that supplies commands, tools, and iterative verification.
Evaluate reasoning with decision rubrics
Score a decision brief on constraint coverage, evidence alignment, treatment of unknowns, and a measurable reversal condition. Include conflicting notes and missing cost data to check whether the model preserves uncertainty rather than inventing a convenient answer.
The native model supports adjustable reasoning effort. Treat effort as an evaluation variable only where the interface exposes it; increasing it is not proof of a better decision, and output length is not a substitute for correctness.
Measure latency, usage, and rework together
Keep inputs and acceptance criteria consistent when recording first visible text, total duration, token usage, and the number of revisions needed for an acceptable result. Evaluate short extraction tasks separately from longer architecture reviews.
Long history and large outputs can increase cost. Compare total cost of an accepted result, not just the price of one request, and establish practical limits with a small sample before expanding concurrency or production volume.
GPT 5.5 compared with GPT and Claude models
Compare GPT 5.5 with catalog alternatives using the same task and evidence. The table defines useful comparison samples rather than assigning unverified benchmark scores or assuming that version numbers establish a winner.
| Task | GPT 5.5 sample | Alternative | Validation |
|---|---|---|---|
| Architecture decision | Constraint-aware recommendation | GPT 5.4 | Check requirements and reversal conditions |
| Cross-module repair | Patch and regression plan | GPT 5.6 Sol | Compile and run the same tests |
| Document synthesis | Conflicts and evidence gaps | Claude Opus 4.6 | Compare conclusions with source passages |
| Short extraction | Typed fields with unknowns | Gemini 3.8 Flash | Check field accuracy and total cost |
When to prioritize testing GPT 5.5
Prioritize a GPT 5.5 trial when the answer must reconcile engineering constraints, analyze a difficult repair, or produce an evidence-based decision from several documents. Build test cases around failure recovery and compatibility as well as the preferred path.
Keep an existing GPT 5.4 baseline when evaluating a switch, including cases that already work. Inspect regressions, changes in output style, and integration requirements before treating the new configuration as a replacement.
Choose models by task on API Hop
Evaluate GPT 5.6 Sol and Claude Opus 4.6 alongside GPT 5.5 for technical analysis; include Gemini 3.8 Flash for shorter extraction or summaries. These are task candidates, not assertions that one model always wins on quality or price.
Use the current account prices and the same acceptance rubric. Shared Chat Completions helps reuse authentication and message structure, but Responses, Messages, optional parameters, and tool availability need model-specific checks.
Why use GPT 5.5 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.
How to use GPT 5.5 on API Hop
After creating an API key in the console, follow the documentation to get started with GPT 5.5.
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 5.5 into your business
Follow our developer documentation to integrate GPT 5.5 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.
GPT 5.5 API FAQ
Can the GPT 5.5 Playground accept images, PDFs, or video?
This chat tool accepts text without file or image upload controls. Native image understanding does not enable uploads here. A Responses integration must follow its documented image or PDF content blocks and confirm model and channel support; pasting a link into chat does not automatically download or analyze it.
Why is the answer missing or cut off after reasoning?
Reasoning can consume output allowance before final text. Check Chat finish_reason or Responses completion status and incomplete details, then narrow the task or adjust the appropriate output budget. Responses uses max_output_tokens; Chat uses its documented budget fields. A visible reasoning summary is not a complete answer, and raw internal reasoning is not guaranteed to be exposed.
Is conversation memory permanent, and why does usage grow?
Memory here reuses eligible completed turns in the current conversation; it is not permanent memory across sessions. More history can increase input usage. Turning it off leaves history visible but excludes it from new requests. Failed, stopped, truncated, or non-reusable replies are not appended as normal history; this control is distinct from Responses 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 for 429. Confirm gpt-5.5 in the models visible to your key. A numeric platform code is separate from the HTTP status. Use the full error and request details to diagnose 502, 503, 504, or errors within an SSE stream; do not assume every failure is a key problem.
Can I use Responses or Claude Messages with GPT 5.5?
The API Hop catalog lists gpt-5.5 for unified Chat Completions and OpenAI-compatible Responses, not Claude Messages. Responses uses input and returns output items, while Chat uses messages and choices. Match the SDK endpoint and parser to the selected protocol; ordinary Responses support is not a guarantee of Compact or previous_response_id support on the selected channel.
Can GPT 5.5 return several options or guaranteed correct JSON?
Ask for several alternatives in one answer without assuming support for multiple generated choices on every route. GPT 5.5 supports structured outputs natively, and Responses documents text.format; confirm the selected integration supports your schema. A JSON prompt in this chat tool does not enforce a schema. Validate syntax, types, source alignment, and business rules before using the result.
Can GPT 5.5 run code, call tools, or complete actions here?
This chat tool returns text and does not run commands, change repositories, browse, or update records. Tool-capable API workflows require documented tool support, application execution, permission checks, and result feedback. Treat a function call as an instruction to handle, not proof that an action succeeded.
Should I immediately retry a slow response or interrupted stream?
Check partial results, request records, and usage first. Long inputs and reasoning can affect duration. Stopping browser waiting or losing a stream does not establish that the upstream request was cancelled or unprocessed. Resolve outstanding results and use bounded retries to reduce duplicate requests and charges.
Related text APIs
GPT 5.5 API examples and supported parameters
GPT 5.5 API quickstart
gpt-5.5model is required; these examples use the current page model ID. Check model access for your API key with GET /v1/models.
Chat CompletionsPOST /v1/chat-completions
curl -X POST "https://api.apihop.ai/v1/chat-completions" \
-H "Authorization: Bearer <API_KEY>" \
-H "Content-Type: application/json" \
-d '{"model":"gpt-5.5","messages":[{"role":"user","content":"Hello, introduce yourself in one sentence."}],"stream":false}'ResponsesPOST /openai/responses
curl -X POST "https://api.apihop.ai/openai/responses" \
-H "Authorization: Bearer <API_KEY>" \
-H "Content-Type: application/json" \
-d '{"model":"gpt-5.5","input":"Hello, reply with one short sentence.","stream":false}'Family and protocol reference
OpenAI Multimodal Responses
Full API documentationOpenAI-compatible Responses API. This endpoint uses OpenAI-style `input`, supports text, public image URLs, PDF file input, tool calls, reasoning options, and streaming parameters.
POST
https://api.apihop.ai/openai/responses