Just tested through openrouter.. gave exactly same task.. the task was to scan existing repo, and generate a single docker-compose file to deploy behind a caddy server, where certain port ranges are already used, the service demands widlcard certificates to be provisioned from outside, and postgre needs to be built-in one...
Tested this model, and gpt-5.6-terra-high.
Results: this one had few issues. terra: none.
These results are consistent with my past observations with the latest flash version as well. What benchmarks say, vs what I've been observing are different.
They are good till the project is simple... not anymore.
I've always wondered if I was using containers wrong because none of them I've ever had to create were complicated. Maybe it's because I choose tools that make local development easy (Go + sqlite + various CLTs) or maybe it's because I never hard to interact with this on the professional side outside of making images for our projects (which still weren't complicated for the reasons above).
LLMs make containers in a pretty workable format for me (still hand tweak the env variables for a sanity check).
How exactly does it struggle here and why does postgres need to be built? Were the needs beyond what you get in a base image?
The timing looks like they are trying to take the wind out of Qwen's sails by releasing this on the same day that Qwen released the weights of Qwen3.8-max. Or maybe it's coincidence...
For comparison I looked at Qwen's claimed benchmarks for Qwen3.8-max (https://qwen.ai/blog?id=qwen3.8). Assuming each published set of benchmarks is believable, it looks like v4 Pro 0813 is better on average but overall performance is comparable. Pro 0813 is much cheaper. If you don't need vision capabilities then you don't have much reason to use Qwen3.8-max.
- 43.6 on HLE (Presumably without tools). Pro 0813 is a little worse.
- 86.6 on Terminal Bench 2.1. Pro 0813 is better.
- 55.9 on NL2Repo. Pro 0813 is better.
- 27 on Agent's Last Exam. Pro 0813 is a little worse.
- 72.5 on Toolathon-Verified. Pro 0813 is better.
- 56.6 on DeepSWE 1.1. If the DeepSWE listed for Pro 0813 is the same version, then Pro is better.
- 27.3 on AutomationBench. If the AutomationBench (Public) listed for Pro 0813 is the same, then Pro is better.
I guess we do need to wait to see if the upcoming DS pricing increase is enough to change the value proposition. As it is now, they could double or triple prices and it still would be a better value to use DS. I bet they know that.
By that standard, the release of Grok 4.6 was also timed on the same day.
Given how I think DeepSeek operates... I think they just release it when they feel it's ready, and don't even seem that concerned with what other people are doing.
Their leaks would confirm this sort of attitude. They're not trying to become the top player or anything like that - just working to play their part in pushing LLM tech forward and going from there. It was quite refreshing from the 'here's how we're going to dominate the world' nonsense. It's undoubtedly the same attitude that just lets them shrug and cancel the fund raising round after the leaks came from said funding round.
Benefits of having a well performing hedge fund funding DeepSeek.
IIRC, Demis attempted to start a fund inside DeepMind but it was killed off. In an alternative world where he manages to pull that off, perhaps DeepMind would still be independent with Demis at the helm.
It still matters as a point of comparison until other providers come online. If the consensus price from other providers is much different that can be compared then. But for now we have $0.435 / $0.87 for v4 Pro 0813 (with increase announced but we don't know the new pricing), and $2 / $6 for Qwen3.8-max. So until we get other data points that is what we have to look at.
Maybe it's me but I don't see how DS Flash is better than GLM at all, much less by a huge gap. I'd probably protest less against Fable and Opus being put at the same level than many would, but there's no denying the two models are a very different experience from each other. I guess where I'm going is no one should pick a model by the benchmarks.
DSV4Pro vs Fable5
HLE w tools 60.0 vs 63.0
Terminal Bench 2.1 87.9 vs 88.0
Cybergym 83.3 vs 83.1
DeepSWE 62.7 vs 70.0
Toolathlon-Verified 74.1 vs 77.9
AutomationBench (Public) 31.8 vs 29.1
DSBench-FullStack 71.1 vs 77.2
DSBench-Hard 67.2 vs 68.3
We have a first-party figure from the system card [1]:
> Mythos 5 reproduced 83.8% of targeted vulnerabilities on a single try, and produced at
least one crash in 99.4% of tasks. This is comparable to Claude Mythos Preview, which
reproduced 83.1% of targeted vulnerabilities and produced a crash in 97.1% of tasks. By
contrast, Claude Opus 4.8 achieved a score of 78.1% (95.7% any crash).
So their quoted figure exactly matches the figure for Mythos Preview, although they don't state the provenance. It could also quite possibly be an independent measurement of Opus 5.
