Mozilla: The state of open source AI
stateofopensource.ai114 points by rellem an hour ago
114 points by rellem an hour ago
Speculation: open models is what will kill Anthropic and OpenAI. Hyperscalers can run the models without a licensing fee. Apple can make them smaller and put them on the device.
The frontier models are an edge and a liability. They're astronomically expensive to train. Without them, their models will fade into obscurity. Their marketing depends on people believing the models are meaningfully different, as people have sweatily argued on this forum. Personally, I'm not convinced there's much of a difference between these models at this point. The harness is what takes these random and hallucinogenic models and make them into something deterministic and useful.
Open models are probably also comparatively astronomically expensive to train - just less so than the frontier models because they’re somewhat smaller, +/- the creators are more incentivised to focus on getting more from less compute because they’re have to, +/- they rely on distillation of the frontier models and this is more efficient.
But efficiencies aside; creation of open models still requires a lot of money and compute from a large organisation which is willing to accept zero return for that spend. This largesse is unlikely to continue forever; so the question is which will crack first, the frontier models’ business model or the fast followers’ generosity?
Yes, the problem with comparing open models to open source is that open source requires humans to volunteer their time. Open models requires humans to volunteer their money.
These two types of contributions have very different behavioral profiles, and it doesn't obviously follow that the historical success of getting people to collaborate socially on building software for fun and for the benefit of the community will translate in any meaningful way to the necessity of being able to raise enormous amounts of money to pay for enormous amounts of electricity.
I’m not exactly sure on the “how” but it only makes logical sense for (non-AI) companies to band together to fund the training of a shared model. Apple is a great example, AI is not their core business but they still require it.
The only thing that took us down a different path is the vast sums of VC funding pumped into the AI companies.
You can run the same harness on fable, opus, sonnet, and see a huge difference between them. It is true the harness is important, and openai has begun encryption its instructions to swarmed sub-agents instead of just encrypting the chain of thought, but the model is still important at this stage.
Referent of "the models are meaningfully different" reads as <top closed, top open> rather than <top closed, cheaper closed> to me, so I'm not sure why we'd be comparing Fable vs Opus/Sonnet or Sol vs Terra rather than the same against Kimi K3.
Haven't tried Kimi K3 for now but there was a huge difference between GPT 5.6/Fable and GLM 5.2/Kimi K2.7 that were previous frontier open models.
This will only delay the inevitable. Sitting on some magic prompts is hardly the moat they need.
The thing about not much difference between models and the harness making them deterministic and useful is wrong. Also models have different strengths and weaknesses and some are better at almost everything by a large margin compared to others.
As for your speculation, I think it's hinging on some companies releasing models for free or no big differences between models. In a world with hyperscalers and companies training models you can quickly recreate Anthropic or OpenAI by having an hyperscaler ally with a model training company, train a good/a better model, and not release it.
Just like opensource search engines killed google
oh wait
I don’t even know the names of any open source search engines, but the open source models perform decently on various benchmarks and in personal experience.
Was it ever even a claim that open source search engines were trying to outperform google, let alone kill it?
Even in the world where all models are basically equivalent (a thesis I don’t buy, but will grant you for arguments sake) - I believe there is much more to the AI business than just training and running models.
It’s a very new set of technologies, and understanding what is useful to customers and what isn’t is the whole game. Call it, product taste. There were a million cell phones before the iPhone took over the world. Why iPhone? Product taste. There are a million startups, and only a select few become unicorns. Why? Product taste.
>There were a million cell phones before the iPhone took over the world.
You have tripped yourself up there.
iPhone took over as it introduced something innovative over standard phones, but then Open Source (Android) matched the multi-touch and software differences and Apple's branding, lock-in and design etc have managed to keep it as a big player in wealthier countries. IPhone also came on the back of the massive iPod success.
ChatGPT launched the same innovation vs Google Search, but just like Android Opensource AI is moving fast now.
Android has 72.7% market share at present, Open Source AI will do the same unless the frontier labs can continue to do something new.
