I ran DeepSeek V4 Flash Vision Exp on the current 98-task MindTrial set with the same Python executor available as in the other runs.

The model is interesting because it is an experimental vision-enabled version of the V4 Flash-0731 branch, while the current stable V4 Pro is the newer Pro-0813 build.

The text result surprised me:

• V4 Flash Vision Exp: 38/39, ~25m44s, 70 Python calls
• V4 Pro-0813: 37/39, ~39m24s, 28 Python calls

The Vision model’s one failed text task is basically a formatting miss: all five multiple-choice selections were correct, but it returned 1) b rather than the exact accepted punctuation/format.

So I would not interpret 38 vs 37 as evidence that Flash is actually better than Pro, but there is at least no obvious text-capability regression from adding vision. The Vision/Flash run was also faster on these 39 tasks despite using considerably more Python, although the two models were served through different paths so I would not treat that as a controlled inference-speed comparison.

The visual side was much more mixed:

• Visual1: 21/33
• Visual2: 12/26
• Total visual: 33/59

That discrepancy was one of the more interesting findings. On the older Visual1 set, DeepSeek actually edged Claude 4.8 Opus 21/33 to 20/33. On Visual2, though, Opus was well ahead at 18/26 versus 12/26.

Overall that gives:

• DeepSeek Vision Exp: 71/98 overall, 33/59 visual
• Sonnet 5: 72/98 overall, 34/59 visual
• Claude 4.8 Opus: 73/98 overall, 38/59 visual

So in this workload the experimental DeepSeek vision model lands surprisingly close to Sonnet 5 overall, while the Opus 4.8 comparison depends quite a lot on which visual set you look at. It is still well behind the strongest current multimodal results such as Gemini 3.7 Flash, GPT-5.6, Kimi K3 and Opus 5.

There was also a fairly strong long-tail/tool-use pattern. The complete DeepSeek run took about 6h10m and made 537 Python calls. 46 of 98 tasks reached the 10-call limit, including 25 of the 27 tasks that did not pass.

On visual tasks specifically, capped trajectories passed only 18/43, while tasks that stopped below the cap passed 15/16. That does not mean tool use caused the failures—the difficult tasks naturally need more work—but once this model starts repeatedly experimenting with Python, it seems to be a good sign that it is stuck rather than converging.

Failed/error tasks ended up consuming about 67% of the total runtime. Two hard visual errors went all the way past 133k reasoning tokens before hitting the length limit without producing an actionable final answer.

The strict score is still 71/98; I did not repair the formatting miss or any other answers after the fact.

Results/data: http://www.petmal.net/shared/mindtrial/results/2026-08-31/mindtrial-eval-all-models-03-2026_29.html

Meta has recently launched the Meta Spark 1.2 Contributor which is seriously cheaper than the Deepseek V4 Pro.

A good choice to switch??

If you don't know, DeepSeek is currently the only AI lab that's offering a FIM Completion endpoint that's not mainly for coding. What's special about this is that instead of talking to the model through the usual user/assistant chat format, you basically just give it some text and it just keeps it going, or give it text before + after a certain point and it generates what should go in the middle. This seems to activate the model's pure next-text prediction ability, which is wildly fun for creative writing, storytelling, worldbuilding, editing, and all sorts of stuff.

I've been playing around with this a lot recently while working on AhkLLM, a free and open-source Windows LLM app I've been building in AutoHotkey v2.

AhkLLM has two commands for this, FIM Fill and FIM Continue. Fill gets the text before + after your cursor, Continue only gets the text before it, and since AhkLLM uses UI Automation to grab and inject text directly in a lot of Windows apps you can use it pretty much wherever you're already writing, without having to copy the text into a chat, explain what you want, copy the answer back etc.

So you can do something completely stupid like:

My wife found the hidden folder on my desktop.

[DEEPSEEK WILL WRITE THE MISSING MIDDLE HERE]

Anyway, I own a strip club in Malta now.

and see what kind of batshit middle it'll come up with to connect the two.

What's also fun is keeping the beginning exactly the same and only changing the ending, then running it again. You can make the ending increasingly stupid and Deepseek will still try to somehow make the middle work, which I've wasted a ton of fun time on.

