Kimi K3 and Qwen 3.8 Challenge Anthropic's Dominance in the AI Race
Kimi K3, Qwen 3.8, and Anthropic's (Potential) Unravelling
I explore how the launch of Kimi K3 and Qwen 3.8 threatens Anthropic's market position by proving open models can match top-tier performance. While Anthropic relies on regulatory strategies and high-cost models, competitors owning their infrastructure gain massive margin advantages. This shift suggests model-only providers face an existential risk unless they achieve recursive self-improvement or build unique, sticky products.
If you don't own data centers or power generation, the only thing that matters for your success is model demand.
- LarsDu88
The open weight, open architecture releases of the past several days has me more convinced that ultimately, the winner will be whoever burns their models to ASICs fastest.
The LLMs themselves are capable of doing some aspects of chip design as evinced by the K3 press release.
Furthermore, the frontier models are "good enough" for a wide swathe of tasks and will soon hit that threshold for a good amount of software engineering (if not already). Does anyone think we need a Mythos level model to plan a road trip, or give someone tips on making a cake recipe?
A Fable 5 model running at 9,000 tokens/s on an ASIC rather than 150 tokens/s on electricity chugging Nvidia GPUs, or even giant SRAM Cerebras or Groq chips could be good enough to meet the majority of demand.
Furthermore, if you're an enterprise the risk of data exfiltration and feeding data to a potential competitor like OpenAI or Anthropic is greatly reduced if you could shift to on-prem ASIC deployments. A handful of chips could cover a wide variety of use cases and cover them more securely. There are a lot of corporate use-cases for LLMs that are not frontier math research or coding.
- overgard
I keep thinking about the Figma thing. If you're unaware, here's the google summary:
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The Board Departure: Mike Krieger, Anthropic’s CPO and a co-founder of Instagram, sat on Figma’s board of directors. He resigned on April 14, just days before news of Claude Design broke. This sparked speculation over conflict of interest and the use of proprietary product strategy information.
Betrayal of Partnership: The launch aggravated the tech industry because Figma relied on Anthropic's models to power its own AI features, and even announced a joint "Code to Canvas" integration. Reports indicate Figma was blindsided by the depth and scope of Claude Design.
Market Reaction: The "SaaSpocalypse" thesis—fears that major AI foundation models will rapidly build application layers and cannibalize their own SaaS partners—was realized when the news broke. Figma’s stock saw an immediate 7% drop upon the announcement.
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I would suggest to people using LLMs: you should be cautious about giving these companies data or relying on them. If you're building an AI startup, there's a very good chance they could decide to directly compete with you if your idea has traction. You're also at their mercy for API pricing etc.
- bko
I think the risk is overstated.
For one, on the margin people are willing to pay a lot for slightly better models. I know personally the value the LLM adds to my workflow is considerably more than the $200/m I pay the frontier labs. I have no interest in optimizing that to get it slightly lower. There are a very vocal minority that optimizes this or companies whose LLM expense is marginal, but I think that's the minority (correct me if I'm wrong, curious what their customer base looks like)
Also the actual LLM is a tiny portion of the value added. Anyone that tried to build agentic solutions from LLM apis quickly realizes that a huge value is the Claude Code / Codex harness. There are open source implementations like OpenCode but they're not nearly as good.
Think about it another way. Consider how much money Microsoft spends on maintaining Excel. There are open source alternatives that have >90% of the functionality, they'll even work w/ Excel files and generate them. Google sheets is probably 99% and available to everyone and better in a lot of regards. But the immense value spreadsheet software produces workers above the $100 or whatever a year makes it so that there is a real moat and no one bothers exploring alternatives.
- port3000
It's amazing how quickly Fable went from 'Game-changing model that needs to be banned' to 'Yeah it's alright, but OpenAI is also just as good and there are a couple of good open weight alternatives that are equivalent for almost everything'
The hype cycles are shortening, perhaps we really are reaching some kind of plateau this time (famous last words)
- drob518
> More importantly, as sustainable long-term businesses, model-only providers are particularly at risk. Knowledge Atlas, Moonshot Labs, and Anthropic face defensibility challenges versus OpenAI, Alibaba, SpaceX, Meta, and Google.
Hm. How is OpenAI not a “model-only” provider just like Anthropic? Seems like they are vulnerable in the same way.
- davidpapermill
I think a big question is whether any of these labs can produce a model that is _ahead_ of Anthropic and OpenAI.
A related question is how much they're dependent on the APIs of Anthropic and OpenAI to achieve their results - whether through distillation or other uses.
If these models are derivative of Anthropic/OpenAI I would expect performance to be more narrow and progress to be limited.
- torginus
Upper bound of AI progress - recursive self improvement. In this case AI will be responsible for building better models, making people who own datacenters the winners. Anthropic/OAI is cooked.
Lower bound of AI progress - plateau. Progess is slowing, focus is on serving a meaningful peak capability at the lowest possible price. There's been news today that Google is building a Gemini chip with weights baked into silicon. Considering a chip's lifetime of 2-3 years at minimum, and that a 2-3 year model today would be useless today, they're expecting they wont make a similar amount of progress in the next 3. Game is about selling at the lowest margin. Anthropic/OAI is cooked.
So their survival rests on the presumption that AI progress will fall between these two extremes.
- spaceman_2020
Dario can reverse this by going on the podcast circuit again and threatening everyone with 75% job losses due to (his) AI this time
Ramp the number up to 85% if that doesn’t work
If it still doesn’t work, go nuclear and target 100% job losses language
- sinuhe69
I have a problem with the cost per task metrics of Artificial Analysis. We don’t know how they calculate it exactly. But recently, cost per task has become the most discussed topic. The logic is basically: if model A achieves 55% on benchmark X and model B 60%, but the cost per task of A is 50% cheaper, people would choose A instead of B.
But that implies that all output of the less intelligent model A is usable, perhaps only a bit worse than the output of B. But what if the output of A is unusable, or it can only deliver usable results in 1 out of 5 tries? In such cases, the user will have to rerun the task and it will very quickly double or triple the cost and makes the old average number misleading! I would argue the retry and flaky cost will be many times bigger than the average token cost and that is the true cost the users have to bear.
AI-Benchy [0] (admittedly a one man benchmark) shows a much different figure than the numbers of Artificial Analysis. Opus 4.8 cost per task according to AA is $1.80 and Kimi K3 is $0.94$. According to AI Benchy, however, the *cost per successful task* of Opus 4.8 is 10.7 cents vs 19.4 cents of K3. The number of correct tests and pass rate of Opus 4.8 is also higher than Kimi K3.
So on a cost-per-usable-result basis, Kimi K3 is actually pricier than Opus 4.8 — the opposite of what AA’s headline number suggests.
Thus, I don’t know if I can believe the numbers of AA or we need to track the cost ourselves.
[0] https://aibenchy.com/compare/a […]
- a13n
Definitely feels like there’s a particular narrative being pushed on HN today.