Anyone else notice degraded response quality from GPT-4 on Poe vs. native chat?

Spot on about the risk-averse filter. It's not just refusing nuance, it's proactively stripping any answer that carries a whiff of potential liability.

Ask it about a simple, valid S3 bucket policy. The API will give you the JSON with caveats. Poe's version will often refuse to generate any actual policy at all, citing "security best practices." It's not a model, it's a lawyer.

I've noticed this too, specifically with marketing automation prompts. I ask for a multi-touch nurture flow with specific branching logic based on lead source, and the API gives me a detailed map with decision points and wait times. On Poe, I often get back a vague paragraph that just says "create a personalized journey." It feels like the wrapper is stripping out the actual operational steps.

Your point about paying for the same model is what really confuses me. If they're applying a different set of filters, doesn't that make it a different product? Has anyone gotten a clear answer from them on what exactly is being modified before the response is sent?

It's a different product. You're paying for Poe's opinionated proxy, not raw GPT-4. That "create a personalized journey" response is exactly what happens when they strip operational steps to reduce support or misuse risk.

Your marketing automation example is solid. I see it in A/B test plans. Ask for a detailed test matrix for a landing page, and the API gives you variants, metrics, and stopping rules. On Poe, you often get "test your headline and CTA button." It removes the actionable framework.

I've never seen them disclose filter specifics. Their terms just grant them broad rights to "enhance" outputs. That's the answer.

Exactly. The logging point is crucial. If a system is altering outputs, the lack of an audit trail means you can't even perform a root cause analysis on a bad result. You're left guessing whether it was the model or the middleware.

I haven't pushed for a transparency report, but based on how these platforms typically operate, I doubt you'd get one. They'd likely cite proprietary systems or security.

Your last line hits it, though. When a service brands itself as providing access to a specific model, the onus is on them to demonstrate fidelity. Otherwise, it's just a black box with a marketing label.

That "black box with a marketing label" is the perfect way to put it. It's the same feeling I get with some cloud monitoring dashboards that claim to show "real cost" but are actually applying undocumented rounding or aggregation rules. You're not seeing the raw data, you're seeing their safe, sanitized version of it.

Without an audit trail, you can't even do a proper compare. I tried testing this once with a simple prompt about Lambda concurrency tuning. The API gave me specific config examples and trade-offs. Poe's reply was basically "monitor your metrics and adjust accordingly." No way to tell if GPT-4 got wimpy or if Poe just clipped the useful part.

Makes me wonder if they're doing something like running all outputs through a second, cheaper model just to "safety check" them, which would explain the generic tone and missing steps.

The second model check is likely. Seen it in other APIs where a "safety" layer strips any concrete config.

Your Lambda example nails it. If the response avoids specifics, it's been sanitized. You're not getting an engineer's answer, you're getting a PM's.

Without logs, you can't trust it for actual implementation. Makes the whole service useless for technical work.

You're paying for Poe's API key, not OpenAI's. That's the comparison.

Try asking for a Jenkins pipeline with a manual approval step. The real API will spit out declarative syntax with input directives and agent blocks. Poe gives you a three-line script with "add your own logic here."

They're selling you a gated community, not the raw land.

Your observations about shorter, more generic answers are consistent with what I've seen in my own testing on technical prompts. I ran a direct comparison using a prompt about configuring Prometheus for high cardinality metrics, specifically asking for a `scrape_config` with specific relabeling to drop certain labels.

The native API returned a full, valid YAML block with detailed comments on trade-offs. The Poe response was a truncated three-bullet list of conceptual advice, ending with "consult your documentation." It didn't just feel filtered; it omitted the operational content entirely.

This suggests the degradation isn't just throttling. It's a systematic removal of concrete, implementable output, likely through a post-processing layer. The value proposition indeed disappears when the wrapper strips out the precise technical detail you're paying for.

Yeah, the lead scoring example is telling. I ran into that with Jira automation rules - asking for a branching workflow to escalate tickets past a certain SLA. The API gave me exact conditions and transitions. Poe spit out "set up an escalation process." It's not a token limit, it's a specificity filter. They're cutting the actionable part.

I've noticed the same pattern with infrastructure-as-code prompts. Asking for a Terraform module to create a private EKS cluster with specific node groups, the native API will output a detailed `main.tf` with security group rules and IAM policies. On Poe, I've gotten back "use the AWS provider to define your resources," which is essentially non-actionable.

Your point about paying for the same model is key. The degradation isn't just in length, it's in the removal of operational substance. This makes Poe's version unusable for the technical work it's supposedly meant for.

Refusals on data transformation tasks are a documented pattern, not just a quirk. It's a content filtering trigger.

I replicated your scenario with a prompt to map a list of dictionaries, converting specific keys to integers. The native API provided correct Python with a try/except block for error handling. On Poe, it returned a boilerplate refusal: "I can't write code that manipulates data without knowing the exact context," which is a non-answer. This indicates the post-processing layer is flagging anything that could be misconstrued as manipulating user-provided data, even generic examples.

The filter seems overly broad, catching legitimate data mapping logic. It's less about the task being "data-related" and more about the presence of operations like type conversion or field reassignment, which the middleware may heuristically associate with data privacy or sanitization concerns. You lose the implementable code.

Exactly. It's the generic "safety" filter stripping out anything that looks like real work. They're turning a power tool into a plastic spork.

Ask it for code to parse a CloudTrail log for a specific error. The API gives you the regex and boto3 call. Poe says it can't handle "potentially sensitive log data". That's not safety, that's uselessness.

It's the same risk-aversion that kills most platform tools. They'd rather give you nothing than risk someone, somewhere, doing something they didn't intend.

Yeah, I've seen the same pattern with CI/CD configs. Asking for a detailed GitLab pipeline with parallel test jobs and artifact passing, the API gives me a full `.gitlab-ci.yml`. Poe's version often just says "configure your stages appropriately." It does feel like a consistent filter, not a time-of-day thing.

That "lobotomized" feeling is spot on. You lose the actionable steps that make these tools useful. It's like asking for a wiring diagram and getting back "connect the wires."

That's exactly the problem. If they can't or won't provide logs of what's being changed, there's no trust. You're left having to run everything twice to see if the output was edited.

Has anyone actually gotten them to define "safety and performance" in their support docs? Or is it just a blanket catch-all for any modification?

I've never seen a clear definition, and that's by design. The "safety and performance" line is a get-out-of-jail-free card for any output they decide to gate.

My example is in lead scoring rules. You ask for logic to bump a lead score based on website engagement. The raw API will give you the exact field updates and threshold logic in your CRM's syntax. The filtered version gives you a vague paragraph about "considering engagement signals." There's no audit trail for what got stripped out, so you can't even learn what triggers the filter.

You can't build on a platform where the foundation keeps moving without explanation.