DeepSeek发布v4.1 Flash，性能超越v4 Pro且更便宜
DeepSeek 发布 v4.1 Flash，更便宜且强于 v4 Pro
推荐理由
DeepSeek旗舰模型迭代，性能全面超越旧版Pro且大幅降价，直接影响API调用成本与选型策略，开发者需关注版本迁移风险。
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| DeepSeek launching v4.1 flash cheaper and more capable than v4 pro384 points by nickweb 8 hours ago | hide | past | favorite | 199 commentsDSeek plans to officially release the V4.1 Flash model around September 10, 2026 (Beijing Time). After extensive internal and external testing, V4.1 Flash has comprehensively surpassed V4 Pro across all key metrics, including performance, cost, speed, and task completion time. In keeping with our commitment to user responsibility, following the official launch of V4.1 Flash and prior to the release of V4.1 Pro, all requests to the Pro model will be routed to V4.1 Flash and billed at Flash's price. If you encounter any issues during your comparative testing between V4 Pro and V4.1 Flash, please do not hesitate to reach out to us with your feedback. Thank you for your support!We will adjust the pricing for the Flash series effective from 12:00 Beijing Time on September 10, 2026. During off-peak hours, the unit price will be $0.003 for input cache hits, $0.15 for input cache misses, and $0.6 for output. Peak-hour prices will be double the off-peak rates. Please plan your usage accordingly. help aftbit 5 hours ago | next [–] >In keeping with our commitment to user responsibility, following the official launch of V4.1 Flash and prior to the release of V4.1 Pro, all requests to the Pro model will be routed to V4.1 Flash and billed at Flash's pricePlease don't do this kind of thing. If a user has validated a workflow on V4 Pro, they might not want to suddenly start testing it in production on V4.1 Flash. Instead, keep V4 Pro around but deprecated for a defined period of time, then remove it.At least as open weights models, it's possible to use something like Together.ai or OpenRouter to run the V4 Pro model as long as other providers keep it up.replynolok 5 hours ago | parent | next [–] Usually I would very much agree with you, but those things are not deterministic so if that's an issue for you you're probably not making the right choices.replylkois 3 hours ago | root | parent | next [–] That's a narrow take. Non-deterministic doesn't mean random; workflows can be reasonably validated and consistent to some known degree.I work for an education department that serves a chatbot for students, and model changes go through painstaking content safety reviews. I initially assumed it's just a bunch of bureaucratic paranoia. But every other model upgrade has a measurably different adherence to the existing system prompts about not talking to the kids about sex and drugs and mental health issues.replynolok 3 hours ago | root | parent | next [–] I work in the same field for one of my company, in europe, and if you're not self hosting sorry but your worries are not something I can accept because models are very much not reliable on that front, let alone when you let the host decide HOW to serve a model (ressources allocated, different version of the same model, etc ...).I'm not being a d**, just saying, the problem you have is something that I have faced EXACTLY, and at least here it's not working until you host in house or remote but on raw hardware. Otherwise it keeps having subtle changes, and you will notice no LLM API providers has guarantees about these.replyfrde_me 3 hours ago | root | parent | next [–] It's not that you're a d*, it's just that you lack any kind of nuanceThere's a whole spectrum between self-hosting open weight models and having a cloud provider swap models from under youShould you self host a model if want to maximize predictability to the limit? Yes. Does that mean it's wrong for someone hitting a model on API to expect that it won't switch to a completely different model under the hood from one day to another? Probably not.replynolok 3 hours ago | root | parent | next [–] Sure, but if you're working in a field where that limitation is not "because you like it" but "because you have to" it doesn't matter. If you cannot assume it to be true, then you have to assume it isn't.replykristjansson 3 hours ago | root | parent | prev | next [–] The user cares about the distribution of outputs. That distribution is structurally determined by the distribution of inputs (i.e. prompts), the weights of the model, and (these days) the dynamics of the harness guiding successive generations.The only way to characterize whether a choice is 'right' is to characterize the output distribution (i.e. evals)! Changing the underlying weights necessarily invalidates whatever characterization may have been done. One may assert that one's harness regularizes outputs back toward the desirable distribution, or one may hope the different weights induce a sufficiently similar output distribution.But no, one should not be completely agnostic to the choice of weights just because there's some nondeterminism.replygcanyon 4 hours ago | root | parent | prev | next [–] They’re nondeterministic at a fine level, but can be “deterministic” at a more general level: e.g. you might know that one model will always return properly formatted json when asked. That might not be true of the replacement, even if it is in general “better” and cheaper.Just the risk of such a thing means regression testing every time you update the model, and you want to be able to run that testing on your schedule rather than having it forced on you.replypacketlost 4 hours ago | root | parent | next [–] > They’re nondeterministic at a fine level, but can be “deterministic” at a more general level: e.g. you might know that one model will always return properly formatted json when asked. That might not be true of the replacement, even if it is in general “better” and cheaper.This isn't true. Even Sol messes up JSON formatting for me on occasion.Do not delude yourself into thinking these things are reliable. They are not.replykamranjon 4 hours ago | root | parent | next [–] Is nobody using structured outputs? They use constrained decoding at the generation stage to ensure the probability of tokens that would break the format are set to 0. I kinda figured everyone was doing this at this point.replywongarsu 3 hours ago | root | parent | next [–] Most serious providers are now supporting structured outputs in a reasonable way for all model configs. But for example on ollama structured outputs are still incompatible with tool calling and with reasoningreplyZopieux 34 minutes ago | root | parent | next [–] Friends Don't Let Friends Use Ollama. https://sleepingrobots.com/dreams/stop-using-ollama/replynolok 3 hours ago | root | parent | prev | next [–] If you use a large enough volume, you will know this isn't fully reliable. You might get json, and it might not match what the model actually sent because the last layer cut it up to match what you want. At the end, not json, or json but not really matching what the model wanted, it's sort of the same issue: when you use them you HAVE to assume they can have a brain fart. That's fine, just code around it.replygcanyon 3 hours ago | root | parent | prev | next [–] We use structured output and To my knowledge it has never failed (millions of data points). There seem to be two classes of people: those doing productive work with LLMs, and those who only get replies insulting their mothers…replyvikramkr 1 hour ago | root | parent | prev | next [–] These models have specific behavioral characteristics trained into them from reinforcement learning and prompts optimized for one aren't guaranteed to transfer to the new generation. Think if the difference between gpt 5.4 and 5.5 and then 5.5 to 5.6 for example. 5.5 was "better" than 5.4 for struggled more across compaction boundaries and needed much more precise instructions before 5.6 sol recovered some of 5.4's ergonomics. All from the same lab but each model was trained with specif |
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