I think it's worth providing some more details about my experience using Deepseek V4 Flash, and some additional perspective about why it's becoming a favored model.

I'll reiterate here that I still use my $20/month zip file workflow in ChatGPT (https://aibynick.com/thread/3) to work on all the largest software projects which I've begun using that process during the past year - and I'll continue to take advantage of that workflow, for as long as OpenAI continues to give away apparently limitless world class inference for $20 per month.

Aside from that workflow, I've settled on doing all my other work with Deepseek V4 Flash first.

To clarify some context about the sorts of agentic tasks Deepseek V4 Flash has been able to complete for me, here are some examples:

I had Deepseek perform the local install of DS4 on a self hosted DGX Spark. This is a pretty meta task - using a remotely hosted Deepseek model to install Deepseek locally. That process required the V4 Flash model I was using on OpenRouter, to complete research about which quant to choose for the machine architecture and hardware resources available on my local machine, then download all the required tools, perform the compilation from source of all the software components needed to run DS4 on DGX's ARM platform, in Ubuntu OS (those compilations steps were not a trivial undertaking, and V4 Flash aced them effortlessly). Then after sourcing and compiling all the parts, it fully installed, configured, tested, and benchmarked the 2 bit version of Deepseek v4 Flash on that local DGX machine - until I had a fully working local installation. That entire installation was completed hands off. Then next, it configured all the networking and software settings required to distribute DS4 across a cluster of 2 locally connected DGX machines. It did all that work from start to finish, without a hitch - and then it documented the entire process, so that it could be duplicated on other servers. Finding a human with enough knowledge to complete all those steps reliably, is a hard thing to do - and no person could accomplish those steps as fast as V4 Flash was able to.

And here's the thing: V4 Flash can perform that same sort of complex research/source/compile/install procedure with any of millions of other software packages, just as quickly and easily. No single person has the breadth of knowledge and experience to do all those things as well. In general, V4 Flash is above the threshold of any single human's ability to accomplish all computer/tech related tasks, at least when time and resource constraints are taken into consideration.

So, I've been using v4 Flash to perform every other sort of system configuration task imaginable. For example, when I set up Cline-pass API, I had v4 Flash create the prompts and files needed to configure Pi on all my other local laptop machines, my VPS server accounts, my phone, etc. - and also to configure PicoClaw on my phone and tablets (adding Cline-pass instantly as a provider for all those machines). Trying to do that otherwise manually would have taken so many more hours of my life.

And the same is true for all other grinding configuration routines that go into supporting my clients. I used v4 Flash to configure all the Cloudflared connections for all the LLM APIs I have running on local server machines - more than 100 models spread across 7 machines at different locations - so that now every client I ever want to give access to those local models, can be configured instantly.

Having a smart, capable, exceedingly inexpensive assistant that works like a pro, at the speed of light, is a dramatic improvement in productivity and quality of life for me.

I've had V4 Flash clean up old docker containers, forgotten running scripts, and no longer used environments on servers. I've had it find duplicate files, and misplaced files, across multiple hard drives attached to a machine. I've had it configure Rustdesk on multiple machines. I've had it discover why machines are running thermally hot. I've had it adjust power, sleep, and login settings on every common OS. I've had it build deeply capable device control automations on my phone. I've had it write and run browser control automations to log into accounts and compile daily dashboard summaries of information in private accounts. I've had it build automated email response bots. I've had it explain significantly sized code bases and provide documentation for complex systems. I've used it regularly to research general world knowledge, to complete task steps in virtually every sort of daily work. I've used it to write emails to clients and IT teams, to answer personal curiosity questions, to find the perfect joke for a special moment, to discuss philosophy, to write lyrics to songs, to download and install software of all sorts, etc., etc., etc.

But that's just the beginning.

I've been absolutely floored at the software development capabilities of this littler model. It works much more like the current frontier models than even the best small competitors such as Qwen 3.6. Qwen 3.6 is undeniably remarkably capable for a small model. A year ago we wouldn't have believed Qwen 3.6 was possible. In fact, I still think Qwen 3.6 MOE MTP is an extraordinarily useful model, because it's just so strikingly capable for its size (really I don't think any comparably sized model is as capable at coding), and it runs fast even on GPUs with only 16BG VRAM. For local inference, Qwen 3.6 is the cornerstone model for local inference on consumer grade hardware. It makes every machine I have with a GPU, useful at completing all sorts of generative AI tasks - but Deepseek v4 Flash is, nevertheless, much, much better. V4 Flash feels more like using GPT and other frontier models, than a small model like Qwen 3.6. It's just so smart and capable.