The frontier labs are saddled with enormous investor and other debts. How long they can keep innovating by spending so much on R&D and paying there staff very high wages remains to be seen.
Once investors cash out via an IPO, the companies are back down to earth and playing in the real world again.
Exactly 4 months ago, the marketshare on openrouter was 60%-40% in favor of closed models. Now it's 63%-37% in favor of open models. On March 19th, the open labs processed 888B tokens in aggregate, yesterday, they processed 4.19T tokens in aggregate. That's almost 5x in 4 months! I can't think of the right intensifier to describe this level of growth.
If you are looking for more details (as inferred by openrouter data), I built a dashboard that updates daily: https://dirac.run/labs-market-share
https://stateofopensource.ai/state-of-open-source-ai-2026.pd...
the pdf is easier to read
The design and layout made it harder to read than it needed to be.
Regardless, the inference costs dropping almost 50× is really amazing to see. And now Kimi K3 release has shown how open models are getting closer to the frontier level already. Open source AI is moving a lot faster than Anthropic and OpenAI would have expected lol.
> Mozilla exists because one company tried to own the front door to the web, and an open community rose up to make sure it never could.
I'd say that the front door to the web is pretty much owned by Google and Apple at this point given Firefox current marketshare. And maybe that's enough, maybe a future where a low percentage of open models keep the rest of the system honest but that doesn't seem the argument of this article
It sure is nice to see that Mozilla is still doing all that they can to keep on top of current trends, except developing a decent privacy-focused web browser for developers and power users.
Haven't been following the articles and snippets we get from these labs about training their models for a while. But I'm guessing the latest chinese models are way less based on distilling? If not, then your speed of progress is still limited by the two labs (which we are collectively, in various forms subsidizing).
It appears open models were used to create this slop.
That opening is so hard to understand what they are trying to say, from the font and how it's written. It took me several times rereading to even grasp.
Plus the article is filled with cryptic things like:
Open ships easy.
Open deploys hard.
I think it’s supposed to mean “open source is easily shipped, but open source is hard to deploy”? Or perhaps “deploys hard” is a figure of speach, as in “we are deploying this open source and we are deploying it /hard/“? I don’t know, it’s not good.
From the title of a chart:
> The venture-funded open-source ecosystem: total disclosed funding, USD M
> Bars grow as you scroll.
The bars, in fact, don't grow as you scroll. And I don't even see why they should.
On my device, bars grow as I scroll. I want your feature, being able to just scroll the static page without elements jumping around.
> The bars, in fact, don't grow as you scroll. And I don't even see why they should.
On my device, they grow as I scroll to them.
Quick fix for the font, which many people are (rightly) complaining about.
Array.from(document.getElementsByClassName("quote")).forEach(p => { p.style.marginTop = "20px"; p.classList.remove("quote", "reveal") })
This new trend of content appearing while scrolling down is so terrible accessibility-wise, I do not understand how Mozilla of all institutions would do it.
Not every trend needs to be followed. Have some backbone. You receive donations to have that.
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Apart from the website being - frankly - bullshit, the content is also - frankly - bullshit.
It's just on the frontpage because the title says "open source AI".
> This new trend of content appearing while scrolling down is so terrible accessibility-wise, I do not understand how Mozilla of all institutions would do it.
Could you explain what is wrong with the accessibility of this page? All the content is included in the html payload, so it is accessible to screen readers and text-based browsers; and as for the "reveal" effect, it seems to respect user's choice of "prefers reduced motion" and is disabled when that is user's preference.
> it seems to respect user's choice of "prefers reduced motion".
Cool, that I didn't check, because it is impossible to enable that setting, as it breaks _huge_ amounts of websites.
I'm not aware of a way to enable it selectively, but one could also just display the content at all times. It's a static page. It's static content. None of this makes any sense.
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The idea behind that style of gradual reveal is probably some kind of storytelling, but the only story it tells is that mozilla is wasting donations on people with incorrect opinions that could be used on.. idk not building torment nexii?
