Reason: Could not reach upstream
Detection results
Model: claude-haiku-4-5-20251001 · mode full · relay https://api.apiqik.com
Verify whether server-side exclusive fields such as Claude thinking / signature are transparently transmitted.
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 canned reasoning questions to check whether verifiable answers are correct.
Hosted channel:Anthropic
Model:— · Observed tokens:None · Expected tokens:None · Match:unknown
Use minimalist requests to observe whether input_tokens is abnormally high and identify implicit System injection.
Compare Claude series characteristics through behavioral preferences and answer patterns.
Use Anthropic / Claude general knowledge questions to cross-validate whether the backend is as claimed as Claude.
Comparing request model, response model and identity stability in multiple rounds of returns.
Check message IDs, SSE event sequences, and content blocks for compliance with Anthropic specifications.
Check whether the tool_use block, toolu_ ID, function name and parameter schema are standardized.
Compare stream and non-stream text, token, and end reason for consistency.
Check that the input / output / cache token fields are complete and authentic.
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 needle-in-haystack to verify that long context windows are honored.
Submit a PDF probe to confirm that the document understanding link has not been stripped by the transfer station.
What does each Claude check cover?
- Model authentication
- Examine the model's self-reported identity, brand residue, and common signs of camouflage.
- Think supportive ⭐
- Verify whether server-side exclusive fields such as Claude thinking / signature are transparently transmitted.
- 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.
- Channel detection
- Identifies the hosting channel from the response ID prefix.
- Claude Tokenizer verification
- Count input_tokens with fixed hints compared to calibrated model baseline.
- behavioral style consistency
- Compare Claude series characteristics through behavioral preferences and answer patterns.
- knowledge accuracy
- Use Anthropic / Claude general knowledge questions to cross-validate whether the backend is as claimed as Claude.
- Model declaration consistency
- Comparing request model, response model and identity stability in multiple rounds of returns.
- Message structure specification
- Check message IDs, SSE event sequences, and content blocks for compliance with Anthropic specifications.
- Tool Use/Structured Output
- Check whether the tool_use block, toolu_ ID, function name and parameter schema are standardized.
- streaming consistency
- Compare stream and non-stream text, token, and end reason for consistency.
- Token usage consistency
- Check that the input / output / cache token fields are complete and authentic.
- Official comparison of the same title
- The same question compares input consumption and response behavior between channels and official (or baseline).
- long context authenticity
- Use needle-in-haystack to verify that long context windows are honored.
- Multimodal/PDF recognition
- Submit a PDF probe to confirm that the document understanding link has not been stripped by the transfer station.
- 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.