And that's not solely a subjective opinion. Whereas Qwen 3.6 performs about like a GPT-4 class model on benchmarks, Deepseek V4 Flash significantly outperforms GPT-4 across nearly all metrics. V4 Flash genuinely rivals models like GPT-5.5 and Claude Opus 4.7 on the benchmarks, so you can feel confident trusting it to complete tasks you give it. V4 Flash seems to always solidly understand not only the problem at hand, but all the surrounding context around a given goal, and it gets the job done skillfully, every time.

The killer features of v4 Flash are how cheap and fast it runs, over any of the well known API service providers - and just as important, it's great that so many LLM API services make it available (Openrouter, Ollama, Deepseek.com, DigitalOcean, Fireworks, Cline-pass, and many more). You don't have to worry about a single API going down in the middle of a project, or the model disappearing from the ecosystem.

Just now, on a Monday at 1:45pm East Coast US time, I spot-tested V4 Flash's performance. It's currently consistently working at 176 tokens per second on the Cline-pass API, in Pi coding agent. Openrouter is noticeably slower at the moment, but still clocks along at 110 tps, and the Deepseek.com API is returning a smoking 198 tps! You don't get that sort of speed from any of the other big frontier models.

Plus, pre-fill speed for very large input contexts is even more outrageously fast with V4 Flash - and don't forget that it has a truly usable 1 million token context window. None of the frontier models beat that limit. That means V4 Flash can chomp through enormous input contexts faster than any other model that approaches frontier quality. That generally makes it the performance winner for long context agentic tasks.

Speeds like that are a big part of what make V4 Flash so effective in practice, with real-life tasks. Because it can iterate so quickly, it can often find, test, and complete the best working solutions to a problem, faster than a bigger, smarter model can generate a first attempted solution. It can get to the better final solution more effectively, in less time, even if it needs to go through more steps, because it can work through those steps so ridiculously quickly. This isn't just a conceptual win - it's exactly what I'm experiencing empirically, over and over again, in real life tasks.

And of course, the speed of V4 Flash makes it an utter joy to work with. It can accomplish so many things instantly. You don't have to wait. It's satisfying and encouraging to see work get accomplished so quickly. What a relief, what a practical benefit, and what fun it is to use.

V4 Flash is clearly built for agentic tool use and coding - to me it feels basically as good in those domains as the frontier models. In any situation where it lacks knowledge, it makes up for that lack of knowledge in iterative speed. Just let it run in a loop, and you'll get a working solution. It's smart enough to know how to plan complete goals, how to complete required research, how to test its own output, etc. When you use it for a lot of tasks, you'll see, it is capable like a frontier model.

V4 Flash has been much more effective for me, for example, than Minimax M3. I thought M3 was a marvel when I first tried it, but V4 Flash has just been undeniably better all around than M3. Of course, V4 Flash doesn't know as much about arcane and obscure topics as the trillion parameter models such as GLM 5.2 and Kimi K2.7, but for building Flask apps, coding in Python, etc., it's a consummate pro.

I just don't feel like I give up anything moving from Deepseek V4 Pro (or from any other frontier model, for that matter) to V4 Flash. In many cases, I've actually preferred the output from V4 Flash, over that of bigger models. V4 Flash is in a very different class than the typical few hundred billion parameter models which compete with it for potential use on pro-sumer local GPU hardware.

After many dozens of hands-on hours working with V4 Flash on a massive variety of development and general computing tasks, I'm left with a genuine sense that it works at a frontier quality level, for all practical purposes, for most mainstream coding and agentic work. It simply does not disappoint.

And finally, Deepseek V4 Flash costs $.09/$.14 per million tokens input/output on OpenRouter - less than 1/4 the cost of V4 Pro - and remember that V4 Pro already costs a small fraction of what all the other frontier models cost. Now, additionally consider that I've been running V4 Flash on the Cline-pass API. They advertise 2x-5x the volume of usage for the same cost, compared to other LLM API providers, and from what I've seen, that claim appears to be true.