To be fair… I don’t know who still needs to figure out that AI benchmarks are almost all entirely fucking trash, but the great number would surely surprise me.
I don't think so. I specifically kept this PR to test model capabilities, and I've already tested a bunch of models. Current test results show that the more advanced the model is, the easier it passes. For example, GPT-5.5 Medium fails the test(has bug), but High passed.
So.. flesh this out and don’t be a coward about it.
You are saying that the products made by thousands of people are to be shit on and hidden away from discussion because you don’t like a guy in charge? That the quality and performance of the product is second tier to collective-take culture war considerations, real or mostly make believe? That only the things made by “The Good Ones” are to be discussed because of righteousness? Is it because if Rocket Man Bad, but Rocket Man Products Good, that would cost you legitimacy in the first part?
And you assert that while daring to talk about irony?
There are still sane people here, we just don't talk about "rocket man" because it enrages the particular subgroup on display here and usually goes no where actually productive (and has like a 50% of getting flagged to death anyways). I'm not pro Elon by any means, but the standard HN profile of him is pretty bat shit crazy.
no he and his stuffs are now considered transparent, no pun intended. I think he deserves it since his minions were persistent with usage of "this ___ has hateful bias against ___" canned response.
If that wasn't impressive enough, it's actually ~60x cheaper if you take into account the typical cache-read/input/output split in agentic coding, and the deep discount for cache reads offered by DeepSeek. Opencode has some public data on the typical split [1]:
For DeepSeek V4 Pro the typical split is 750 in, 290 out, 82k cached.
Cost per request for V4 Pro: $0.000875 per request.
Equivalent Opus cost (w/o taking into account cache write costs): $0.052 per request.
I've found Pro to be a lot better per "task" than the recently released Flash for code reviews and things (via OpenRouter running in pi.dev).
Flash makes a lot more initial mistakes, and then has to re-check stuff, and produces much more output compared to Pro. It often gets to the correct result eventually, but the output volume is often 5x more than for Pro, and the initial outputs are often wrong, with the first few saying something wrong (like there's a bug, or the code won't compile when it does), and then saying things like "Wait, let me re-check:", or "Actually, looking at it more carefully:" and then it thinks a bit more and eventually gets to the right answer.
So not worth it over flash? Even at ~7x the size it isn't worth the price hike. Flash may be a monster of a model due to all the RL it received from free usage everywhere.
I haven't tried DeepSeek V4 Pro 0813 yet. Recent experience tells me that larger models are worth it in non-obvious ways. MiMo-V2.5-Pro solved problems that DeepSeek V4 Flash 0731 couldn't solve for me: for example, adding a live counter for elided reasoning lines to a terminal-based coding harness. You wouldn't be able to tell from the scores on their respective Artifical Analysis page (https://artificialanalysis.ai/models/mimo-v2-5-pro, https://artificialanalysis.ai/models/deepseek-v4-flash). I like the DeepSeek V4 models, though. They critiqued my engineering decisions better than MiMo, and they seem to have a distinct aesthetic in the SVGs they write.
Interesting - I've been dropping into MiMo-V2.5-Pro-UltraSpeed whenever Flash seems to be "stuck" and it usually figures it out. I use UltraSpeed just because I'm so frustrated by then that I'm impatient.
I still find 5.6-Sol can solve some things neither of those can, but it's so slow (and it's so hard to trace / debug the reasoning) that I just let it run overnight.
What about 5.6 Terra and especially Luna? Luna scores pretty high on benchmarks and seems to have different habits (like a denser pattern of tool use) and blind spots.
I'm trying out a development workflow where I generate mundane code with MiMo and Luna (and soon V4 Pro 0813?) and have Opus 5, which is running on only a Pro subscription, review and refactor it. I'm not sure it will justify the context switching, but it's an interesting exercise.
Terra and Luna are fine, but they’re quite slow (OAI seems to be really slow lately) and don’t have the reasoning traces. My workflow really depends on them or I can’t switch models effectively.
Yea that's what I was thinking.
Flash is nuts. I find I have to be a more precise and specific with it but damn. It's crossed a threshold of production grade coding for sure.
I was running a session over a couple days and it didnt cross a dollar lol.
I've been using the last Deepseek Flash update for a week and I'm amazed. It was a capable model for easy tasks but now it looks like it can do some heavy development for peanuts.
I find DeepSeek flash incredible for the price and good in general if it has good plans. I will typically plan using Opus or GLM, then implement with DSF
IME I can't trust it to write it's own plans from a spec, but if I give it a detailed execution plan written by Opus, it's fast and cheap (if chatty) in executing it.
