---
title: "GLM-5.3 vs. Claude Fable 5 on DeepSWE: Cost, Coding, and Routing"
description: "GLM-5.3 and Claude Fable 5 were compared on the DeepSWE benchmark across 113 tasks with four trials each, totaling 904 rollouts. On first-attempt accuracy, Fable 5 scored 69.7% and GLM-5.3 scored 69.0%, a statistical tie inside error bars. GLM-5.3 led on pass@2 (81.1% vs. 77.1%) and pass@4 (87.6% vs. 84.1%), and cost $3.99 per rollout versus Fable’s $21.63, a 5.4x price gap. Per $100, GLM-5.3 solved 17 tasks while Fable solved 3. Both models regress test suites in only 11% of failures, and GLM-5.3 averages 35 minutes per rollout with 80k output tokens versus Fable’s 34 minutes and 114k tokens. The models are near-substitutes with 0.65 per-task correlation; GLM-5.3 won five of eight domains including JavaScript and concurrency, while Fable excelled only in Rust and serialization, making GLM-5.3 the practical default."
---

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

[Together AI](https://yomu.fyi/company/together-ai) · Aug 21, 2026

**Type:** Benchmark

## Summary

GLM-5.3 and Claude Fable 5 were compared on the DeepSWE benchmark across 113 tasks with four trials each, totaling 904 rollouts. On first-attempt accuracy, Fable 5 scored 69.7% and GLM-5.3 scored 69.0%, a statistical tie inside error bars. GLM-5.3 led on pass@2 (81.1% vs. 77.1%) and pass@4 (87.6% vs. 84.1%), and cost $3.99 per rollout versus Fable’s $21.63, a 5.4x price gap. Per $100, GLM-5.3 solved 17 tasks while Fable solved 3. Both models regress test suites in only 11% of failures, and GLM-5.3 averages 35 minutes per rollout with 80k output tokens versus Fable’s 34 minutes and 114k tokens. The models are near-substitutes with 0.65 per-task correlation; GLM-5.3 won five of eight domains including JavaScript and concurrency, while Fable excelled only in Rust and serialization, making GLM-5.3 the practical default.

## Context

The post compares GLM-5.3 and Claude Fable 5 on the DeepSWE benchmark, which tests software engineering ability across multiple task types and programming languages. The comparison uses 113 tasks with four trials each, analyzing accuracy, cost, and domain performance.

## Approach / What changed

The authors ran 904 rollouts (452 per model) on all 113 DeepSWE tasks, four trials each, using the published per-trial records. They compared pass@1, pass@2, pass@4, cost per rollout, solves per $100, per-task correlation, domain performance, and failure modes.

## Takeaways

- GLM-5.3 and Claude Fable 5 are a statistical tie on pass@1 (69.0% vs 69.7%), but GLM-5.3 wins on pass@2 and pass@4 and costs 5.4x less.
- Per $100, GLM-5.3 solves 17 DeepSWE tasks while Fable solves only 3, making the cost gap the decisive factor.
- The models are near-substitutes with 0.65 per-task correlation; GLM-5.3 dominates five domains including JavaScript and concurrency, while Fable only excels in Rust and serialization.

## FAQ

### How do GLM-5.3 and Claude Fable 5 compare on first-attempt accuracy?

They are a statistical tie: Fable 5 scores 69.7% and GLM-5.3 scores 69.0%, a gap inside the error bars. The difference is not significant.

### What is the cost difference between GLM-5.3 and Claude Fable 5?

GLM-5.3 costs $3.99 per rollout, while Fable costs $21.63, a 5.4x gap. Per $100, GLM-5.3 solves 17 tasks versus Fable's 3.

### When should I use Claude Fable 5 instead of GLM-5.3?

Only for Rust and serialization-heavy work, where Fable has a 15-point advantage on Rust (85% vs 70%). For all other domains, GLM-5.3 is the better choice due to lower cost and comparable or better accuracy.

**Tags:** [Benchmark](https://yomu.fyi/topic/benchmark), [Claude](https://yomu.fyi/topic/claude), [Cost Optimization](https://yomu.fyi/topic/cost-optimization), [LLMs](https://yomu.fyi/topic/llm)

- Source: [Together AI](https://www.together.ai/blog/glm-5-3-vs-claude-fable-5-on-deepswe-cost-coding-and-routing)
- Source URL: https://www.together.ai/blog/glm-5-3-vs-claude-fable-5-on-deepswe-cost-coding-and-routing
- Ingested by Yomu: 2026-09-19T10:13:46.407Z

[Read original post](https://www.together.ai/blog/glm-5-3-vs-claude-fable-5-on-deepswe-cost-coding-and-routing)