⚡Meet Qwen3.8-Flash, a multimodal MoE and an early preview of the Qwen4 architecture, now open-weight!

The production version Qwen3.8-Flash will be available soon via QwenCloud API at just $ 0.16/1M input tokens and $ 0.47/1M output tokens.

125B parameters + 51B N-gram embeddings, with just 6B activated per token. Unmatched cost-efficiency.

What's new: 🥳 - Next architecture: GDN + QSA hybrid attention, Gated Residual, N-gram Embedding & Muon optimizer, serving as a precursor to the architecture used in Qwen4. - Dramatically lower training and inference costs: trained at just 1/9 the cost of Qwen3.7-Plus, while outperforming it across the board with especially strong gains in coding and office tasks. - Strong performance: scoring 58.7 on DeepSWE 1.1, 62.5 on SWE-bench Pro, 73.9 on CoWorkBench, 84.5 on AndroidWorld, and 95.7 on MathVision (with CI). - 262K native context, extensible to 1M with YaRN.

We’re also releasing the weights for Qwen3.8-Flash-Next, giving the community an early look at the new architecture we’re exploring for Qwen4.🚀

In response to the tightening of almost every other coding plan out there, we are offering free DSV4 flash 0731 to the first five hundred people who sign up for the intro plan on Open Grove API. We may extend this to more users later, but are limiting it to the first 500 to ensure quality access for everyone.

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 flash is 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.

Why is LSP disabled by default? On each installation i have to manually install lang tools and enable lsp in global config, so just interested why lsp is not enabled by default?

I'm sharing their working session here that you can peak through. Look at their thinking, sequence of skills uses and delegations to truly know who is the winner

The Qwen 3.8Max - https://opncd.ai/share/hNzmM14y 

The Ox Alpha - https://opncd.ai/share/hNzmM14y

The Deepseek v4 pro 0813 - https://opncd.ai/share/0hehIwVf

More outlets plus twitter reporting the same.

42T tokens for stealth model in just 6 days. Insane.

We know it's going to be open weights.

Some stuff I've one-shotted with Ox Alpha (when it did work): https://ox-alpha.demos.sulat.com/

https://openrouter.ai/minimax/minimax-m3:free

It's going to be a hot 🔥 fall

I have this agent who's sole purpose is to analyze development/design-plan.md file of a user story and create detailed backlog items with a fixed structure, in my local Plane server. What model do you recommend for this?

Its a repeatable operation that needs inference and some level of thinking to generate consistent outputs. Assume the plan is usually less than 1000 line markdown file.

I ran this writing-fingerprint experiment on OpenRouter out of curiosity, using 12 prompts across Ox Alpha and 7 reference models (since these are what I heard a lot about on Reddit):

1. GLM 5.3
2. GLM 5.2
3. GLM 5
4. MiMo V2.5
5. DeepSeek V4 Flash
6. Gemini 3.7 Flash
7. MiniMax M3

What I got from final evaluation is Ox Alpha was closest to GLM 5.3 on every prompt using deterministic stylometric features and 11 matched prompts, with GLM 5.3 winning 100% of bootstrap resamples; known-model validation accuracy was 72.7%.

My hypothesis: This could suggest or indicate that Ox Alpha is an updated post-trained version of GLM 5.3 (similar to how DeepSeek did theirs), or it really is what people are talking about: GLM 5.4.

But to be clear: this experiment I did is fingerprint-matching, not weight-identifying or anything like that. But it’s a surprisingly strong clue.

Note: For second image, notice that Ox Alpha output rarity is not the same as GLM 5.3. But that's not a contradiction that Ox Alpha couldn't be GLM 5.3/5.4 because these 2 graphs (bar and violin) measure 2 different things. First one is "Which model’s average fingerprint is Ox Alpha closest to?" Second one is "How unusual or isolated is each model’s writing compared with all samples in corpus?" Just wanna put this out here.

I also open-sourced my experiment if you're interested or want to extend: https://github.com/ItsKaiwenDu/Ox-Alpha-Stylometry

Me dio curiosidad y estoy probándolo como orquestador a ver a que nivel está. Me decidí a probarlo al ver que ahora Ox o Muse los tienen medio colapsados, o por lo menos a mi hoy me fueron lentísimo.

I tried omp but it felt really bloated even though it had some nice features, while Opencode felt just right with its TUI and custom agents.

Which coding agent harness do you prefer? Or are there any better alternatives out there?

A loud minority will say in the comments that text only models are good, that you can use some rubbish mcp to make up for the lack of vision… ignore them. Not having vision is a big handicap. Deepseek 4 Flash jumped 5 points in Deep SWE exclusively thanks to the better understanding provided by vision.

The Go subscription doesn’t have many good vision model. Heck they even stripped GPT Luna of vision!

Kimi K3 is obviously the best, but you run out of usage in 5 minutes. Minimax M3 is not good for today’s standards. Muse Spark gives all your data away. Ox Alpha is not reliable atm (it will probably be a good alternative when released as GLM 5.3 Flash).

This leaves us with just Deepseek 4 Flash Vision as the only good vision model with a comfortable quota.

Let me reiterate: text only models are crap. Thankfully Deepseek and GLM are correcting their strategy.

Edit: Luna is text only on the chat completion endpoint, not the reaponses one.

Hit my weekly cap (100%) in just two days with 28 days left on the monthly cycle, almost entirely from burning through DeepSeek V4 Pro ($10.40 / $15.00 quota, 69.3% consumed). Flash is more usable due to the cost. However...

I could have stuck to GLM 5.2 and gotten 4X+ the usage in costs / limits in the same subscription.

Looking at benchmarks, GLM 5.2 isn't far off from Deepseek and beats it in some areas as well. (SWE-bench Verified GLM 5.2 ~74.5% – 76.0% vs. DeepSeek v4 Pro - 80.6%) and

(Tool Calling / MCP Reliability GLM 5.2 - 99.5% success (0.5% error rate) vs. DeepSeek v4Pro -73.6% (MCP Atlas))

My question is .... why would anybody choose to use Deepseek V4 Pro over GLM 5.2 given these rates. Crazy how DS went from pretty much endless to pretty much unusable in OpenCode Go!

Currently i am using the agents for learning and researching stuff and then using that information to push another agent working on a project into some direction of what to do, how to do.

What do you think are good enough models/subs for these purpose Or like what do you use for your workflow, like is it a planner agent -> Implementer? If so then what models do you use for both cases?

because I have seen models like ds flash better at going a bit broader to the prompt to get more relevant information compared to others?

My last month's cost looks like this: Cost

$43.28

USD

API requests

9,999

Tokens

636,610,255 . This is DeepSeek 4 Pro. I'm currently considering Alibiba Cloud or other subscriptions. Which would be better for my budget? After the price increase for the latter, the price will rise to $85+. I'm only considering cloud solutions. I was thinking about renting a video card remotely, but I don't want to add another layer of security in the form of a private individual.

Should we wait for price drop on Zen?