Confirm that Chat Completions basic requests can return stably.
Detection results
Model: gpt-5.5 · mode standard · relay https://test.apiqik-kratos.apiqik.com/
Compare the request model and response model fields to see if they match.
Check tool_calls, function name, parameter JSON and call structure.
Verify that JSON Schema / strict output constraints are in effect.
Verify the shape of fields such as id, object, choices, finish_reason, usage, etc.
Compare stream and non-stream text and token usage for consistency.
Check prompt / completion / total token metering relationship.
The model is asked to reproduce a fixed mark verbatim, checking how deterministic the instructions are followed.
Compare user only to explicit system+user, checking whether fixed words are overridden by hidden directives.
Use minimalist requests to observe whether input_tokens is abnormally high and identify implicit System injection.
The same question compares input consumption and response behavior between channels and official (or baseline).
Randomized variants cross-check for abnormal signals such as nonce leaks, fixed replies, and suspicious cache reuse.
Check if the security policy is stable using denial-of-answer scenarios and rewriting/encoding/progressive variants.
Use canned reasoning questions to check whether verifiable answers are correct.
What does each OpenAI check cover?
- Basic request
- Confirm that Chat Completions basic requests can return stably.
- model consistency
- Compare the request model and response model fields to see if they match.
- function call
- Check tool_calls, function name, parameter JSON and call structure.
- Structured output
- Verify that JSON Schema / strict output constraints are in effect.
- Protocol normativeness
- Verify the shape of fields such as id, object, choices, finish_reason, usage, etc.
- streaming consistency
- Compare stream and non-stream text and token usage for consistency.
- Token billing
- Check prompt / completion / total token metering relationship.
- Official comparison of the same title
- The same question compares input consumption and response behavior between channels and official (or baseline).
- Accurate command reproduction (anti-casing)
- The model is asked to reproduce a fixed mark verbatim, checking how deterministic the instructions are followed.
- System conflict testing
- Compare user only to explicit system+user, checking whether fixed words are overridden by hidden directives.
- Minimalist Input Token Audit
- Use minimalist requests to observe whether input_tokens is abnormally high and identify implicit System injection.
- Extreme reasoning ability (number theory counting)
- Use canned reasoning questions to check whether verifiable answers are correct.
- long context authenticity
- Use needle-in-haystack to verify that the context window is honored.
- Hide injection detection
- Randomized variants cross-check for abnormal signals such as nonce leaks, fixed replies, and suspicious cache reuse.
- Security rejection and policy stability
- Check if the security policy is stable using denial-of-answer scenarios and rewriting/encoding/progressive variants.