Kimi K3 Is Competitive with Fable; Kimi K3 and Fable Is SoTA

K3 is a frontier quality open model at a fraction of the cost. Even bigger is that it complements Fable predictably, which makes it possible to get the highest quality intelligence by routing tasks.

🧭 tl;dr: We ran Kimi K3 (open) against Fable 5 (closed) on ~1,000 agentic tasks finding:

We averaged benchmarks, each aimed at a different kind of work, and ran K3 and Fable 5 through the same harness. About 1,030 tasks in all, in real agent loops.

| Family | What it tests | Tasks | |---|---|---| | SWE | Real repo bug-fixes (SWE-bench style) | 460 | | Terminal | Long agentic ops: security, crypto, reverse-eng, sysadmin | 89 | | Algorithmic | LeetCode / AtCoder-style problems | 100 | | Multi-Language | Implementation across six languages | 225 | | Legal | A legal-agent benchmark (lawyer-graded tasks) | 120 |

One quick definition before we get into the results. Oracle routing is a method for measuring the best theoretical performance by running the task through each model and then picking the cheapest correct option (the cost/performance ceiling). In a practical router, you don’t get to run your task against multiple models. The router makes a prediction of which model has the best cost and quality trade off, but ultimately it’s a guess.

In this study, oracle routing demonstrated K3 is selected for 72-96% of tasks. This suggests a near-perfect router might be achievable, by learning the difference between day-to-day tasks and the true long tail of frontier work. It will require an order of magnitude more routing data, and real world performance to say definitively.

From a 10,000 foot view, it can be easy to look at both models and call the head-to-head a tie. For example, if you look at SWE, the headline benchmark, K3 gets 92.4%, Fable 92.6%. Across the five types of tasks we benchmarked on, the two models tend to stay within a few points of each other, with Fable pulling slightly ahead on its coding-language breadth (Multi-lang).

It’s easy to stop there and say “they’re roughly even”. The news is that they have discretely better performance across different task types.

If you take a peek inside a single benchmark, there’s more to see than just a top-line accuracy number. Take SWE, where the two are dead even overall. If you split SWE by problem domain you can see where each model shines. K3 is sharpest on symbolic math and dev tooling; Fable wins on web & data visualization work. The same pattern runs through the multi-language set, where Fable's breadth carries Java, Python and C++, while K3 draws even on JavaScript and Rust.

For long-horizon work at a terminal, driving a shell and prodding at systems across dozens of turns, K3 showed its true colors. It cleared a batch of tasks Fable never cracked: a 7z hash, FEAL cryptanalysis, leaked secrets, a live vulnerability, runaway async jobs.

While quality is a near-tie at a high level, price isn't close.

So where's this huge price gap coming from? token pricing, prompt caching, and effort-per-task. On SWE for example, K3 works much harder than Fable: roughly 55 turns and 1.3M tokens a task versus 21 turns and 130K. On the long terminal tasks it's the other way around: Fable is the one that spirals, running up 64 turns and 1.5M tokens (sometimes straight into a timeout).

Prompt caching does most of the work of turning that effort into K3's price advantage: even when K3 reads ten times the tokens, with cache hits that means that SWE runs still come in lower cost than Fable. There’s a tradeoff. Tasks with extra turns generally mean more wall-clock time per run i.e. slower runs. If you need an answer in two seconds, that matters; if you're running agents in the background at scale, a bill that's a fraction of the size matters a lot more.

If you send every task to whoever handles it best, you don't land somewhere between the two models, you land above both.

Per-task routing always out performs any single model run:

The oracle router choose K3, 72-96% of task traffic. By architecting a router this way, you end up with overall quality above either model alone at a cost close to just using just the cost-optimized one.

Put both quality and cost on one plot. K3 in blue lands to the left (the more cost-effective side) of Fable in red in all five task-families. Accuracy trades back and forth: Fable pulls ahead on multi-language, K3 on terminal and legal, the rest roughly level.

Kimi K3 + Fable routed together unlocks their best qualities at the best price.

The single model provider, token maxxing days, are coming to an end. The task-level data says these models are specialists at very different prices. The best AI no longer comes out of a single lab, it’s a mixture of models.

What this means in practice: