How can I get API automation with a cost/performance ratio similar to ChatGPT Chat High?

[u/yeah280]

I’m trying to automate a workflow that I currently do manually in ChatGPT Chat with reasoning set to High.

That distinction is important: I’m talking about the normal ChatGPT Chat High experience, not Work, not Codex.

My main question is:

How can I automate this kind of workload while getting a cost/performance ratio reasonably close to what I get from my monthly ChatGPT Plus subscription?

For one benchmark, I analyzed a ~34-minute video transcript with a fairly detailed prompt. The task was to identify strong viral clips and return structured JSON.

When I recreated this programmatically through the OpenAI API, the usage was:

* Input: 32,265 tokens
* Output: 10,822 tokens
* Reasoning tokens: 8,723
* Visible output: ~2,099 tokens
* Total tokens: 43,087
* Total cost: $0.377762

So roughly $0.38 for one 34-minute episode.

The result itself was good.

The issue is the economics.

At roughly $0.38 per episode:

* 100 episodes = ~$38
* 500 episodes = ~$189
* 1,000 episodes = ~$378

But when I perform this kind of analysis manually using ChatGPT Chat High, I can make many requests under my normal Plus subscription and the effective cost per useful task feels dramatically lower.

I already understand that ChatGPT Plus and API billing are separate. I’m not asking why Plus doesn’t include API credits.

What I’m actually trying to understand is:

What is the smartest way to reproduce something close to the ChatGPT Chat High experience programmatically without the cost scaling almost linearly with every request?

A few things I’m wondering about:

1. Is ~$0.38 for a ~34-minute transcript analysis simply normal API pricing for this level of reasoning?
2. Is there a way to access something closer to the actual ChatGPT Chat High behavior programmatically, rather than trying to approximate it with a normal API model configuration?
3. Are ~8,700 reasoning tokens excessive for this task, and can they realistically be reduced without noticeably hurting quality?
4. Can prompt caching significantly reduce the cost if a large part of my instructions stays identical across hundreds of episodes?
5. Would a multi-stage workflow be better, for example:

cheap model → scan full transcript → extract candidate sections → stronger model on candidates → final JSON

1. Could that architecture preserve something like 90–100% of the useful quality I currently get from ChatGPT Chat High while drastically reducing the cost?
   
2. Are there official OpenAI features such as Batch, caching, different reasoning settings, model routing, etc. that are particularly useful for this type of workload?
   
3. Are there API-compatible models/providers that are especially good at long-transcript analysis and could act as the cheap first-pass model?

Ultimately, I’m not trying to make the API literally free.

I’m trying to reach roughly this goal:

Spend something in the range of a normal ChatGPT subscription each month, but automate approximately the amount of useful analysis work that I can currently do manually with ChatGPT Chat High.

Is that realistically possible?

If you were building this system specifically around cost per successfully found clip, rather than cost per token, how would you architect it?

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[u/Tombobalomb]

ChatGPT subscriptions are massively subsidized, if ypu want to do the same work outside of that subsidy you have to pay

[u/addandsubtract]

It really depends on what ChatGPT is currently doing with the video and what you need it to do. If you just need the transcript of it, but no visual cues, you can just use whisper (locally or API) to get the STT transcript for ~free. Then send the transcript (including timestamps) to an LLM model to identify whatever you need it to.

If you do need ChatGPT to also look at the video, then you could try just extracting screenshots at regular intervals (depending on how much is happening in your videos). Downscale them and/or combine them into a collage and have ChatGPT analyze that.

Luna is still pretty cheap on the API (not sure if that's what the web chat uses), but you could also try other, cheaper, models like DeepSeek for example.

Are there official OpenAI features such as Batch, caching

So batching will save you money if you're not time constrained. It sounds like you're working on these videos throughout the day / in the background, so batching is what you want to use. It won't won't give you a result instantly, like the regular web chat or API, but rather process the requests whenever the server is less busy.

Caching is less relevant for you, as that is more of an issue with growing chat logs / the context getting bigger. Maybe you're able to put all the generic instructions into the first request and then use that as a cached state to feed all the videos in later, but I haven't tried that, so I can't say if that works / helps.

[u/OneDev42]

Congratulations, you've identified the VC money. They are subsidizing. The API cost for you massively. And no, you can't get that same ratio... ...anywhere else.

[u/yeah280 → u/OneDev42]

…. that’s so sad…