GPT 5.5 Pro found two out of four cases that it got to before blowing its budget. Maybe it would have been the best of the bunch with infinite budget, but Opus 4.8, DeepSeek V4 Pro, and MiMo 2.5 Pro found four of nine of the bugs. Opus was an order of magnitude cheaper than GPT 5.5 Pro (and something like 30% cheaper than GPT 5.5), DeepSeek and MiMo were two orders of magnitude cheaper at roughly a dime per case.

GPT Pro also chews a lot and a long time, relatively speaking.

I can't come up with a use case where I can rationally spend ~31 times what Opus costs to use GPT 5.5 Pro, and I won't be doing any more benchmarking with it.

Given how much token costs are becoming an issue people talk about, the fact that there are models that cost dramatically less than the big American providers is going to be an issue for Anthropic and OpenAI. I'm happy to pay a premium (within reason) for the best model for interactive coding, but for API use, where having the model repeat it itself, compare against other models, have models judge other models work, etc. is not time-consuming for a human and is just a matter of implementing the harnesses and framework for proving correctness, I can't come up with a reason to spend ten or two hundred times as much as DeepSeek.

The article reads like thin, auto-generated ai clickbait for nerd sniping or shilling a model.

Consider the lead:

> DeepSeek V4 Pro wins this head-to-head by being more exact where it matters: following instructions, matching schemas, and solving edge cases cleanly. GPT-5.5 Pro is still strong, but it gave away points with avoidable deviations.

“where it matters”, “cleanly”, “is still strong”, and vague references instead of telling 3 out of 4 tests Deepseek yielded more concise results.

1 star.

The best valuable part of DeepSeek V4 pro is its low price, I don't expect have much better performance than GPT-5.5, even it's just the performance like gpt-5.4, it's still a good model.

I only hit the 5 hour limit every few days and the weekly limit a day or two before it resets at the most aggressive. I wouldn’t expect my usage to increase dramatically, other than not being stopped by limits.

I’m still apprehensive about shipping all my stuff off to a lab under an adversarial government (to the US), so not just looking at this from a pure cost basis, but my question is from the cost lens at the moment.

An AI generated article about single ai run test which in theory had many components and the AI judge declared deepseek "won"?

How many runs were there on each test to account for some temperature variance? Only one.

Did deepseek write better code? Did GPT's code have bugs when doing the regex? The AI "news" article doesn't actually say that. It says that grok thought that GPT's approach could have bugs so it declared deep seek the winner.

This is absolute worthless methodology. And barely measurable methodology - nothing more than a prompt. No definition of what the scoring approach actually is. No definition of what "precision" actually means in this context. This is absolutely worthless and has no business being in the site, forget about on the front page.

So why is it's on the front page? Because it aligns with the current "feels" of the community that deepseek will get better and it shows "bad things" about the en vogue to dislike closed models.

I happen to agree with both of the views, but this site is utterly worthless.

If you want HN to be astro-turfed to the max, just up vote content like this without any critical reading of the.

I mean the past 6 months of "here is my chat gpt blog post of how to use a coding agent" are 1000x better than this "news article".

Seriously the amount of respect I've lost recently for the HN community is incredible. A bit harsh, but very true.

Maybe it's generational thing, maybe it's due to the state of politics, maybe it's a side effect of me getting older, but recently online has turned into nothing but people explicitly (or implicitly) writing about their "team". Comments on this post are nothing but people who clearly see themselves as being on "team deepseek" or "team open models" or some similar variant writing posts in support even though this is probably one of the worst "articles" to make it to the front page on ages.

It clearly doesn't matter. It supports something on their "team" so they support it via comments.

If kills any form of intellectual discussion. It's all just "this is my team".

> We ran 4 fresh text tasks, generated on the fly for this matchup so neither model could prepare in advance, and had grok-4-1-fast-non-reasoning score each one. DeepSeek: DeepSeek V4 Pro scored 38.0 to OpenAI: GPT-5.5 Pro's 33.0.

I don’t know what it is specifically, but my weak human pattern-matching skills find this kind of language increasingly revolting. I don’t know why it is revolting, per se. It’s just the feeling I get.

Of course, me saying this on HN will get incorporated into GPT-5.6.175 or Claude 4.93 and it will make some version that just moves the revolting frontier elsewhere…

As usual, different models get stuck on different things. I run DeepSeek v4 API for most of my Cursor experimentation / poking around / proof of concept stuff, but I trust it less than OpenAI/Claude for writing production code. Sometimes DeepSeek is great for debugging, planning, etc. Sometimes it gets stuck or outputs low quality. That's true of OpenAI and Anthropic models as well though.

Overall, DeepSeek seems serviceable but a rung below Opus 4.8 and GPT 5.5. I run them all on maximum thinking settings.

Hardest stuff i threw at it... i did like a set of 3 each for claude/gpt/ds, it was all pretty steady across all providers. I think claude won but it could have just been it rng'd into the 3 easier tasks, they are all similar tasks but not identical, these aren't like benchmark tasks just a steady flow of annoying html/json/regex type stuff. Almost always they need a second pass regardless of what model i throw at it, just to tighten up some loose ends, and it fit right into what my current expectation was of gpt 5.5 and opus 4.6.

It's maybe not quite as knowledgeable as the most expensive American models and maybe makes more mistakes (just a feeling based off of vibes, don't take my word for it), so you need to constrain its scope more. That suits my workflow, half the time I have it generate code in the chat window and then write it myself, and I'm mostly using it at the level of generating function bodies and stuff, not entire features. Although it is writing a lot of SwiftUI without me really knowing the language and doing a fine job as far as I can tell (which isn't much admittedly).

One benefit I don't see talked about is it's speed - it's really quick, doesn't spend too much time reasoning even on "max", and the flash model is pretty dang good too. This lets me get into "flow state" when I'm writing code, compared to my experiences with Codex and Opus which would take minutes to complete even basic tasks and kind of ruined my focus.

It's so cheap though, you could download a different harness (Crush, OpenCode, Pi etc) and load $5 in credits and test it for yourself.

if it's 99.9% comparable performance for less money I'm interested, but I'm skeptical it's there