So with the V4 Flash API I've been using, we're talking about a model that's dozens of times less expensive than the most common large open-source models, and hundreds of times less expensive than the mainstream closed source frontier models.

That orders-of-magnitude difference means using V4 Flash on Cline-pass has seemed like it's basically free to run, in comparison to other models. For less then $7 per month (at the yearly Cline-pass rate), virtually endless pro-quality inference is available sustainably.

Remember also that Deepseek has the best cache rate cost of any model, by far, at $.018 (less than 2 cents per million), so if you're using an agent which does well with generating cache hits, you're going to see a large majority of input token during long agentic processes being charged that ridiculously low rate. It's outrageously inexpensive to use.

That cost changes how work gets done, because you can use it to complete virtually any computing task which can be automated, without worrying about cost at all. Projects which cost hundreds of dollars a few months ago, now get done for less than a buck. If you compare that to using Fable, you'll likely spend thousands of times more per million tokens with Fable.

Don't get me wrong, Fable is a real marvel - it's genius level in many domains. That's so exciting to see, but you certainly don't need Fable for most daily business tasks, or for most mainstream software development work tasks - no more than you need a genius with a degree from Harvard to operate a cash register. Routine work in many business environments is relatively simple, and V4 Flash is more capable than most people are at completing the majority of daily development work tasks, IT tasks, research tasks, etc.

The ridiculously low cost and high speed of V4 Flash offers a step change in the volume of typical work you can complete with an LLM. Imagine how much more work you could get done in your business if human labor cost a few pennies per hour, and your employees could work 100,000 times faster.

That level of changed expectations is what V4 Flash has materialized for me lately.

Add to all that, the fact that we can run the same Deepseek V4 Flash model available on public APIs, using what's proved to be a reliable framework and quantization method (DS4), on a single self-hosted DGX Spark (or cluster 2 DGX sparks for top tier quality and more headroom for long context KV cache), and you get the same familiar and trusted output from a model that has been put through the ringer repeatedly. It's a tremendously beneficial situation to have the same trusted,, well understood model available in your local self-hosted environment.

That trusted experience means I can recommend V4 Flash to my clients who have only modest budgets for in-house inference (especially those who must heed privacy and compliance constraints, which keep them from using any affordable mainstream LLM API services), and I can trust that frontier quality coding and agentic capability is available for all their toughest tasks. Sure, I'll use Qwen 3.6 for fast responses to easy tasks, and Gemma 4 for vision and tandem work with Qwen 3.6. Plus I have other favorites such as Stepfun 3.7 Flash, the bigger Qwen 3.5 models, Nemotron Super, etc. for knowledge lookup, and as an additional agentic worker in the mix, but Deepseek V4 Flash can be trusted as the adult in the room.

In a locally hosted setup, enable Deepseek V4 Flash to judge, monitor, filter, fix, guide, and coordinate the work of a stable of other capable smaller, faster sub-agent models, and you've got the foundation for LLM routing which works as fast and effectively as frontier APIs.

Oh, and don't forget that using Deepseek V4 Flash is better for the environment. It doesn't use anywhere near the energy or hardware resources required to run the bigger frontier class models.

I finally feel like I have one model that does everything needed to replace the majority of my frontier model API use, with incredible performance, great intelligence, ridiculously low cost, and which is also self-hostable without having to install a new electric service panel in the building - and most small businesses can afford to buy the hardware needed to run it in house. V4 Flash has been the model I've been waiting for, since the beginning of the age of LLMs.

I think it's worth providing some more details about my experience using Deepseek V4 Flash.

To clarify some context about the sorts of agentic tasks Deepseek V4 Flash has been able to complete, here are some examples:

I had Deepseek perform the local install of DS4 on a self hosted DGX Spark. This is a pretty meta task - using a remotely hosted Deepseek model to install Deepseek locally. That process required the V4 Flash model I was using on OpenRouter, to complete research about which quant to choose for the machine architecture and hardware resources available on my local machine, then download all the required tools, perform the compilation from source of all the software components needed to run DS4 on DGX's ARM platform, in Ubuntu OS (these compilations steps were not a trivial undertaking). Then after sourcing and compiling all the parts, it fully installed, configured, tested, and benchmarked the 2 bit version of Deepseek v4 Flash on that local DGX machine - until I had a fully working local installation. That entire installation was completed hands off. Then next, it configured all the networking and software required to distribute DS4 across a cluster of 2 local connected DGX machines. It did all that work from start to finish, without a hitch. Finding a human who can complete all those steps reliably, is a hard thing to do, and no person can do it as fast as V4 Flash did.