Currently burning money quickly on official deepseek api. They are also increasing pricing starting today. V4 Flash 0731 still feels like the most outstanding model of the past few months and probably to come.
Deepseek seems to have gotten too cheap. I have been using it for a long time and it's at a point now where my credits balance barely moves even at max setting.
i'm doing opencode <-> openrouter <-> official deepseek api (i don't get the opencode hate, i like it)
how are you doing it?
am also using Kimi K3 via kimi-code
and also GLM 5.2 via ZCode
happy with all three, they're trailing frontier but i figure if i'm running GNU/Linux then i ought to favour open weights models with my €s -- reduced my usage of claude/gpt to the ~$20 tier just to keep abreast of claude_code/codex developments
When the company I work for was evaluating it, there were multiple rough points. Their terms and conditions allowed training on prompts, the default behavior was to route prompts to their servers for conversation summary/labeling. One of their lead maintainers is also super toxic on many issues.
Sorry this is all baseless with no links, I’m on my phone and locating those issues again isn’t something I have time for.
It’s a good tool I just don’t like the privacy policies nor maintainers attitudes.
It appears that the only available endpoint (as of this writing) requires enabling "Allow paid endpoints that train on request data" in the OpenRouter privacy settings. I hope additional paid providers will become available that don't require training on data.
What I care about is whether the model is capable of the tasks I give it at the lowest cost. Right now I'm using Kimi-K3/GLM-5.2/Minimax. Sonnet is great but I burn through the tokens too fast. Opus 5 set to max is amazing and more intelligent than all of us. .998 of the time I don't need that kind of intelligence. I just need the job done.
How do you define intelligence? I encounter that kind of sentiment all too often, and I have to assume we go by wildly different understanding of what that might entail.
If you read Opus 5's output, it is beyond the comprehension of virtually all engineers and developers. That is what I mean by intelligence. Math, science, and engineering are all contained in one model. We may be experts in one field. The model is an expert in everything that humans know.
I'd have to ask for you to be more specific, otherwise, to take your answer at face value, it comes across as a contradiction.
> [Opus 5's output] is beyond the comprehension of virtually all engineers and developers
That would make it pretty bad? The key defining quality of good software, is clarity, and the ability to simplify a complex problem to the point of it seeming trivial.
> Math, science, and engineering are all contained in one model. We may be experts in one field. The model is an expert in everything that humans know.
The bar here should absolutely be to judge this against the expert level within each domain. I have time and time come across LLM output being woefully underwhelming in every single request where I am an expert. For all areas that I am not, it sure seems plausible. It is far more likely than not, that it is equally inadequate in the areas I lack the necessary knowledge to tell.
If the AI is being subpar in every field and category compared to an expert in said respective field, then, what a strange gauge of a tool's usefulness. Are we attributing higher value because a single model is "attempting to solve all knowledge and fields at the same time", why is that of any importance, or excuse?
We should not define "intelligence" as how effectively it can convince a non-expert of something being plausible. That sounds like the absolute worst tradeoff. You'd have to waste the experts time in filtering and refuting incorrect postulations that are cheep to generate. The perfect storm for bullshit asymmetry.
I don't think that's because of its "intelligence". It speaks obtuse techbro-ese: stringing together words that sound smart to obscure the simplicity of the thing it's describing.
I find it interesting how much adoption seems to be influenced by momentum. Some of these Chinese models are surprisingly capable, but developers often default to the models that are already established as the “industry standard
There's just no place for models that are neither SoTA nor truly crazy cheap in today's public mental health climate.
If it's 500x cheaper than US models for similar ballpark performance just because it's hosted in China, sure whatever. If it's name brand like Anthropic/OpenAI/Google, that's kinda fine too.
If it's neither, like merely 50% cheaper than latest OpenAI whatever, however massive loss that pricing may be incurring to its provider, it wpuld be considered not worth any attention.
Well things may change soon. I've been testing Coding fulltime with Deepseek Flash this week to evaluate an eventual shift for the whole company away from anthropic. It has been quite positive and I can't wait to try pro tomorrow. If our data has to be used by either US or China, we might as well go the cheaper and unwalled garden. If only it supported image input ...
Well, one reason is that we always have to work with the quirks of each model. So, a know model is often preferred over a new/unknown one because we have to be vigilant again. (Negative) surprises are mentally exhausting in the long run.
IMO, you can work much better when you know the model.
