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.
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.
> With $3.88 & 690,003,591 tokens and 5 hours, Deepseek Pro & Flash combined, managed to reverse engineer Teamspeak's Licensing System for 3.13.8 (latest of post)
https://www.reddit.com/r/DeepSeek/comments/1txcfrh/with_388_...
This is some of the funniest stuff I've read in a while
And nice to see the cheap models doing so well.
I don't know whether models are over fitted to benchmarks and people take them at face value, but I spend less on DS4 apis than I do for Claude Code 100$ subscription and I code everyday. So far I'm quite happy with the results.
Besides the (quite true) joke, if sending data to DeepSeek is a concern the good thing is that the models are open weight, you can self host them or use third party providers.
Though, I added Mistral's latest model to the mix in the hope that some European model could be a contender, but it failed completely. I don't know if it hit safety guardrails or is just not competent at security work, but it scored 0/9. No errors, it returned the empty JSON set it was supposed to return if it didn't find anything. But, there were plenty of real bugs to find, and some very small self-hosted models found at least some of them.
I don't think that the country matters, whoever you send data to among these AI labs you are at security risk and data risk.
I was concerned I would need to do something specific in my dumb agent harness to make caching effective, since I'd read Anthropic's reason for forcing people to use Claude Code in order to use the rolling token usage limits on a subscription was because they could control cache behavior more effectively, but DeepSeek seems to be able to handle caching very effectively for raw API calls.
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.
export ANTHROPIC_BASE_URL=https://api.deepseek.com/anthropic
export ANTHROPIC_AUTH_TOKEN= *** PUT YOUR DEEPSEEK KEY HERE ***
export ANTHROPIC_MODEL=deepseek-v4-pro
export ANTHROPIC_DEFAULT_OPUS_MODEL=deepseek-v4-pro
export ANTHROPIC_DEFAULT_SONNET_MODEL=deepseek-v4-pro
export ANTHROPIC_DEFAULT_HAIKU_MODEL=deepseek-v4-flash
export CLAUDE_CODE_SUBAGENT_MODEL=deepseek-v4-flash
export CLAUDE_CODE_EFFORT_LEVEL=max
> I’m still apprehensive about shipping all my stuff off to a lab under an adversarial government (to the US)
Do you mean you don't want to use the models created by a non-US lab? In that case, yes you're stuck with US models, but there's a half dozen big labs in the US. If you meant just where your inference is done, there are providers in 12 different countries through OpenRouter, including the US. Several subscription providers host in multiple countries. There's a lot of choices.
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.
https://artificialanalysis.ai/evaluations/omniscience
Esp check the Hallucination rate for Deepseek - it's not good.
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
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.
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".
... and I believe which is happening. I've been advocating for DeepSeek V4 Pro and no one paid me. It's almost too good to be true.
We've been using it for async "heartbeat" processing and sms replies, but it's just too slow for live chat replies (which is a shame, as I'd really love to use it there).
Very capable model, but also very slow.
> 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.
Requests to grok-4-1-fast-non-reasoning now silently route to grok-4.3 (a 5x more expensive model), with reasoning set to "none".
https://docs.x.ai/developers/migration/may-15-retirement
TFA was published today, which implies grok-4.3 was used.
Hopefully this dynamic continues long enough to make local/private inference the leading solution for coding.
As for other segments, high API pricing gets people to switch to the subscriptions instead which is stickier than the API.
So it doesn't surprise me at all that the methodology is weak, too.
1. MoE (nothing new here, but, this helps a lot)
2. Compressed Attention Mechanisms (this is their core innovation) - this dramatically reduces the Key-Value (KV) cache requirements for longer contexts
Another thing that helps is significantly lower energy costs in China.
Another point from my own guess: they are running (some percentage) the inference on their own home-grown AI inference chips.
My guess is that they do aggressive caching / some proprietary optimizations in their hosting setup that they haven't published. Maybe also running at loss to gain market share.
And judging from latency / network performance, I don't think what you access, when you access deepseek.com from Europe, is hosted in China.
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…
"Harry finally had control of the broom. Draco was dead in his sights. The matchup feels earned."
Also, which SOTA western models are you comparing it with? Just to give more flavor.
1. DS4Pro: around opus 4.5
2. DS4Flash: around sonnet 4
3. Mimo v2.5 pro: between opus 4.5 and opus 4.6.
4. minimax M3: around opus 4.6
All of these are very close in terms of quality and pricing. For anything that is not specifically related to coding, DS4Flash has become ny de-factor model. It just works... super fast, tool calling is perfect, and the price is unbeatable. Caching is out of the world. Im now regularly hitting 90%+.