Kimi K3 and Fable: How Routing Beats Single Models for Cost and Quality
Kimi K3 Is Competitive with Fable; Kimi K3 and Fable Is SoTA
We tested Kimi K3 against Fable 5 on over 1,000 agentic tasks and found that routing between them achieves 93% accuracy while cutting costs by up to 50x. While both models perform similarly overall, they excel in different domains like symbolic math or terminal operations. By using an open model like Kimi K3 as the default and reserving Fable for complex tasks, we unlock state-of-the-art intelligence at a fraction of the price.
The single model provider, token maxxing days, are coming to an end. The best AI no longer comes out of a single lab, it's a mixture of models.
- nxtfari
If you haven’t been really running and testing these models yourself, they are all benchmaxxed. No matter how close they score to frontier on whatever metric, they always fall apart in real world tasks and their token efficiency is ridiculously bad. Fireworks has incredible incentive to make this claim in a headline, because Fireworks hosting K3 for you is pure profit for them, unlike when they host closed source models.
- acd
This benchmark is probably also self promotion of services. Fireworks happens to make a router. Using the router gets better performance.
- JSR_FDED
Very interesting. They test Kimi K3 and Fable on a set of approx 1000 tasks grouped into 5 areas (SWE, Legal, etc).
They put a router model in front that predicts whether Kimi or Fable is going to give a better cost for a correct result. (They believe that ultimately such a router model should be continuously trained on your own workloads so it makes the best decisions for you).
Their router chose Kimi the majority of the time (72% in one category, all the way to 96% in another category), leading to cost savings in every category (from 1.5x to 50x depending).
- kaycey2022
What is routing? How do they decide which is better? The only way to come up with a routing model for your workload is to send queries to all the models and then come up with a way to say which solution was better, often trying out multiple times for the same model + query to account for other statistical errors.
This makes you, an ai inference user an unwitting AI company with a non scalable product.
The biggest mental trap people have fallen for is the notion of “best” and, always using the frontier model. Instead you should just bite the bulet and choose the cheapest or the best. This routing dance is just tokenmaxxing in disguise.
Edit: Another thing concerning me is that the models themselves are not concrete behind their endpoints. Models can be arbitrarily dumbed down by reducing their inference resources. Once Anthro/OAI release a new great model, they are fully incentivised to dumb down their current models to drive traffic to the new shiny more expensive one. In fact they can do this for any reason. Once they switch things behind the API interface, how useful is your meticulously tuned router? Not much at all.
- thecopy
When DeepSeek was released, it had an immidiate and significant impact on the US stock-market. Now when its becoming common knowledge that China is almost at parity with US SOTA models with good momentum, why is there no sentiment change on the market?
- abdullahkhalids
I will accept a 5% drop in benchmarks for a model that talks to me like a human.
- zkmon
Anthropic looks like Roman empire fast-farwarded, getting to the other side of the peak even before the IPO.
- hmokiguess
What's the data governance and privacy controls on using Kimi K3 if I subscribe to their coding plans? I want to migrate away from Anthropic
- brunooliv
Genuine question: can these posts be paid to hype the open source models? If yes, what would be the purpose?
On my work tasks, FastAPI Python and Springboot Java on a modern SaaS product, the only open model that can do tasks well and efficiently is Qwen3.7-Max.
In all my experiments, both GLM-5.2 and Kimi are busy grepping around the codebase for ALMOST 70-80K tokens before writing anything and when they do it typically breaks the code… it feels to me that these models are good but only when you write out a super detailed spec of the task just like it was done a year ago… Qwen3.7 just… does it
- nharada
Is there something specifically with Kimi that's better here? As far as I know Kimi pricing is about the same as Sonnet 5 -- what happens if you use that model and Fable instead? Or Grok 4.5 which is even cheaper?