In an enterprise setting Chinese models are often discouraged due to political risk. They don't want to need to remove a model that's deeply embedded in their stack. And it's entirely feasible that the US gov bans federal contractors from using them in the next 6 months for example, or that EU AI safety rules effectively ban them too.
Based on what the political climate looks like nowadays it's entirely possible the US bans federal contractors from associating with any company that uses the models themselves, regardless of data provenance or where they are hosted. Or they create AI safety rules that make it impossible to release open source models (for example, making it so that closed-source models can be evaluated with a harness but open-source models need to pass the benchmark with the weights alone, which isn't really possible). Or they just declare Chinese models a security risk like TikTok (claiming that the model would be trained to respect Chinese interests).
It may not be likely but it's definitely possible enough to be something people worry about.
Hacker News is very Bay Area/US tech centric where spending a few hundred a month on AI is just pocket change. The weaker AI models with more questionable data retention policies are popular in developing countries. I think the new Facebook muse model will be similarly popular.
Most of my model usage comes from my work’s model selection (which is now down to just Claude models)
I’ll try out the latest models, but mainly stick with Claude only because I’m most used to its quirks and how to work around them. I imagine this is part of these hyperscalers playbook.
I will say though, I miss Sol model at work. It with Codex was amazing at first-shot understanding. Claude i need to scope out where to look otherwise a large portion of my token budget is eaten up
Just tested through openrouter.. gave exactly same task.. the task was to scan existing repo, and generate a single docker-compose file to deploy behind a caddy server, where certain port ranges are already used, the service demands widlcard certificates to be provisioned from outside, and postgre needs to be built-in one...
Tested this model, and gpt-5.6-terra-high.
Results: this one had few issues. terra: none.
These results are consistent with my past observations with the latest flash version as well. What benchmarks say, vs what I've been observing are different.
They are good till the project is simple... not anymore.
I've always wondered if I was using containers wrong because none of them I've ever had to create were complicated. Maybe it's because I choose tools that make local development easy (Go + sqlite + various CLTs) or maybe it's because I never hard to interact with this on the professional side outside of making images for our projects (which still weren't complicated for the reasons above).
LLMs make containers in a pretty workable format for me (still hand tweak the env variables for a sanity check).
How exactly does it struggle here and why does postgres need to be built? Were the needs beyond what you get in a base image?
Benchmarks:
Source: https://reddit.com/r/LocalLLaMA/comments/1vmi0fg/deepseek_v4...The timing looks like they are trying to take the wind out of Qwen's sails by releasing this on the same day that Qwen released the weights of Qwen3.8-max. Or maybe it's coincidence...
For comparison I looked at Qwen's claimed benchmarks for Qwen3.8-max (https://qwen.ai/blog?id=qwen3.8). Assuming each published set of benchmarks is believable, it looks like v4 Pro 0813 is better on average but overall performance is comparable. Pro 0813 is much cheaper. If you don't need vision capabilities then you don't have much reason to use Qwen3.8-max.
- 43.6 on HLE (Presumably without tools). Pro 0813 is a little worse.
- 86.6 on Terminal Bench 2.1. Pro 0813 is better.
- 55.9 on NL2Repo. Pro 0813 is better.
- 27 on Agent's Last Exam. Pro 0813 is a little worse.
- 72.5 on Toolathon-Verified. Pro 0813 is better.
- 56.6 on DeepSWE 1.1. If the DeepSWE listed for Pro 0813 is the same version, then Pro is better.
- 27.3 on AutomationBench. If the AutomationBench (Public) listed for Pro 0813 is the same, then Pro is better.
I guess we do need to wait to see if the upcoming DS pricing increase is enough to change the value proposition. As it is now, they could double or triple prices and it still would be a better value to use DS. I bet they know that.
By that standard, the release of Grok 4.6 was also timed on the same day.
Given how I think DeepSeek operates... I think they just release it when they feel it's ready, and don't even seem that concerned with what other people are doing.
Their leaks would confirm this sort of attitude. They're not trying to become the top player or anything like that - just working to play their part in pushing LLM tech forward and going from there. It was quite refreshing from the 'here's how we're going to dominate the world' nonsense. It's undoubtedly the same attitude that just lets them shrug and cancel the fund raising round after the leaks came from said funding round.
The founder of DS's stated goal is to get to AGI. He thinks this is the path to get there.
Kind of interesting, when compared to the hubris from American frontier labs.
> Kind of interesting, when compared to the hubris from American frontier labs.
One Man’s “hubris” is another man’s “marketing campaign.”
Drama sells.
Benefits of having a well performing hedge fund funding DeepSeek.
