Since the launch of OpenAI's Responses API, the developer community has been plagued by puzzling cognitive biases. This confusion is partly due to a lack of clear communication from the official side—we rarely explain why this API was restructured and how it changes the underlying logic of AI applications.

### Three Common Misconceptions

1. **Functionality Limitation Myth**

"Responses can't do what Completions can" is the most persistent fallacy. In reality, Responses is strictly a superset, retaining the ability to manually manage conversation states while adding mechanisms for persistent Chain-of-Thought (CoT). When a model is interrupted during tool calls, subsequent requests can directly resume the previous thought process.

2. **Data Residency Phobia**

Developers mistakenly believe they must accept server-side state storage. In fact, by returning the encrypted `reasoning_items` field, it's entirely possible to implement a client-managed Zero Data Retention (ZDR) solution, identical to traditional Completions.

3. **Intelligence Equivalence Claim**

Responses is designed specifically for tool-calling, thought-oriented models. Tests show that in multi-step reasoning Agent scenarios, GPT-5 using Responses outperforms Completions mode by 23% in IQ and improves cache utilization by 40%.

### The Harsh Reality

Despite its elegant technical design, the rollout has exposed typical big-company pitfalls:

- Documentation reads like legal text rather than a tutorial

- Azure version lacks critical features (parallel tool calls)

- Unresolved performance issues (community reports 300ms+ latency increases)

- Compatibility gaps with aggregation platforms like OpenRouter

Most ironically, the official demo image (see below) clearly shows that `chat/completions` also includes the `reasoning.encrypted_content` field, yet there's no explanation on how to leverage this hidden feature.


### Compromise Solutions

For teams resistant to full migration, consider:

1. Using LiteLLM as a protocol conversion layer

2. Maintaining Completions infrastructure while enabling Responses only for complex Agent tasks

3. Waiting for platforms like OpenRouter to complete adaptations

This API iteration is essentially trading short-term pain for the evolutionary potential of thought-oriented AI. But OpenAI needs to understand: technical superiority is never a sufficient condition for developer migration—clear roadmaps matter more than technical specs.