And here's the thing: V4 Flash can perform that same sort of complex research/source/compile/install procedure with any of millions of other software packages, just as quickly and easily. No single person has that breadth of knowledge and experience.

So, I've been using v4 Flash to perform every other sort of system configuration task imaginable. For example, when I set up Cline-pass API, I had v4 Flash create the prompts and files needed to configure Pi on all my other local laptop machines, my VPS server accounts, my phone, etc. - and also to configure PicoClaw on my phone and tablets (adding Cline-pass instantly as a provider for all those machines). Trying to do that otherwise manually would have taken so many more hours of my life.

And the same is true for all other grinding configuration routines that go into supporting my clients. I used v4 Flash to configure all the Cloudflared connections for all the LLM APIs I have running on local server machines - more than 100 models spread across 7 machines at different locations - so that now every client I ever want to give access to those local models, can be configured instantly.

Having a smart and capable assistant that works like a pro, at the speed of light, is a dramatic improvement in productivity and quality of life for me.

I've had V4 Flash clean up old docker containers, forgotten running scripts, and no longer used environments on servers. I've had it find duplicate files, and misplaced files, across multiple hard drives attached to a machine. I've had it configure Rustdesk on multiple machines. I regularly have it research, download, and install software of all sorts.

But that's just the beginning.

I've been absolutely floored at the software development capabilities of this littler model. It works much more like the current frontier models than even the best small competitors such as Qwen 3.6. I think Qwen 3.6 is remarkably capable for a small model - a year ago we wouldn't have believed Qwen 3.6 was possible. In fact, I still think Qwen 3.6 MOE MTP is an extraordinarily useful model, because it's just so strikingly capable for its size (really I don't think any comparably sized model is as capable at coding), and it runs fast even on GPUs with only 16BG VRAM. For local inference, Qwen 3.6 is the cornerstone model for local inference on consumer grade hardware - it makes every machine I have with a GPU, useful at completing all sorts of generative AI tasks - but Deepseek v4 Flash is, nevertheless, much, much better. V4 Flash feels more like using GPT and other frontier models, than a small model like Qwen 3.6. It's just so smart and capable.

And that's not just a subjective opinion. Whereas Qwen 3.6 performs about like a GPT 4 class model on benchmarks, Deepseek V4 Flash significantly outperforms GPT-4 across nearly all metrics. V4 Flash actually matches or genuinely rivals models like GPT-5.5 and Claude Opus 4.7, so you can trust it to complete the tasks you give it. It seems to always solidly understand not only the problem at hand, but all the surrounding context around a given goal, and it gets the job done skillfully, every time.

The killer features of v4 Flash are how cheap and fast it runs, over any of the well known API service providers - and just as important, it's great that so many LLM API services make it available (Openrouter, Ollama, Deepseek.com, DigitalOcean, Fireworks, Cline-pass, and many more). You don't have to worry about a single API going down in the middle of a project.

Just now, on a Monday at 1:45pm East Coast US time, I spot-tested V4 Flash's performance. It's consistently working at 176 tokens per second on the Cline-pass API, in Pi coding agent. Openrouter is noticeably slower at the moment, but still clocks along at 110 tps, and the Deepseek.com API is currently returning a smoking 198 tps! You don't get that sort of speed from any of the other big frontier models.

Plus, pre-fill speed for very large input contexts is even more outrageously fast with V4 Flash - and don't forget that it has a truly usable 1 million token context window. None of the frontier models beat that limit. V4 Flash can chomp through enormous input contexts faster than any other model that approaches frontier quality. That generally makes it the performance winner for long context agentic tasks.

Speeds like that are a big part of what make V4 Flash so effective in practice, with real-life tasks. Because it can iterate so quickly, it can often find, test, and complete the best working solutions to a problem, faster than a bigger, smarter model can generate a first attempted solution. It can get to the better solution more effectively, in less time, even if it needs to go through more steps, because it can work through those steps so ridiculously quickly. This isn't just a conceptual win - it's exactly what I'm experiencing with it empirically.