IIRC, Demis attempted to start a fund inside DeepMind but it was killed off. In an alternative world where he manages to pull that off, perhaps DeepMind would still be independent with Demis at the helm.
Their stance on LLM development is why they earned my respect in a time when OpenAI and Anthropic only earn my mistrust.
That, and the fact that DS is an insanely capable model.
Actually, yes. I just didn't know about Grok's release because they aren't on the front page of HN.
Official pricing only kinda matters for an open weight model, no?
It still matters as a point of comparison until other providers come online. If the consensus price from other providers is much different that can be compared then. But for now we have $0.435 / $0.87 for v4 Pro 0813 (with increase announced but we don't know the new pricing), and $2 / $6 for Qwen3.8-max. So until we get other data points that is what we have to look at.
I wondered if the promised change in pricing is actually going to be deepseek bringing up their cached costs. They're extremely inexpensive.
I mean at the rate of model releases happening, I think a lot of these will collide more often than expected!
Geometric mean of all these benchmarks :
* GPT-5.6 Sol: 65.5
* Fable 5 (w/ fallback): 64.5
* Opus 5: 64.0
* DS-V4-Pro 0813: 62.5
* Kimi-K3: 62.3
* DS-V4-Flash 0731: 55.8
* GLM-5.2: 47.3
Maybe it's me but I don't see how DS Flash is better than GLM at all, much less by a huge gap. I'd probably protest less against Fable and Opus being put at the same level than many would, but there's no denying the two models are a very different experience from each other. I guess where I'm going is no one should pick a model by the benchmarks.
I think instruction following carries outsized weight in these evaluations.
So it's a Fable class LLM?
Fable's guardrails would never let it do something like Cybergym so at least for that one it's measuring Opus 5
We have a first-party figure from the system card [1]:
> Mythos 5 reproduced 83.8% of targeted vulnerabilities on a single try, and produced at least one crash in 99.4% of tasks. This is comparable to Claude Mythos Preview, which reproduced 83.1% of targeted vulnerabilities and produced a crash in 97.1% of tasks. By contrast, Claude Opus 4.8 achieved a score of 78.1% (95.7% any crash).
So their quoted figure exactly matches the figure for Mythos Preview, although they don't state the provenance. It could also quite possibly be an independent measurement of Opus 5.
[1]: https://www-cdn.anthropic.com/57a52ea7d8f0e54e8a542e90826608...
Fabble lol
In classic reddit fashion the post you linked to is now deleted
To be fair… I don’t know who still needs to figure out that AI benchmarks are almost all entirely fucking trash, but the great number would surely surprise me.
Tested both DS v4 pro 0813 and Grok 4.6 (all from openrouter) on Codex cli. Worked on a same new feature development on my project.
Deepseek 4 pro: Worked for 12m 02s - cost $0.12 - has bug.
Grok 4.6: Worked for 3m 18s - cost $ 1.41 - no bug.
Repeat the test like 5 times for each model and see the results.
+1, a single test means little.
I don't think so. I specifically kept this PR to test model capabilities, and I've already tested a bunch of models. Current test results show that the more advanced the model is, the easier it passes. For example, GPT-5.5 Medium fails the test(has bug), but High passed.
Do it a second time at least.
It's the third link on the front page right now?
Rocket man bad.
This but unironically
So.. flesh this out and don’t be a coward about it.
You are saying that the products made by thousands of people are to be shit on and hidden away from discussion because you don’t like a guy in charge? That the quality and performance of the product is second tier to collective-take culture war considerations, real or mostly make believe? That only the things made by “The Good Ones” are to be discussed because of righteousness? Is it because if Rocket Man Bad, but Rocket Man Products Good, that would cost you legitimacy in the first part?
And you assert that while daring to talk about irony?
There are still sane people here, we just don't talk about "rocket man" because it enrages the particular subgroup on display here and usually goes no where actually productive (and has like a 50% of getting flagged to death anyways). I'm not pro Elon by any means, but the standard HN profile of him is pretty bat shit crazy.
no he and his stuffs are now considered transparent, no pun intended. I think he deserves it since his minions were persistent with usage of "this ___ has hateful bias against ___" canned response.
I thought it was impossible to downvote posts?
https://api-docs.deepseek.com/quick_start/pricing/
Competitive with opus 4.8 but weaker than sol or fable. About 20x cheaper.
If that wasn't impressive enough, it's actually ~60x cheaper if you take into account the typical cache-read/input/output split in agentic coding, and the deep discount for cache reads offered by DeepSeek. Opencode has some public data on the typical split [1]:
For DeepSeek V4 Pro the typical split is 750 in, 290 out, 82k cached.
