GLM-5.3 vs. Claude Fable 5 on DeepSWE: Cost, Coding, and Routing

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Published 8/21/2026

GLM-5.3 vs. Claude Fable 5 on DeepSWE: Cost, Coding, and Routing

A statistical tie on the first attempt, and a 5.4x price gap that decides the rest.

Authors

Zain Hasan, Shobhit Dixit

Table of contents

40+ Models Chosen for Production...40+ Models Chosen for Production...40+ Models Chosen for Production...

Key Takeaways

GLM-5.3 and Claude Fable 5 finish within noise of each other on DeepSWE accuracy, but GLM-5.3 costs a fifth as much per task and wins every multi-attempt metric. When two models are this close on quality, the price gap becomes the entire decision. The first attempt is level. Fable 5 posts 69.7% pass@1 and GLM-5.3 posts 69.0%, a 0.7 point gap that sits inside the error bars on both sides. GLM-5.3 owns the retries. It leads pass@2 (81.1% vs. 77.1%) and pass@4 (87.6% vs. 84.1%), so it has the higher ceiling as well as the lower price. The cost gap is 5.4x. \$3.99 per rollout vs. \$21.63. Per \$100 spent, GLM-5.3 solves 17 tasks and Fable solves 3. They are near-substitutes. Per-task correlation is 0.65, the highest agreement in this set, so running both adds little coverage. Keep the lower-cost model and escalate to Fable only for Rust and serialization work.

In our GLM-5.3 vs. Claude Fable 5 comparison on DeepSWE, a benchmark that tests a model's software engineering ability across many task types and programming languages, the two models are almost impossible to separate on quality. Fable 5 leads pass@1 by 0.7 points. It also costs 5.4x more per rollout and is the single most expensive configuration on the DeepSWE board. That combination makes the interesting question a narrow one: what does the premium buy when the accuracy is the same?

DeepSWE · Head to Head

GLM 5.3 vs Claude Fable 5 at a glance

Model Pass@1 Avg cost Solves / $100 Out tok Steps

glm-5.3 [max] 69.0% ± 2.7% $3.99 17 80k 124

claude-fable-5 [max] 69.7% ± 2.3% $21.63 3 114k 85

We ran GLM-5.3 (max) against Claude Fable 5 (max) on all 113 DeepSWE tasks, four trials each, from the published per-trial records: 904 rollouts in total, 452 per model. Both belong to the disciplined, low-regression school, which is why they behave so much alike. Every figure below comes from this run, so it can differ from other public GLM-5.3 vs. Claude Fable 5 scorecards.

The DeepSWE scoreboard: pass@1 and pass@k Single shot it is a tie. Fable 5 solves 69.7% of tasks on the first try under DeepSWE's official scoring, GLM-5.3 solves 69.0%, a gap well inside the noise band. The models separate once you allow retries, and they separate in GLM's favor. At two attempts GLM-5.3 leads 81.1% to 77.1%. At four it leads 87.6% to 84.1%. The open model matches Fable's first-shot accuracy at a fraction of the price, and it also holds the higher ceiling once retries are allowed. There is no attempt count at which paying 5.4x for Fable buys more coverage.

Cost comparison: GLM-5.3 vs. Claude Fable 5 pricing At $3.99 a rollout, GLM-5.3 is 5.4x lower-cost than Fable at \$21.63: 17 solves per \$100 against Fable's 3. Fable is the most expensive configuration on the board, and the price does not come with a speed penalty for GLM. GLM-5.3 averages 35 minutes per rollout against Fable's 34, roughly even. The token profile explains the shape of the run. GLM-5.3 is the less verbose model (80k output tokens to Fable's 114k) despite taking more steps (124 vs. 85). Fable writes more per step, GLM takes more and lower-cost steps. Neither is faster, but only one costs a fifth as much.

Coverage vs. reliability: where GLM-5.3 and Fable 5 differ Decompose pass@1 into coverage (tasks solved at least once) and reliability (pass rate on those tasks) and the two land near the same corner of the plane, with GLM reaching slightly further. GLM-5.3 has the higher coverage at 87.6% against Fable's 84.1%. Fable is marginally steadier per attempt at 82.0% reliability against 78.8%, and solves more tasks four-for-four (56 vs. 48). In practice both behave like disciplined generalists, with GLM trading a sliver of per-shot reliability for meaningfully wider reach across the benchmark. Failure modes: how each model gets it wrong This is where the family resemblance is clearest. Both models regress the existing test suite in only 11% of failures, far below the GPT-family's 20%, so both are safe to accept without heavy regression gating. The differences are small. Fable carries the larger big-miss share (18% vs. GLM's 16%), meaning that when it is wrong it is slightly more often badly wrong. GLM fails by near miss a little more often (61% vs. 57%). Broadly, these two fail the same disciplined way, which is exactly why their results are so correlated.

Where each wins, by task type Despite the tie on pass@1, the domain maps are not identical. GLM-5.3 takes the structured, interpreter-style work: query and config (88 vs. 72), language and runtime internals (83 vs. 78), stateful reactivity (73 vs 64), concurrency and durability (62 vs 45), and program analysis (64 vs 56). That concurrency result is a 17-point gap in Fable's weakest domain. Fable answers with the exact-contract domains: data modeling and serialization (88 vs. 79), build and ops (71 vs. 68), and protocol conformance (55 vs. 44), which is GLM's weakest area. GLM wins five domains to Fable's three, and Fable's 45% on concurrency is the one lane to keep it out of entirely.

GLM-5.3 vs. Claude Fable 5 by programming language Fable's case rests almost entirely on one language. Its Rust is 85% to GLM's 70, a 15-point margin and the widest single-language gap in the matchup. Serialization-heavy work is the natural companion to that strength. GLM answers with the best JavaScript on the board (90 vs 75), near-parity on Go (76 vs 71), and TypeScript (61 vs 57). Fable holds Python (70 vs 66). Outside Rust and serialization-heavy work, there is no language where Fable's 5.4x premium buys a meaningful accuracy edge.

How similar are GLM-5.3 and Claude Fable 5? Here is the catch for anyone hoping to...