And of course, the speed of V4 Flash makes it an utter joy to work with on most tasks. It can accomplish so many things instantly. You don't have to wait. What a relief, and what fun it is to use - it's satisfying to see work get accomplished so quickly.

V4 Flash is clearly built for agentic tool use and coding - to me it feels basically as good in those domains as the frontier models. In any situation where it lacks knowledge, it makes up for that lack of knowledge in iterative speed. Just let it run, and you'll get a working solution. It's smart enough to know how to plan complete goals, how to complete required research, how to test its own output, etc. When you use it for a lot of tasks, you'll see, it is capable like a frontier model.

V4 Flash has been much more effective for me, for example, than Minimax M3. And I thought M3 was a marvel when I first tried it. V4 Flash has just been undeniably better all around than M3. Of course, V4 Flash doesn't know as much about arcane and obscure topics as the trillion parameter models such as GLM 5.2 and Kimi K2.7, but for building Flask apps, coding in Python, etc., it's a consummate pro.

I just don't feel like I give up anything moving from Deepseek V4 Pro (or from any other frontier model, for that matter) to V4 Flash. In many cases, I've actually preferred the output from V4 Flash, over that of bigger models. V4 Flash is in a very different class than the typical few hundred billion parameter models which compete with it for potential use on pro-sumer local GPU hardware.

After many dozens of hours working with V4 Flash on a massive variety of development and general computing tasks, I'm left with a genuine sense that it works at a frontier quality level, for all practical purposes, for most mainstream coding and agentic work. It simply does not disappoint.

And finally, Deepseek V4 Flash costs $.09/$.14 per million tokens input/output on OpenRouter - less than 1/4 the cost of V4 Pro - and remember that V4 Pro already costs a small fraction of what all the other frontier models cost. Now, additionally consider that I've been running V4 Flash on the Cline-pass API. They advertise 2x-5x the volume of usage for the same cost, compared to other LLM API providers, and from what I've seen, that claim appears to be true.

So with the V4 Flash API I've been using, we're talking about a model that is dozens of times less expensive than the most common large open-source models, and hundreds of times less expensive than the mainstream closed source frontier models.

That orders-of-magnitude difference means using V4 Flash on Cline-pass has seemed like it's basically free, in comparison to other models. For less then $7 per month (at the yearly Cline-pass rate), virtually endless pro-quality inference is available sustainably. Remember, Deepseek has the best cache rate cost of any model, by far, at $.018 (less than 2 cents per million), so if you're using an agent which does well with generating cache hits, you're going to see a large majority of input token during long agentic processes being charged that ridiculously low rate.

That cost changes how work gets done, because you can use it to complete virtually any computing task which can be automated, without worrying about cost at all. Projects which cost hundreds of dollars a few months ago, now get done for less than a dollar. If you compare that to using Fable, you'll likely spend thousands of times more per million tokens with Fable.

Fable is a real marvel - it's genius level in many domains. That's exciting, but you certainly don't need Fable for most daily business tasks, or for most mainstream software development work - no more than you need a genius with a degree from Harvard to operate a cash register. Routine work in many business environments is relatively simple, and V4 Flash is more capable than most people are at completing the majority of daily development work tasks, IT tasks, research tasks, etc.

The ridiculously low cost and high speed of V4 Flash offers a step change in the volume of typical work you can complete with an LLM. Imagine how much more work you could get done in your business if human labor cost a few pennies per hour, and your employees could work 100,000 times faster.

That's what V4 Flash has been for me lately.

Add to all that, the fact that we can run the same Deepseek V4 Flash model available on public APIs, using what's proved to be a reliable framework and quantization tech (DS4), on a single self-hosted DGX Spark (or cluster 2 DGX sparks for top tier quality and more headroom for long context KV cache), and you get the same familiar and trusted output from a model that has been put through the ringer thousands of times - it's a tremendously beneficial situation to have the same trusted,, well understood model available in your local self-hosted environment.

That trusted experience means I can recommend V4 Flash to my clients who have only modest budgets for in-house inference (especially those who have privacy and compliance constraints which keep them from using LLM API services), and I can trust that frontier quality coding and agentic capability is available for all their toughest tasks. Sure, I'll use Qwen 3.6 for fast responses to easy tasks, and Gemma 4 for vision and tandem work with Qwen 3.6. Plus I have other favorites such as Stepfun 3.7 Flash, the bigger Qwen 3.5 models, Nemotron Super, etc. for knowledge lookup, and as an additional agentic worker in the mix, but Deepseek V4 Flash can be trusted as the adult in the room.