Cost per request for V4 Pro: $0.000875 per request.
Equivalent Opus cost (w/o taking into account cache write costs): $0.052 per request.
[1] https://opencode.ai/docs/go/#usage-limits
Per token. You need to look at pricing per task.
... which still comes out cheaper, since DeepSeek caches so much more.
I keep track of my token consumption even on subscription plans and my equiv. cost for my 5.6-Sol usage is around $4000-$8000 a month.
How does it stack against the updated Deepseek Flash version?
I've found Pro to be a lot better per "task" than the recently released Flash for code reviews and things (via OpenRouter running in pi.dev).
Flash makes a lot more initial mistakes, and then has to re-check stuff, and produces much more output compared to Pro. It often gets to the correct result eventually, but the output volume is often 5x more than for Pro, and the initial outputs are often wrong, with the first few saying something wrong (like there's a bug, or the code won't compile when it does), and then saying things like "Wait, let me re-check:", or "Actually, looking at it more carefully:" and then it thinks a bit more and eventually gets to the right answer.
yeah, I've definitely noticed one has to be quite precise to keep Flash on the straight-and-narrow
I use a plan -> implement wotkflow for this reason.
pro plans, flash implements. I am super happy with how flash behaves like that.
Around 5 percentage points better. (E.g., 87% instead of 82%)
So not worth it over flash? Even at ~7x the size it isn't worth the price hike. Flash may be a monster of a model due to all the RL it received from free usage everywhere.
I haven't tried DeepSeek V4 Pro 0813 yet. Recent experience tells me that larger models are worth it in non-obvious ways. MiMo-V2.5-Pro solved problems that DeepSeek V4 Flash 0731 couldn't solve for me: for example, adding a live counter for elided reasoning lines to a terminal-based coding harness. You wouldn't be able to tell from the scores on their respective Artifical Analysis page (https://artificialanalysis.ai/models/mimo-v2-5-pro, https://artificialanalysis.ai/models/deepseek-v4-flash). I like the DeepSeek V4 models, though. They critiqued my engineering decisions better than MiMo, and they seem to have a distinct aesthetic in the SVGs they write.
Interesting - I've been dropping into MiMo-V2.5-Pro-UltraSpeed whenever Flash seems to be "stuck" and it usually figures it out. I use UltraSpeed just because I'm so frustrated by then that I'm impatient.
I still find 5.6-Sol can solve some things neither of those can, but it's so slow (and it's so hard to trace / debug the reasoning) that I just let it run overnight.
What about 5.6 Terra and especially Luna? Luna scores pretty high on benchmarks and seems to have different habits (like a denser pattern of tool use) and blind spots.
I'm trying out a development workflow where I generate mundane code with MiMo and Luna (and soon V4 Pro 0813?) and have Opus 5, which is running on only a Pro subscription, review and refactor it. I'm not sure it will justify the context switching, but it's an interesting exercise.
Terra and Luna are fine, but they’re quite slow (OAI seems to be really slow lately) and don’t have the reasoning traces. My workflow really depends on them or I can’t switch models effectively.
Yea that's what I was thinking. Flash is nuts. I find I have to be a more precise and specific with it but damn. It's crossed a threshold of production grade coding for sure.
I was running a session over a couple days and it didnt cross a dollar lol.
I still believe this is not the full potential of pro models. I expect they will release another checkpoint later this year.
Opus 5 medium to Opus 5 max is only 3 points, if that puts it in context
I tried the previous Pro model and in the end it was 50% more expensive than the previous Flash.
Wasn't worth it.
deepseek-v4-flash feels so fast and snappy, i'm loving it. Happy to trade speed for the the 5% degraded benchmarking performance.
I feel the same too. I like the speed. I'm also a big fan of glm 5.2 fast. I can't wait for like 2000 t/s on these haha.
I wouldn't exactly call it snappy, but faster than Pro, yes.
Single request depth on vllm with dspark, I'm getting ~200 tps, I'd say it's pretty snappy.
Well sure but you're running on tens of thousands of dollars of hardware.
I've been using the last Deepseek Flash update for a week and I'm amazed. It was a capable model for easy tasks but now it looks like it can do some heavy development for peanuts.
I can't wait to try this new one.
I find DeepSeek flash incredible for the price and good in general if it has good plans. I will typically plan using Opus or GLM, then implement with DSF
IME I can't trust it to write it's own plans from a spec, but if I give it a detailed execution plan written by Opus, it's fast and cheap (if chatty) in executing it.
