Why not also Qwen?

Xi going to shut down open-weighting of them in a matter of months. No one seriously doubts this. They will be too powerful and they will be gone.

CCP will be happy! Go on and share all your data with them..

Usable local AI has been here for a while, esp on say a 5090.

You can’t treat Qwen3.6 like its fable, but if you prompt precisely and specifically it’s a great executor.

I actually found it refreshing to use more of my brain for once, and actually have to think deeper about what I’m trying to do, and how to build it.

What are your goalposts? Depending on your requirements, there have been many moments of usable local AI. More recent ones were gpt-oss 120b and Qwen 3.6 27b.

Have you tried the one that was just released?

Have you completed the identity verification? It's much more lenient once you have

I got an account warning on OpenAI (waved after I complained) just because I was asking it how to root some >10 years old Android device.

Try to use DS platform directly - cheaper and better than openrouter, no subscription

Yes, gladly! I have not yet open-sourced my skills etc., but I can give some insight and share a couple here.

Review is a skill, as in, a SKILL.md with a folder full of references:

- SKILL.md: https://gist.github.com/lionkor/161525be858d1d75db4c13c0f093...

- references/output-contract.md: https://gist.github.com/lionkor/8c68e33becef7a21f8408c7dc119...

- references/review-lenses.md: https://gist.github.com/lionkor/0a8b080fe45306213efddf3ebb75...

- references/review-workflow.md: https://gist.github.com/lionkor/d2d374b133ceb7e3660bd530ee72...

- references/section-rules.md: https://gist.github.com/lionkor/8a9e503adc7fd3697410cf021f27...

I'm aware that almost all of this is prompt voodoo, and there's no guarantee for the review to find anything or everything, but making it a dedicated skill and thoroughly observing the output thinking, tool calls, and result, lets me adjust these over time and fill the weak spots with even more prompting.

I use this skill by simply telling the agent something like "Review the changes on the current branch against origin/main, take special care with backwards-incompatible changes to the public API" or something like that.

I use `pi` (pi.dev) with a subagents extension, so that I can ask the agent to invoke a subagent to do the review, on work that the agent did.

For models, I use the highest possible reasoning on whatever model I feel like makes sense, usually this is GPT-5.5 or deepseek flash/pro, depending on the confidentiality of the codebase, on the highest reasoning always (for reviews).

I've also had success with a review checklist, though it doesn't produce an easy to parse (for humans) output: https://gist.github.com/lionkor/054ac2cf241e0765eee2383f0dba...

This is why my review skill mandates a very strict output contract. I need the output to be very easy to parse, and the output contract I've specified there does that.

In general I let <whatever the latest model of OpenAI's ChatGPT is> author and review SKILL.md and similar large prompts, usually with a ruleset like this, which is a 1600 line research artifact from a long GPT 5.5 "Pro" research session on prompt engineering: https://gist.github.com/lionkor/71498794d0a7d72173fc58766f25...

Does the review catch all issues? Not at all. Does it catch, usually more than one, important issue, across large changesets? Absolutely, and that's the point! :)

Feel free to ask me any questions, I'm also happy to share more about my setup via email or add you or anyone else to my private repos with more of these.

I use pi, and, like the sibling comment, the caching ratio is fantastic. I work on C#, Rust, C, C++, shell scripting, and other areas.

I'm using pi and my caching is ~99%.

It hallucinates plenty, about the same as Codex models and all other LLMs! I review all code it writes, thoroughly, check the test coverage, write tests myself, have other models/chats cross-check the work with a review skill (which I've shared in another comment in this thread).

Usually unreviewed code only gets committed if I really don't care, like for one-off scripts, which I sandbox with github.com/lionkor/sbh or run as an unprivileged user.

Aside from what squidbeak already said, I have to say the quality varies a lot, and goes above the "good" threshold enough times that using these models is worth the time and effort. Compared to something like GPT-5.4 to GPT-5.6 Codex models, they cost 50-100x less (fifty to one hundred TIMES less), and can do most annoying or well-specified tasks just as well.

