Detection results

Model: gpt-5.6-sol · mode full · relay https://gw.hbapis.com/

Total score

Each item is pass / fail / error; the total is a weighted result and may be in-between.

83 / 100

Basic request Verification completed

Confirm that Chat Completions basic requests can return stably.

Pass 100

model consistency Verification completed

Compare the request model and response model fields to see if they match.

Pass 100

function call Verification completed

Check tool_calls, function name, parameter JSON and call structure.

Pass 100

Structured output Verification completed

Verify that JSON Schema / strict output constraints are in effect.

Pass 100

Protocol normativeness Verification completed

Verify the shape of fields such as id, object, choices, finish_reason, usage, etc.

Failed 60

streaming consistency Verification completed

Compare stream and non-stream text and token usage for consistency.

Pass 100

Token billing Verification error

Reason: Check prompt / completion / total token metering relationship.

Verification error 0

Accurate command reproduction (anti-casing) Verification completed

The model is asked to reproduce a fixed mark verbatim, checking how deterministic the instructions are followed.

Pass 100

System conflict testing Verification completed

Compare user only to explicit system+user, checking whether fixed words are overridden by hidden directives.

Pass 100

Minimalist Input Token Audit Verification completed

Use minimalist requests to observe whether input_tokens is abnormally high and identify implicit System injection.

Pass 100

Official comparison of the same title Verification completed

The same question compares input consumption and response behavior between channels and official (or baseline).

Not applicable —

Hide injection detection Verification completed

Randomized variants cross-check for abnormal signals such as nonce leaks, fixed replies, and suspicious cache reuse.

Pass 100

Security rejection and policy stability Verification completed

Check if the security policy is stable using denial-of-answer scenarios and rewriting/encoding/progressive variants.

Failed 50

Extreme reasoning ability (number theory counting) Verification completed

Use canned reasoning questions to check whether verifiable answers are correct.

Pass 100

long context authenticity Verification completed

Use needle-in-haystack to verify that the context window is honored.

Failed 67

How to read this result?

中转站疑似伪装成 OpenAI

响应的 usage 字段里出现了 Anthropic / Google 后端才会用的字段(如 claude_cache_creation_*、gemini_* 或 usage_source 自报非 openai)。这强烈暗示中转站把你的请求转发给了别的厂商后端再包装成 OpenAI 响应,所谓的 GPT 输出可能并非真正的 OpenAI 模型在生成。

Token 计费存在风险

Token 统计有明显偏差,建议留意是否存在多算或统计错误。

First token

1,606ms

Total time

100,741ms

Throughput (T/S)

14.7

Input tokens

165,085

Output tokens

1,480

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.