GPT-5.6 Luna vs Qwen3.8-Next-Flash

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

Key Takeaways

Coding tier: Qwen3.8-Next-Flash has a higher reference index on the selected shared benchmark configuration than GPT-5.6 Luna (104.89 vs 104.63, a 0.26-point difference). This does not by itself establish general coding superiority. Editorial tiers: GPT-5.6 Luna = A; Qwen3.8-Next-Flash = 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: $0.4400 (GPT-5.6 Luna) vs $0.2440 (Qwen3.8-Next-Flash).
Metric GPT-5.6 Luna Qwen3.8-Next-Flash
Coding Index
104.63
104.89
Coding tier A A
Input Price $0.2/1M $0.15/1M
Output Price $1.2/1M $0.47/1M
Context Window 1.05M 1M

Coding Index

Coding Index · Reference baseline: 100

GPT-5.6 Luna 104.63
Qwen3.8-Next-Flash 104.89

Integer index difference: 0.26

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

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

Qwen3.8-Next-Flash Tier A

Best For
  • •Coding assistance
  • •Document and codebase analysis
Strengths
  • •Text, image and video input
  • •Million-token context window

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.

Qwen3.8-Next-Flash

OpenRouter quote 2026-10-11
Benchmark evidence and ranking method · Qwen3.8-Next-Flash

Cross-checked

Artificial Analysis · Qwen3.8-Flash-Next

Test configuration: unspecified

Terminal-Bench 2.1: 86.14% · SciCode: 50.58%

LiveBench · 2026-06-25 · qwen3.8-flash-next

Coding: 72.55% · Agentic Coding: 61.62%

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):

GPT-5.6 Luna: $0.4400

Qwen3.8-Next-Flash: $0.2440

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: GPT-5.6 Luna or Qwen3.8-Next-Flash?

Qwen3.8-Next-Flash has a higher reference index on the selected shared benchmark configuration than GPT-5.6 Luna (104.89 vs 104.63, a 0.26-point difference). This does not by itself establish general coding superiority. Editorial tiers: GPT-5.6 Luna = A; Qwen3.8-Next-Flash = A. Tiers are separate editorial assessments and do not establish a reliable order within a tier.

How do GPT-5.6 Luna and Qwen3.8-Next-Flash compare in API pricing and costs?

GPT-5.6 Luna: $0.2/1M input, $1.2/1M output (OpenRouter quote); Qwen3.8-Next-Flash: $0.15/1M input, $0.47/1M output (OpenRouter quote). 1M input + 200K output (USD): $0.4400 vs $0.2440.

What are the context window sizes of GPT-5.6 Luna and Qwen3.8-Next-Flash?

GPT-5.6 Luna provides a 1,050,000 token context window, while Qwen3.8-Next-Flash provides 1,000,000 tokens.

Compare current models

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