Maybe its the wildfire smoke in my eyes, but that font choice feels aggressive.
It's their own fonts: Mozilla Headline and Mozilla Text.
No idea why they'd be using the display font for the abstract though, that kind of defeats the whole purpose. It's supposed to be quirky and bold, but used far more sparsingly.
Just like how the web was won?
I think Mozilla is chasing a past formula, but the projection isn't linear enough to remain consistent, and the critical parts of the outcome, utter centralization of the market dominance of the three C's, are left out of the equation.
We might get the consolation prize, a few nerds having competitive alternatives to applaud, but we will be left with the hidden costs: stagnation by bloated market leaders, consumers and businesses pouring trillions of dollars into the commercial offerings while open development wonders where money comes from, and the leakage of these imbalances into political and social spheres.
If we follow a Mozilla template and get to the peak of Mozilla's success at the web, look at what that really is. Facebook, Amazon, Google etc are orthogonal to that equation.
"Open won"... to be fair cause "google paid it".
I think the fact that Mozilla survival model ultimately depends on Google's money means Google is keeping a corpse propped up just to have a defense argument that a browser competitor still exists, so they don't get hit with monopoly regulations.
Particularly in this day in age when the FF market share is down to low single digits
The UI is really hard on the eyes. Personally, I think the font size is way too big, and the animation timing feels off. If this is a benchmark page and not a product page, I feel like the information should be scannable at a glance. The UX is bad.
I'm unsure what it is about AI developers seemingly not having eyeballs. The Hermes Agent website is absolutely eye-searing and the application itself resembles some sort of weird "RETVRN" greek-styled travel agent website.
Really wish websites weren't allowed to force smooth scroll on. Hijacking basic browser functionality is so hostile.
Oh that's easy: they outsource design to the LLM, which doesn't have eyeballs.
I use hermes only ever saw their repo. Atrocious. I was sure you were exaggerating.
This is really insane to me.
There's nothing practical about open-source models yet that makes them even remotely comparable to closed frontier models.
All the hype around GLM, Qwen, now Kimi.... Are people really this naive that they believe these reports or, more worringly, are people NOT using these models and seeing the HUGE gap that still exists?
Take a task, any medium-sized task, decently scoped that you'd trust to give to Sonnet to finish without a hitch. Now give it to ANY open-source frontier model and watch them struggle and go in circles while failing tool calls and randomly assuming things.
Open-source is and has been amazing but its so hard to deploy reliably and at scale and there's still big problems in the underlying models with instruction following and tool calling that makes it basically unusable for production workloads at a decent price point...
> Take a task, any medium-sized task, decently scoped that you'd trust to give to Sonnet to finish without a hitch. Now give it to ANY open-source frontier model and watch them struggle and go in circles while failing tool calls and randomly assuming things.
Claude used to be much worse than it is now, just as bad the open weights models are. And the open weights were worse. The labs will also try to keep the lead, but at some point people start seeing real value from open models. Maybe you say they're not ready yet for medium tasks, but everyone sees the writing on the wall.
I hope you're right and I want you to be right, but, even seeing the current hype around local models, etc... and open-source models, I think the industry is currently under a big confusion where they see the benchmarks of things like Kimi, GLM, Qwen, they play with it via opencode, and they think like: "Wow this is pretty good, I want to deploy this". But they don't understand how the KV cache grows over time and can take almost as much memory as needed for a 30B param model, they dont understand that a quantized model WILL NOT be the same as a full precision one, and they surely don't see the engineering work needed to serve inference to even tens of customers at a decent quality and latency level.
The biggest moat of these giant labs and models is increasingly shifting towards deployment capabilities and (debatably) having better (proprietary) harnesses.
The models themselves can be impressive on benchmarks, but unless they can be served reliably to customers either at scale, hosted somewhere, or even on edge with predictable latency and memory usage, then frontier will always be leading.
If your doing things the closed models won't let you do; its the whole ball game.