Claude Fable 5.1 vs GLM-5.2

Select two models to compare their coding performance, pricing, and features.

Key Takeaways

Coding tier: Claude Fable 5.1 has a higher reference index on the selected shared benchmark configuration than GLM-5.2 (120.65 vs 100.32, a 20.33-point difference). This does not by itself establish general coding superiority. Editorial tiers: Claude Fable 5.1 = S+; GLM-5.2 = B+. Tiers are separate editorial assessments and do not establish a reliable order within a tier.
API cost estimate: Estimated 1M in / 200K out tokens: $20.0000 (Claude Fable 5.1) vs $1.4600 (GLM-5.2).
Metric Claude Fable 5.1 GLM-5.2
Coding Index
120.65
100.32
Coding tier S+ B+
Input Price $10/1M $0.06/1M
Output Price $50/1M $7/1M
Context Window 1M 1.05M

Coding Index

Coding Index · Reference baseline: 100

Claude Fable 5.1 120.65
GLM-5.2 100.32

Integer index difference: 20.33

DeepSeek V4 Flash 0731 = 100. The index reflects two shared tests, not capability multiples or statistically established gaps.

Verdict

The models have different editorial tier assessments. The index is supplementary and cannot predict performance on every development task.

Capabilities & Use Cases Comparison

Claude Fable 5.1 Tier S+

Best For
  • •Multi-file refactoring
  • •Front-end development
Strengths
  • •Agentic coding
  • •Long-running refactors and visual development

GLM-5.2 Tier B+

Best For
  • •Chinese market
  • •Bilingual development
  • •Budget coding
  • •Web apps
Strengths
  • •Good bilingual coding support
  • •Affordable pricing
  • •Strong in Chinese tech ecosystem
  • •Decent at common coding tasks

Claude Fable 5.1

OpenRouter quote 2026-10-11
Benchmark evidence and ranking method · Claude Fable 5.1

Cross-checked

Terminal-Bench 4.0 · Claude Code · max: 57.88% · 95% CI ± 3.76%

Artificial Analysis · Claude Fable 5.1 (Adaptive Reasoning, Max Effort, Default Fallback)

Test configuration: max

Terminal-Bench 2.1: 91.39% · SciCode: 63.08%

LiveBench · 2026-06-25 · claude-fable-5-1-max-effort

Coding: 86.38% · Agentic Coding: 66.06%

ModelRank assigns editorial tiers using public coding evidence, not automatic index thresholds or statistically proven classes. Price, speed, context size and sponsorship do not affect tiers.

DeepSeek V4 Flash 0731 = 100. The index reflects two shared tests, not capability multiples or statistically established gaps.

Results reflect particular model configurations. Agent harnesses, reasoning effort, tools, budgets and tasks may differ. Close indices do not establish superiority. B describes a relative position in this selected group, not an inability to code.

Data checked: 2026-09-10. Snapshot date is the retrieval date, not necessarily the evaluation date.

GLM-5.2

OpenRouter quote 2026-10-11
Benchmark evidence and ranking method · GLM-5.2

Cross-checked

Artificial Analysis · GLM-5.2 (max)

Test configuration: max

Terminal-Bench 2.1: 77.90% · SciCode: 51.16%

LiveBench · 2026-06-25 · glm-5.2

Coding: 79.65% · Agentic Coding: 51.77%

ModelRank assigns editorial tiers using public coding evidence, not automatic index thresholds or statistically proven classes. Price, speed, context size and sponsorship do not affect tiers.

DeepSeek V4 Flash 0731 = 100. The index reflects two shared tests, not capability multiples or statistically established gaps.

Results reflect particular model configurations. Agent harnesses, reasoning effort, tools, budgets and tasks may differ. Close indices do not establish superiority. B describes a relative position in this selected group, not an inability to code.

Data checked: 2026-09-10. Snapshot date is the retrieval date, not necessarily the evaluation date.

Results reflect particular model configurations. Agent harnesses, reasoning effort, tools, budgets and tasks may differ. Close indices do not establish superiority. B describes a relative position in this selected group, not an inability to code.

API cost estimate

1M input + 200K output (USD):

Claude Fable 5.1: $20.0000

GLM-5.2: $1.4600

Estimates use the displayed channel and time band; DeepSeek uses peak rates. Cache discounts, tool fees, long-context price tiers and uncounted additional tokens are excluded.

Frequently Asked Questions

Which model is better for coding: Claude Fable 5.1 or GLM-5.2?

Claude Fable 5.1 has a higher reference index on the selected shared benchmark configuration than GLM-5.2 (120.65 vs 100.32, a 20.33-point difference). This does not by itself establish general coding superiority. Editorial tiers: Claude Fable 5.1 = S+; GLM-5.2 = B+. Tiers are separate editorial assessments and do not establish a reliable order within a tier.

How do Claude Fable 5.1 and GLM-5.2 compare in API pricing and costs?

Claude Fable 5.1: $10/1M input, $50/1M output (OpenRouter quote); GLM-5.2: $0.06/1M input, $7/1M output (OpenRouter quote). 1M input + 200K output (USD): $20.0000 vs $1.4600.

What are the context window sizes of Claude Fable 5.1 and GLM-5.2?

Claude Fable 5.1 provides a 1,000,000 token context window, while GLM-5.2 provides 1,048,576 tokens.

Compare current models

GLM-5.2 vs DeepSeek V4 Flash 0731 → GLM-5.2 vs DeepSeek V4 Pro 0813 →
Try Claude Fable 5.1 → Try GLM-5.2 →