For actual writing though, this is undoubtedly way more useful than a typical chat workflow when you're already mid-paragraph and just want to see where the model thinks things could go next, or quickly edit a few words in the middle of a sentence and have the model rewrite it while keeping the surrounding text intact. The writing itself is lot better than the same model via its chat interface, since the model doesn't have to pretend to be a "helpful" assistant and can just directly continue what you're writing.

I think completions kind of got forgotten once everything moved to chat, which is a real shame because base completion models are really good writers when you can just give them text and let them cook. I remember people were doing crazy stuff with the original GPT-3 completions back in the day, and Deepseek brings that back to a large extent, and it's dirt cheap in comparison.

Anyway, both FIM Fill and FIM Continue are built into AhkLLM, and you can assign them to whatever hotkeys you want under Settings -> Commands. AhkLLM itself is a much bigger app at this point with a full chat GUI and a bunch of other stuff, but I mainly wanted to post this because I almost never see anyone talking about Deepseek's FIM endpoint for normal text.

I'm curious if anyone else has played around with FIM or text continuation in general for creative writing, and if so, what did you think of it? If you're a Windows user and want to give it a quick try, you can download Ahkllm here:
https://github.com/AmitTzah/ahkllm/releases/latest/download/AhkLLM.zip

I love Opencode CLI very much, it's by far my favorite harness available.

Now that i switched from Opencode GO to direct API access, i've wondered if i should switch to reasonix, pi or deepseek harness but all of them don't really appeal to me.

Any reason to switch? Will other Harnesses with better cache hit rate benefit me so much that i should bite the bullet and leave the convenience that opencode gives me?

Thx in advance!

(The data in the screenshot is all based on opencode sessions.)

It feels different to work with today - it's starting to talk like Opus 5 a little bit. Lots of seams, fair, inventing new words I haven't seen before. It didn't feel like this 4-5 days when I worked with it the whole day.

Anyone else?

Edit: Via API, using with pi for coding tasks.

I was using opencode Go plan to access DeepSeek V4 Pro model, but the recent price change made it unsustainable for me to continue with opencode subscription.

I wonder what other DSV4 Pro providers are available with Zero Data Retention (ZDR) policy?

I don't want Silicon Valley deciding when I'm allowed to spend my own monthly budget. The 5-hour windows, the weekly caps, the "your usage resets Monday 7:00 AM". It feels like convincing my mom that I'm an adult and that this should be my decision.

GLM Coding Plan, Kimi, MiniMax all these have the 5-hour thing too..

So I've been testing providers that don't do the limit thing. So far Standard Compute has been the best of them for me. Flat monthly price, no 5-hour or weekly windows, and honestly the most open and transparent about usage and pricing of everything I tried. Includes both open and close sourced models.

Featherless ai is also in this terrain, but don’t serve frontier models.

OpenRouter is of course on the list too, full control and every model, but it's pay-per-token, and token anxiety is real. I don't want to wake up to a runaway $1,000 bill because an agent got creative overnight.

Any other LLM providers you've tested that don't interfere with when usage is spent?

There are super powerful new models coming out almost weekly right now, but at the same time a lot of the coding plans have been tightening and lowering usage. So we are offering free DSV4 flash 0731 and GLM 5.3 Flash for a month on Phoenix Grove API. We opened this up last week for five hundred new member slots, and got so many signups that we decided to open the doors to another 500 new members.

People are looking for options, and here is one.

Other Cool Stuff:
All of our models are running on 100% US infrastructure, private with zero training on your code or prompts. Use the top open source models without sending your private prompts to a training lab. No complications, no "some models are private, other's aren't". They all are, all the time.

We host 20+ other major models in case you ever want to upgrade (no pressure though). Including the Kimi family, GLM, Qwen, Nemotron and bunch of others. On average our token pricing is 20% lower than market price.

Our higher plans bank up to ten days of usage, so when you aren't using them your usage saves up for later. Usage doesn't go to waste, so you can actually code when you want to.

The intro plan is a free one month trial with the standard cancel anytime, it bills at 3.99 after that. Use it, cancel it, that's fine. Free Flash for a month.

Figured i'd keep this short because we all know the new flash models are the point :)

For the API plan: api.pgsgrove.com

If you want to read more about us as a company, just pgsgrove.com

Also: There's a lot going on in the background with major AI companies right now, we are at a major turning point in the industry.