The main difference is that deepseek is bad at prose, and Codex models are much more eager to use tools provided to them (which is usually fine, they often make like 3 todos via tool calls for a simple one step task which is silly though), and deepseek in general benefits from good instructions more than GPT models maybe do.

Deepseek becomes much better if you give it tools for asking clarifying questions, doing self-review with subagents, and so on. The more tools it uses, the better the signal-to-noise ratio in its context, and the more consistent the output.

You must still review 100% of the code as if it's trying to sell you insurance.

An experienced software engineer (read his profile) praises the value he's found in Deepseek, and gives some real data showing how affordable that value is.

Then you, dakolli - out of generosity and minute-to-minute devotion to enlightenment - sacrifice time from your busy day to sit down (though perhaps that's been painful lately?) or stand up with your phone - and offer a profound, deeply thought-out counterpoint in the following form (and I'll paraphrase):

"Nah mate, it's shit. All LLMs are shit."

For doing what?

Recommendation? No. Just go with the passive-aggressive advice "let pi build it for you". :-)

To be more constructive, what I did (as an experiencd SWE but a complete noob to agentic coding): went to pi.dev's extension marketplace and looked into all the new shiny stuff. Subagents, mcps, context and memory optimizers, skills. Using the most popular ones (not necessarily the best ones)

It was like 15years ago learning the new mindset of vim (and spending a ton of time to customize it to my workflow). My understanding is that Claude and opencode doesn't give you this flexibility.

Learning all these stuff drove me to also set up openwebui, and it was such a successful private project that I implemented it at work (with jira/confluence/bazel query access) and management said "we need this by tomorrow".

I believe the models matter not that much anymore. The "harness" does. (unless you just want to vibe code. Thebn, throw crap at fable and call it a day)

Happy with oh-my-pi (omp)

pi-role-model

Not sure, haven't tried it today.

Last night it single handedly implemented a feature after a grilling session, and came back with the red-yellow-green risk assessment points that I mostly saw with anthropic models. I had to check if I'm using the right model, but it was DS4Flash.

So maybe I was using it already?

You build it as an extension or create your own fork/copy of pi and add it. This basically goes for most things in pi, except the most basic stuff. It's basically meant for people who think "I'll just add that myself" rather than expecting it to be there out of the box or finding other's solution to it, for better or worse.

But on Terminal bench, its

* DS4 Flash: 82.7

* GPT 5.6 Luna: 75.7

For reference, that puts it on the third spot behind GPT 5.5 and Fable 5. For some reason GPT 5.6 Sol is not showing in the leaderboard. If it did, then DS4 Flash was number four.

The thing is, even if Luna is better in DeepSWE and has the 80% discount. DeepSeek is still cheaper.

--------------

DeepSeek V4 | Flash GPT-5.6 Luna (New)

--------------

Input (Cache Hit) $0.0028 $0.02

Input (Cache Miss) $0.14 $0.20

Output $0.28 $1.20

--------------

Both Luna and Flash are heavy on the reasoning > output. And the cache hitrate + prices also matter.

Reality is, you can not go wrong with Luna or Flash at those prices. And remember, DeepSeek V4 Pro is still in the rafters, what is ironically closer to Luna's new price.

I think that's backwards. Anything that changes the performance of a model deserves a minor version bump. A new model has to be qualified before being pushed to production; but we don't get the choice here, just cross your fingers there are no regressions at all on all possible tasks the model might be asked to do.

I know most folks can't afford it I am working on making it viable to rent shared hosting the biggest issue is data leak and prompt injection attacks with shared hosting. (Since the server owner connects to your main system via the coding agent)

I guess using a ZDR provider is good enough for now.

That's a good point. Yeah their caching input is insane.

Can you post the link to the leaked interview? From what I understand he has been pretty tight lipped for a guy who has a larger stake worth more in his company than Dario does in Anthropic.

You should specify which model size and quant because there are many of both.

reminds me back in the day when we used to do version control with google drive. you generally don't need to do this anymore i don't think. it should be fairly trivial to open the codex cli in the project directory, ask it to do some analysis and planning -> save to md. then opencode with deepseek to read that md and start implementing. consider that some cross-model subagent workflows already exist: i use claude/fable to plan and there are openai plugins to directly spin up codex subagents. this latter approach gives claude/fable more directly control to babysit the codex agents whereas the former is more of a clearcut handoff

Source reads like Tony Robbins for LLMs:

  Excuse
  "Too simple to test"
  
  Reality
  Simple code breaks. Test takes 30 seconds.

To add to this, the $60 only applies to DeepSeek-V4-Flash and a few other models. For DeepSeek-V4-Pro, the amount is $15.

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.

Opencode also have a ZDR (zero data retention) deal with them – if I recall correctly, that's not something you can enable as an individual DeepSeek subscriber.

Anecdotal data from my own tests: allow only one provider if you want good cache usage on OR. 80 cents is probably 4x the price you should have paid.

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.

That's what I mean. On DeepSeek it's now just `deepseek-v4-flash`, while OpenRouter calls it `deepseek/deepseek-v4-flash-0731`, so now when someone talks about DeepSeek V4 Flash, like in benchmarks, or other inference providers, which version do they actually mean?

The `-0731` style suffix is worse compared to a proper version bump like V4.1.

Does it still say that it's an anthropic model, when asked? I would guess new post-training has fixed that.

Yes that's my point. The old and the new version are different in capabilities, but now when someone talks about DeepSeek V4 Flash (in benchmarks, on inference providers), you don't know which exact version it's about.

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.

Yes, the Dual DGX Spark looks like the sweet spot for this model for now. Good preprocessing speed. Lots of context. Fast enough for 1-5 devs perhaps. Around 8200€ as of today (used to be 6000€).

Dual Strix Halo is much slower and current Macs with 256GB are both slower and more expensive (Mac Studio M3 Ultra 256GB around 12000€).

To get something faster than the two Sparks you'd need to spend more than $22000 for a server with 2x RTX Pro 6000 at $10000 each.

Beyond that you could get 2x AMD MI350P.

Probably the same. When the same base model is trained, the weight do not tend to change a lot. GLM 5.0 > 5.1 > 5.2 are the same base model, that just kept being trained. Weights hardly change as a result. Think in the like few percentage points size difference.

There's also the 2x spark way, which should be ~8k eur? Someone down the thread reported ~60tps for 2x sparks. That's totally usable for local inference.

You can also do 2x 6kPRO in a workstation, for ~20k.

I'm running a useful quantization of the previous version of Deepseek-V4-Flash -- quite well but with so much fan noise -- on a MacBook Pro M5 Max with 128 GB.

500k is for the 8x B300 version. Which is the only one you can buy atm. But technically a B300 card is more like 60k, just impossible to get.

https://www.tbench.ai/leaderboard/terminal-bench/2.1

> 78.4

The real score is always the official benchmark.

We need to see later if DS4 flash 0731 is going to maintain the score but we need to look at the official benchmarks.

Already seen a PR for DeepSWE to update the benchmark with 0731, so we can verify claimed vs official.

IIRC GPT 3 was priced at per 1k tokens, had to check, the biggest GPT 3 model from OpenAI was $0.06/1k, so $60 / 1M. gpt-3.5-turbo was the first model after ChatGPT and that was $2 / 1M. And no caching. So not really in the same ballpark

> not sure it's apples to apples comparison.

They're literally comparing the previous version of the same model with the new one. It's based on the same architecture, same pre-trained model, just different post-training. It doesn't get more apples to apples than this.

It will be fair if they release the harness though. I think now the future will be paired model-harness releases, not just weight dumps.

The performance changes are so big with the right harness that is makes sense to engineer the harness and fine-tune the model to one another from the start.