In a locally hosted setup, enable Deepseek V4 Flash to judge, monitor, filter, fix, guide, and coordinate the work of a stable of other capable smaller, faster models, and you've got the foundation for LLM routing which can work as fast and effectively as frontier APIs.

Oh, and don't forget that using Deepseek V4 Flash is better for the environment. It doesn't use anywhere near the energy or hardware resources required to run the bigger frontier class models.

I finally feel like I have one model that does everything needed to replace the majority of my frontier model use, with incredible performance, great intelligence, ridiculously low cost, and which is also self-hostable without having to install a new electric service panel in the building - and most small businesses can afford to buy the hardware needed to run it in house. V4 Flash has been the model I've been waiting for, for several years.

I think it's worth providing some more details about my experience using Deepseek V4 Flash.

To provide some context about the sorts of agentic tasks Deepseek V4 Flash has been able to complete, here are some examples:

I had Deepseek perform the local install of DS4 on a self hosted DGX Spark. This is a pretty meta task - using a remotely hosted Deepseek model to install Deepseek locally. That process required the V4 Flash model I was using on OpenRouter, to complete research about which quant to choose for the machine architecture and hardware resources available on my local hardware, then download all the required tools, perform the compilation from source of all the software components needed to run DS4 on DGX's ARM platform, with Ubuntu OS (these compilations steps are not a trivial undertaking). Then after sourcing and compiling all the parts, it fully installed, configured, tested, and benchmarked the 2 bit version of Deepseek v4 Flash on that local DGX machine - until I had a fully working local installation. Next, it configured all the networking and software required to distribute DS4 across a cluster of 2 local connected DGX machines. It did everything from start to finish, in all that setup, without a hitch. Finding a person who can complete all those steps reliably, without a hitch, is a hard thing to do, and no person can do it as fast as V4 Flash did.

And here's the thing: V4 Flash could perform that same sort of complex research/source/compile/install procedure with any of millions of other software packages, just as quickly and easily.

So, I've been using v4 Flash to perform every other sort of system configuration task imaginable. For example, when I set up Cline-pass API, I had v4 Flash create the prompts and files needed to configure Pi on all my other local laptop machines, my VPS server accounts, my phone, etc. - and also to configure PicoClaw on my phone and tablets (adding Cline-pass instantly as a provider for all those machines). Trying to do that otherwise manually would have taken hours.

And the same is true for any other grinding configuration routine. I used v4 Flash to configure all the Cloudflared connections for all the LLM APIs I have running on local server machines - more than 100 models spread across 7 machines at different locations - so that now every client I ever want to have access to those local models, can be configured instantly.

Having a smart and capable assistant that works like a pro, at the speed of light, is a dramatic improvement in productivity and quality of life for me.

I've had V4 Flash clean up old docker containers, forgotten running scripts, and no longer used environments on servers. I've had it find duplicate files, and misplaced files, across multiple hard drives attached to a machine. I've had it configure Rustdesk on multiple machines. I regularly have it research, download, and install software of all sorts.

But that's just the beginning.

I've been absolutely floored at the software development capabilities of this little-ish model. It works much more like the current frontier models than even the best small competitors such as Qwen 3.6. And I think Qwen 3.6 is remarkably capable for a small model - a year ago we wouldn't have believed it was possible. I still think Qwen 3.6 MOE MTP is an extraordinarily useful model, because it's just so strikingly capable for its size (really I don't think any comparably sized model is as capable at coding), and it runs fast even on GPUs with only 16BG VRAM. For local inference, Qwen 3.6 is the cornerstone model for local inference on consumer grade hardware - it makes every machine I have with a GPU, useful at completing all sorts of generative AI tasks - but Deepseek v4 Flash is, nevertheless, much much better. V4 Flash feels more like using GPT and other frontier models, than a small model. It's really smart and capable. You can trust it to complete the tasks you give it. It just seems to always solidly understand not only the problem at hand, but all the surrounding context around a given goal, and gets the job done skillfully, every time.

