Posted by dnhkng 17 hours ago
Probably not.
But Opencode-Go is a great solution for those who don't want to pay DeepSeek directly (or can't due to reasons)
Selfish referral code: https://opencode.ai/go?ref=R1AJZT4VBX
Otherwise, if you're trying to run it locally, even really low quantizations like DeepSeek-V4-Flash-IQ2XXS-w2Q2K-AProjQ8-SExpQ8-OutQ8-chat-v2-imatrix seem to actually not be so dumb compared to smaller models with same quantization, might be worth a try if you're sitting on a lot of RAM/VRAM yet not industry-scale amount :)
3rd party providers on OpenRouter can be cheaper but it's already so cheap.
https://opencode.ai/docs/go/#usage-limits
Previously, OpenCode Go had higher API prices for some models, but now they lowered the API price and simultaneously reduced the allowance.
GPT 5.6 Luna is a new model in Go since I last checked, for example.
I still find this today:
> The plan is designed primarily for international users and provides stable global access. Your data will not be used for model training.
> because we added the new deepseek which we do not yet have a ZDR with we cannot blanket say we offer ZDR
I wonder how the website can make the statement that data will not be used for training.
Played around for a few hours and used up 80 cents of tokens.
Providers' cache hit stats are available to consult, and only 1-2 of them behave properly if I remember correctly, zero if you request providers that don't store and train on sessions.
Most of the times, the total cost, including search API's, is less than $0.05 for full deeply researched output, and the research is actually good.
At the speed Flash has on most providers, it doesn't really turn into a latency concern.
QA analysis of voice transcriptions. Napkin math: we operate at 2-5% of the cost of running on Equiv Frontier, though this changes near-weekly because pricing is so volatile.
It took us about a month to get the inference configured to achieve these numbers. But if you can get your hands on a pair of B300 GPUs and the context works, it's untouchable for price/performance.
(B200 would work, but you don't have the B300's memory, which lets you run it on 2xGPU instead of 4xGPU... with Dspark, it's like magic)
On a side note, for tasks that don't require the intelligence of DS v4 flash, we're using Nemotron-3-super with incredible success. I'm shocked we're not seeing more adoption of this model, given how easy it is to fine-tune and how blisteringly fast the nvfp4 version is. (A single B200 GPU can produce an insane amount of throughput with Nemotron 3 Super.)
Why not call it V4.1?
edit: nope, at least deepseek kept "deepseek-v4-flash" and just updated model underneath. I guess preview is no longer worth serving with that release and you'd have to look through inference provider docs to see if they've updated, yeah..
The `-0731` style suffix is worse compared to a proper version bump like V4.1.
https://news.ycombinator.com/item?id=49082022#49087112
How is that important? Maybe it does, so what?
Some providers like OpenRouter now call it `deepseek-v4-flash-0731`, but even in places like here on HackerNews people say things like "Sonnet is better than DeepSeek" without specifying a version or a reasoning effort, certainly no one will mention that `-0731` suffix when talking about DeepSeek V4 Flash.
The only issue i see, is 3th party providers that have not yet updated. But that is going to be a short time periode. There is no reason to not update.