GLM-5.3-Flash vs GPT-5.6 Luna

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

Key Takeaways

Coding tier: GLM-5.3-Flash has a higher reference index on the selected shared benchmark configuration than GPT-5.6 Luna (104.81 vs 104.63, a 0.18-point difference). This does not by itself establish general coding superiority. Editorial tiers: GLM-5.3-Flash = A; GPT-5.6 Luna = A. Tiers are separate editorial assessments and do not establish a reliable order within a tier.
API cost estimate: Estimated 1M in / 200K out tokens: $0.2500 (GLM-5.3-Flash) vs $0.4400 (GPT-5.6 Luna).
Metric GLM-5.3-Flash GPT-5.6 Luna
Coding Index
104.81
104.63
Coding tier A A
Input Price $0.15/1M $0.2/1M
Output Price $0.5/1M $1.2/1M
Context Window 1.05M 1.05M

Coding Index

Coding Index · Reference baseline: 100

GLM-5.3-Flash 104.81
GPT-5.6 Luna 104.63

Integer index difference: 0.18

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

Verdict

Both models share an editorial tier. The evidence does not establish a reliable order within it; compare them on your own projects.

Capabilities & Use Cases Comparison

GLM-5.3-Flash Tier A

Best For
  • •Coding assistance
  • •Long-context analysis
Strengths
  • •Native multimodal input
  • •Long-context coding and agent tasks

GPT-5.6 Luna Tier A

Best For
  • •Real-time completions
  • •High-volume inline suggestions
  • •Cost-sensitive workflows
  • •Copilot autocomplete
Strengths
  • •Fast, low-latency code completions
  • •Strong quality at a lightweight price point
  • •Great for high-volume inline suggestions
  • •Excellent cost efficiency for everyday coding

GLM-5.3-Flash

OpenRouter quote 2026-10-11
Benchmark evidence and ranking method · GLM-5.3-Flash

Cross-checked

Artificial Analysis · GLM-5.3-Flash

Test configuration: max

Terminal-Bench 2.1: 84.27% · SciCode: 51.62%

LiveBench · 2026-06-25 · glm-5.3-flash

Coding: 78.95% · Agentic Coding: 56.77%

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.

GPT-5.6 Luna

OpenRouter quote 2026-10-11
Benchmark evidence and ranking method · GPT-5.6 Luna

Cross-checked

Terminal-Bench 4.0 · Codex · max: 17.27% · 95% CI ± 2.85%

Artificial Analysis · GPT-5.6 Luna (max)

Test configuration: max

Terminal-Bench 2.1: 80.90% · SciCode: 53.59%

LiveBench · 2026-06-25 · gpt-5.6-luna-max

Coding: 82.91% · Agentic Coding: 48.43%

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):

GLM-5.3-Flash: $0.2500

GPT-5.6 Luna: $0.4400

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: GLM-5.3-Flash or GPT-5.6 Luna?

GLM-5.3-Flash has a higher reference index on the selected shared benchmark configuration than GPT-5.6 Luna (104.81 vs 104.63, a 0.18-point difference). This does not by itself establish general coding superiority. Editorial tiers: GLM-5.3-Flash = A; GPT-5.6 Luna = A. Tiers are separate editorial assessments and do not establish a reliable order within a tier.

How do GLM-5.3-Flash and GPT-5.6 Luna compare in API pricing and costs?

GLM-5.3-Flash: $0.15/1M input, $0.5/1M output (OpenRouter quote); GPT-5.6 Luna: $0.2/1M input, $1.2/1M output (OpenRouter quote). 1M input + 200K output (USD): $0.2500 vs $0.4400.

What are the context window sizes of GLM-5.3-Flash and GPT-5.6 Luna?

GLM-5.3-Flash provides a 1,048,576 token context window, while GPT-5.6 Luna provides 1,050,000 tokens.

Compare current models

GLM-5.3-Flash vs Claude Sonnet 5 → GLM-5.3-Flash vs Qwen3.8-Next-Flash → GPT-5.6 Luna vs Claude Sonnet 5 → GPT-5.6 Luna vs Qwen3.8-Next-Flash →
Try GLM-5.3-Flash → Try GPT-5.6 Luna →