The killer features of v4 Flash are how cheap and fast it runs over any of the API service providers - and just as important, it's great that so many LLM API services make it available (Openrouter, Ollama, Deepseek.com, DigitalOcean, Fireworks, Cline-pass, and many more).

I just tested V4 Flash, on a Monday at 1:45pm East Coast US time, and it's consistently working at 176 tokens per second on the Cline-pass API, in Pi coding agent. Openrouter is noticeably slower at the moment, but still clocks along at 110 tps, and the Deepseek.com API is currently returning a smoking 198 tps!

Plus, pre-fill speed for very large input contexts is even more outrageously fast - and don't forget that V4 Flash has a truly usable 1 million token context window. It can chomp through enormous input contexts faster than any other model which approaches frontier quality, that I've ever seen.

Speeds like that are a big part of what make V4 Flash so effective in practice. Because it can iterate so quickly, it can often find, test, and complete the best working solutions to a problem, faster than a bigger, smarter model can generate a first attempt. It can get to the better solution more effectively, in less time, even if it needs to go through more steps, because it can work through those steps so quickly.

And of course, that speed makes it an utter joy to work with on most tasks. It can accomplish so many things instantly.

V4 Flash is clearly built for agentic tool use and coding - to me it feels basically as good in those domains as the frontier models. In any situation where it lacks knowledge, it makes up for that lack of knowledge in iterative speed. Just let it run, and you'll get a working solution. It's smart enough to know how to complete goals, how to complete required research, how to plan, etc. It is capable like a frontier model.

V4 Flash has been much more effective for me, for example, than Minimax M3. Of course, it doesn't know as much about arcane and obscure topics as the trillion parameter models like GLM 5.2, Kimi K2.7, etc., but for building Flask apps, coding in Python, etc., it's a consummate pro.

I just don't feel like I give up anything moving from Deepseek V4 Pro to V4 Flash, or from any other frontier model for that matter. In many cases, I've actually preferred the output from V4 Flash, over that of bigger models. V4 Flash is in a very different class than the typical few hundred billion parameter models which compete with it for potential pro-sumer local hosting.

After many dozens of hours working with it on a massive variety of development and general computing tasks, I'm left with a genuine sense that it works at a frontier quality level, for all practical purposes, for most mainstream coding and agentic work. It simply does not disappoint.

And finally, Deepseek V4 Flash costs $.09/$.14 per million tokens in/out on OpenRouter - less than 1/4 the cost of V4 - and remember that V4 Pro costs a small fraction of what all the other frontier models cost. Now, additionally consider that I've been running V4 Flash on the Cline-pass API - they advertise 2x-5x the usage for the same cost, compared to other LLM API providers, and from what I've seen, that claim appears to be true. So with the V4 Flash API I've been using, we're talking about a model that is dozens of times less expensive than the most common large open-source models, and hundreds of times less expensive that the mainstream closed source frontier models. Using V4 Flash on Cline-pass has seemed to be basically free, in comparison. For less then $7 per month (at the yearly rate), virtually endless pro-quality inference is available sustainably.

Add to all that the fact that we can run the same Deepseek V4 Flash model, using what's proved to be a reliable quantization (the DS4 framework), on a single self-hosted DGX Spark (or cluster 2 DGX sparks for higher quality and more headroom for long context KV cache), and you get the same familiar and trusted output from a model that has been put through the ringer thousands of times - that's a tremendously beneficial situation. I can recommend V4 Flash to my clients who have very modest budgets for in-house inference (especially those who have privacy and compliance constraints which keep them from using LLM API services), and I can trust that frontier quality coding and agentic is available for all their toughest tasks. Sure, I'll use Qwen 3.6 for fast responses to easy tasks, and Gemma 4 for vision (plus to back up work by Qwen 3.6). I have other favorites such as Stepfun 3.7 Flash for knowledge lookup and as an additional agentic worker, but Deepseek V4 Flash can be trusted as the adult in the room.

Oh, and don't forget that using Deepseek V4 Flash is better for the environment. It doesn't use anywhere near the energy or hardware required to run the bigger frontier class models :)

I finally feel like I have one model that does everything I need, with incredible performance, great intelligence, ridiculously low cost, and which is also self-hostable without having to install a new electric service panel in the building - and most small businesses can afford to buy the hardware needed to run it in house. V4 Flash has been the model I've been waiting for, for several years.