Officially

From the publisher, with its history intact.

Together AI together.ai

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

Imported from official source

AI Classified by Officially

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

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

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?

    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

    This is an extract. The publication continues at the source.

    Read the original at the source: https://www.together.ai/blog/glm-5-3-vs-claude-fable-5-on-deepswe-cost-coding-and-routing

    Officially imported this from Together AI’s own source and shows an extract. If you work there, claiming the profile and verifying the domain lets you choose to show the full text here.

    Provenance

    Organization
    Together AI — imported from official source
    Official source
    https://www.together.ai/blog/rss.xml RSS
    Imported
    September 20, 2026 19:52
    Versions
    1 recorded
    Identity
    https://www.together.ai/blog/glm-5-3-vs-claude-fable-5-on-deepswe-cost-coding-and-routing

    Officially records where a publication came from, not whether it is true. Imported records are reproduced from an organization's own official source.