GLM-5.3 vs. Claude Fable 5 on DeepSWE: Cost, Coding, and Routing
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GLM-5.3 and Claude Fable 5 are basically tied on DeepSWE first try. GLM costs 5.4x less and pulls ahead once retries matter, so the premium looks hard to justify.
Based on reporting by Together AI — read the original for the full story.
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On DeepSWE, GLM-5.3 and Claude Fable 5 land almost on top of each other on the first attempt. Fable gets 69.7% pass@1, GLM gets 69.0%, a 0.7-point gap that sits inside the noise. After that, the open model starts to separate itself. GLM-5.3 leads pass@2 at 81.1% to 77.1%, and pass@4 at 87.6% to 84.1%.
The real story is the bill. GLM-5.3 costs $3.99 per rollout. Fable costs $21.63. That is a 5.4x gap, and it shows up in the simple math: GLM solves 17 tasks per $100, while Fable solves 3. Neither model is faster in any meaningful way — GLM averages 35 minutes per rollout, Fable 34 — so speed does not rescue the expensive option.
The two models also behave like close cousins. Their per-task correlation is 0.65, the highest in the set, which means running both does not buy much extra coverage. Together they cover 106 of 113 tasks, but most of that overlap is the same tasks done by the same kind of model. In other words, this is not a portfolio story. It is a routing story, and the clean answer is to start with GLM-5.3.
There are still pockets where Fable earns its keep. It wins Rust by a wide margin, 85% to 70%, and it is stronger on data modeling and serialization, where it posts 88% to GLM’s 79%. GLM, meanwhile, takes JavaScript at 90% to 75%, plus TypeScript and Go, and it wins five of the eight task domains overall. It is also less verbose, with 80k output tokens versus Fable’s 114k, even though it takes more steps.
So the practical setup is simple: keep GLM-5.3 as the default, and only pay for Fable when the task is Rust-heavy or serialization-critical. For everything else, the expensive model mostly looks like a luxury tax with nicer branding.
My take — AI-written commentary, not fact-checked reporting
This is the kind of benchmark result that should make procurement people smile and model vendors sweat. If two systems are this close on accuracy, the one charging 5.4x more is not a premium product; it is a rounding error with a logo. The bigger lesson is that correlation matters as much as scoreboards, because buying two near-clones is just paying twice for the same mistake.
Read more about this at: Together AI