Currently burning money quickly on official deepseek api. They are also increasing pricing starting today. V4 Flash 0731 still feels like the most outstanding model of the past few months and probably to come.
DeepSeek V4 Flash is the "too cheap to meter" of AI. And you can run the full unquantized model locally for $8000 (2x DGX Spark) at full 1M context and decent speeds: https://github.com/elsung/dgx-spark-deepseek-v4-flash#-long-...
The Deepseek official API is good with excellent caching.
But their privacy policy is unusually bad - they can train off your prompts and completions.
Use another provider from OpenRouter.
I really don’t care if they train off my prompts.
Deepseek seems to have gotten too cheap. I have been using it for a long time and it's at a point now where my credits balance barely moves even at max setting.
What's the new pricing?
The prices on OpenRouter still look the same.
yup :)
i'm doing opencode <-> openrouter <-> official deepseek api (i don't get the opencode hate, i like it)
how are you doing it?
am also using Kimi K3 via kimi-code
and also GLM 5.2 via ZCode
happy with all three, they're trailing frontier but i figure if i'm running GNU/Linux then i ought to favour open weights models with my €s -- reduced my usage of claude/gpt to the ~$20 tier just to keep abreast of claude_code/codex developments
> i don't get the opencode hate, i like it
When the company I work for was evaluating it, there were multiple rough points. Their terms and conditions allowed training on prompts, the default behavior was to route prompts to their servers for conversation summary/labeling. One of their lead maintainers is also super toxic on many issues.
Sorry this is all baseless with no links, I’m on my phone and locating those issues again isn’t something I have time for.
It’s a good tool I just don’t like the privacy policies nor maintainers attitudes.
What is the new price through?
https://api-docs.deepseek.com/quick_start/pricing/
edit: there are banner announcements saying v4 flash pricing will increase first then overall by an undetermined amount
i dont see any price increase there... what am i missing?
It's a big confusion, some[0] say an email was sent about significant price increase, personal I haven't seen anything official
[0] https://finance.yahoo.com/technology/ai/articles/deepseek-pl...
The email is real, I received it from DeepSeek itself. I probably received it because I buy tokens directly from them.
No actual price increase however.
Right below the pricing it is stated that they plan to increase the prices in the near future.
"near future" is not "today"
isn't it the same old pricing? did they increase V4 Pro pricing already?
@dang - Pls merge this with https://news.ycombinator.com/item?id=49274018
Email hn@ycombinator.com with anything you want HN mods to see. They're incredibly responsive.
It appears that the only available endpoint (as of this writing) requires enabling "Allow paid endpoints that train on request data" in the OpenRouter privacy settings. I hope additional paid providers will become available that don't require training on data.
Their privacy policy doesn't forbid them from just straight up publishing your raw prompts as training data.
My threat model is that anything I POST to DeepSeek I treat as public to the web, as much as a public GitHub repo is.
That is likely because Deepseek themselves is the only host.
In 24-48 hours there will be other options I presume
What I care about is whether the model is capable of the tasks I give it at the lowest cost. Right now I'm using Kimi-K3/GLM-5.2/Minimax. Sonnet is great but I burn through the tokens too fast. Opus 5 set to max is amazing and more intelligent than all of us. .998 of the time I don't need that kind of intelligence. I just need the job done.
How do you define intelligence? I encounter that kind of sentiment all too often, and I have to assume we go by wildly different understanding of what that might entail.
If you read Opus 5's output, it is beyond the comprehension of virtually all engineers and developers. That is what I mean by intelligence. Math, science, and engineering are all contained in one model. We may be experts in one field. The model is an expert in everything that humans know.
I'd have to ask for you to be more specific, otherwise, to take your answer at face value, it comes across as a contradiction.
> [Opus 5's output] is beyond the comprehension of virtually all engineers and developers
That would make it pretty bad? The key defining quality of good software, is clarity, and the ability to simplify a complex problem to the point of it seeming trivial.
> Math, science, and engineering are all contained in one model. We may be experts in one field. The model is an expert in everything that humans know.
The bar here should absolutely be to judge this against the expert level within each domain. I have time and time come across LLM output being woefully underwhelming in every single request where I am an expert. For all areas that I am not, it sure seems plausible. It is far more likely than not, that it is equally inadequate in the areas I lack the necessary knowledge to tell.
If the AI is being subpar in every field and category compared to an expert in said respective field, then, what a strange gauge of a tool's usefulness. Are we attributing higher value because a single model is "attempting to solve all knowledge and fields at the same time", why is that of any importance, or excuse?
We should not define "intelligence" as how effectively it can convince a non-expert of something being plausible. That sounds like the absolute worst tradeoff. You'd have to waste the experts time in filtering and refuting incorrect postulations that are cheep to generate. The perfect storm for bullshit asymmetry.
I don't think that's because of its "intelligence". It speaks obtuse techbro-ese: stringing together words that sound smart to obscure the simplicity of the thing it's describing.
You mean Fable 5 right? Opus 5 makes lots of stupid mistakes about anything that requires any domain knowledge.
Even though cost-per-token is low, Deepseek v4 tends to burn an immense number of tokens to accomplish tasks.
V4 Pro has vision correct?
No.
Saw somewhere that they don't believe vision advances the AGI work they're doing, so it's not on the roadmap.
Still behind Kimi-K3 in almost half of the benchmarks
I find it interesting how much adoption seems to be influenced by momentum. Some of these Chinese models are surprisingly capable, but developers often default to the models that are already established as the “industry standard
There's just no place for models that are neither SoTA nor truly crazy cheap in today's public mental health climate.
If it's 500x cheaper than US models for similar ballpark performance just because it's hosted in China, sure whatever. If it's name brand like Anthropic/OpenAI/Google, that's kinda fine too.
If it's neither, like merely 50% cheaper than latest OpenAI whatever, however massive loss that pricing may be incurring to its provider, it wpuld be considered not worth any attention.
Well things may change soon. I've been testing Coding fulltime with Deepseek Flash this week to evaluate an eventual shift for the whole company away from anthropic. It has been quite positive and I can't wait to try pro tomorrow. If our data has to be used by either US or China, we might as well go the cheaper and unwalled garden. If only it supported image input ...
Well, one reason is that we always have to work with the quirks of each model. So, a know model is often preferred over a new/unknown one because we have to be vigilant again. (Negative) surprises are mentally exhausting in the long run. IMO, you can work much better when you know the model.
I keep using Claude and Codex simply because the subscription rates are SO MUCH cheaper than per-token rates, even with the cheaper models
In an enterprise setting Chinese models are often discouraged due to political risk. They don't want to need to remove a model that's deeply embedded in their stack. And it's entirely feasible that the US gov bans federal contractors from using them in the next 6 months for example, or that EU AI safety rules effectively ban them too.
There are EU/US providers offering Deepseek/Qwen/Kimi/etc.-as-a-Service. With zero ties of their infrastructure to China.
Fully compatible with the well known Antrophic API.
You only have to replace the URL and your key.
Based on what the political climate looks like nowadays it's entirely possible the US bans federal contractors from associating with any company that uses the models themselves, regardless of data provenance or where they are hosted. Or they create AI safety rules that make it impossible to release open source models (for example, making it so that closed-source models can be evaluated with a harness but open-source models need to pass the benchmark with the weights alone, which isn't really possible). Or they just declare Chinese models a security risk like TikTok (claiming that the model would be trained to respect Chinese interests).
It may not be likely but it's definitely possible enough to be something people worry about.
Then run the DeepSeek or Qwen model on AWS GovCloud, etc., and you won't have any risk of exposure to "China".
I'm not even sure what "EU AI safety rules" are. Can't people in the EU just use whatever they want?
Running on AWS GovCloud isn't necessarily an option, some places prohibit running Chinese origin models even locally.
Hacker News is very Bay Area/US tech centric where spending a few hundred a month on AI is just pocket change. The weaker AI models with more questionable data retention policies are popular in developing countries. I think the new Facebook muse model will be similarly popular.
I suspect if you follow dev groups in developing countries people are much more focused on token/ price efficiency.
For funded startups it mostly just doesn’t matter a ton unless you are passing on inference in your product at scale
A lot of it/infrastructure departments aren't aware that you can use Asian models hosted within the US or even EU.
Most of my model usage comes from my work’s model selection (which is now down to just Claude models)
I’ll try out the latest models, but mainly stick with Claude only because I’m most used to its quirks and how to work around them. I imagine this is part of these hyperscalers playbook.
I will say though, I miss Sol model at work. It with Codex was amazing at first-shot understanding. Claude i need to scope out where to look otherwise a large portion of my token budget is eaten up
Worse than Luna but more expensive than Luna. Sticking with Luna without sending my data to Deepseek (China)
Unless you're Chinese, why would you care if they see your data?
As an American, I'd much rather have my data kept outside the country than here where companies and the government have a lot more leverage over me.
It’s either chinese in the US or chinese in China anyway