What's actually happening? This is happening because companies that were purely investment based, now need to answer to their investors. The problem has often been a loss based business model that is finally running dry.

There are several tricks that the major AI coding plans use to extract the most they can from their customers. Here are some examples, and what we are doing differently to put the users first. PGS AI was built with a sustainable business model from the ground up, so we can actually offer great usage rates without tricks.

Wasted usage is part of the AI industry, and they plan on it: Most coding plans bet on you letting usage go to waste. The plan goes: "how do we get people to think our coding plan offers a lot of usage, but then break it up into weeks and rolling windows so no one can ever actually use it all."

Many in app subs and coding plans are glorified training pipelines: This comes along with "how do we harvest this data for training without being too loud about that." Unless the company tells you otherwise, your data could be hopping all over world, being harvested by the individual labs or service companies. Some are better than others, but many of these companies rely on users just not noticing or caring that their data is being used for training. Data sales and marketing telemetry sales happen. This means that your private info, your personal life, and anything else you send through the system could become part of a training corpus for the next AI, or a marketing data set for a large company.

Privacy and ease of use should be available for everyone. Open source AI is the future, everyone here is just a bit ahead of the curve :)

Thanks to Deepseek, GLM, Moonshot and several other amazing labs pressing amazing models regularly!

Pulling now. As language only should work for spark cluster with Aiden or Eugr stack. Vision needs works - it's custom native vision. Standard vllm vision processor won't work.

The amount of stuff one can get done in a day is short of amazing tough.

Would be interesting to see what would happen if AI completely disappeared again.

Would any of what you are working on suddenly be unfeasible to complete? My things certainly would.

DeepSeek Harness just went through a pretty major rewrite.

On August 27, DeepSeek released DSH v0.1.2-alpha.1, followed by alpha.2 on August 30.

This wasn’t just another feature update. They changed several of the lowest-level pieces of the Harness architecture.

The old "APIProxy" is gone, and plugin communication is being migrated toward a unified Remote Gateway. The web client has been restructured, and Sessions are now being treated much more strictly as replayable event streams.

As a result, a number of plugins built around the old APIs, DOM injection, or custom "SessionEvent"s have started running into compatibility issues.

At one point, alpha.1 even removed "SessionEvent.ignorable", which meant custom events written by some plugins could potentially make older sessions impossible to restore properly.

In alpha.2, DeepSeek brought it back, while also continuing to improve things like "RemoteError", reconnect handling, plugin scoping, and related infrastructure.

Another important change is Subagents.

This is also probably my favorite part of the update.

Different sub-agents can now have their own model provider, model, reasoning effort, and maximum output length.

Subagents such as Claude Code and Codex can also use independent model configurations.

That means DSH is increasingly starting to look less like a simple harness and more like a full Agent Runtime.

Models, providers, subagents, sessions, plugins, permissions, and communication are all gradually being pulled into one unified architecture.

A while ago, some people assumed DeepSeek Harness had stopped being maintained because the npm package wasn’t getting updated.

The reality is pretty much the opposite.

It wasn’t abandoned. The underlying architecture was changing so quickly that the plugin ecosystem was starting to fall behind.

And alpha.2 has now started making its way into the npm alpha channel as well.

DSH is still far from settled.

DeepSeek is basically rewriting the foundation it expects this thing to stand on for the next several years.

Long time api user. Recently it has started burning tokens a lott faster. I burn like a billion tokens a day so the cost change was noticable. Shifted to glm 5.3 flash as soon as I got to know about it - cheaper, smarter. Looks so good on paper. And maybe for some it is. But it is so slow. I get like ,42 tok/sec with glm and like 120tok/sec in v4 flash which is like night and day in user experience. Have to adjust. Pockets smaller than token requirement. What do you guys think.

soy tan perfeccionista que empieza a darle al botón para encontrar la respuesta correcta y cuando me doy cuenta estoy así 😭

I'm really impressed by how strong it performs.Two hours ago, DeepSeek started a new round of gray testing. If you're using DeepSeek V4 Pro or DeepSeek V4 Flash Vision with PTC mode enabled (a workflow mode in DeepSeek Harness), and you see a lot of "I'm doing" in the chain of thought but rarely see "let me," that means you're on the gray model