Claude Sonnet 5 vs GPT-5.6 Luna
Select two models to compare their coding performance, pricing, and features.
Key Takeaways
| Metric | Claude Sonnet 5 | GPT-5.6 Luna |
|---|---|---|
| Coding Index | 105.06 | 104.63 |
| Coding tier | A | A |
| Input Price | $2/1M | $0.2/1M |
| Output Price | $10/1M | $1.2/1M |
| Context Window | 1M | 1.05M |
Coding Index
Coding Index · Reference baseline: 100
Integer index difference: 0.43
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
Claude Sonnet 5 Tier A
GPT-5.6 Luna Tier A
- •Real-time completions
- •High-volume inline suggestions
- •Cost-sensitive workflows
- •Copilot autocomplete
- •Fast, low-latency code completions
- •Strong quality at a lightweight price point
- •Great for high-volume inline suggestions
- •Excellent cost efficiency for everyday coding
Claude Sonnet 5
Benchmark evidence and ranking method · Claude Sonnet 5
Cross-checked
Terminal-Bench 4.0 · Claude Code · max: 12.42% · 95% CI ± 3.06%
Artificial Analysis · Claude Sonnet 5 (Adaptive Reasoning, Max Effort)
Test configuration: max
Terminal-Bench 2.1: 80.52% · SciCode: 54.28%
LiveBench · 2026-06-25 · claude-sonnet-5-xhigh-effort
Coding: 80.68% · Agentic Coding: 59.39%
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
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):
Claude Sonnet 5: $4.0000
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: Claude Sonnet 5 or GPT-5.6 Luna?
Claude Sonnet 5 has a higher reference index on the selected shared benchmark configuration than GPT-5.6 Luna (105.06 vs 104.63, a 0.43-point difference). This does not by itself establish general coding superiority. Editorial tiers: Claude Sonnet 5 = A; GPT-5.6 Luna = A. Tiers are separate editorial assessments and do not establish a reliable order within a tier.
How do Claude Sonnet 5 and GPT-5.6 Luna compare in API pricing and costs?
Claude Sonnet 5: $2/1M input, $10/1M output (OpenRouter quote); GPT-5.6 Luna: $0.2/1M input, $1.2/1M output (OpenRouter quote). 1M input + 200K output (USD): $4.0000 vs $0.4400.
What are the context window sizes of Claude Sonnet 5 and GPT-5.6 Luna?
Claude Sonnet 5 provides a 1,000,000 token context window, while GPT-5.6 Luna provides 1,050,000 tokens.