How do Qwen3.8 Max and GPT-5.6 Luna compare on coding benchmarks?

The comparison table shows SWE-bench, SWE-Pro, SWE-Atlas, Terminal-Bench, DeepSWE, and coding agent index scores for both Qwen3.8 Max and GPT-5.6 Luna when publicly disclosed.

LLM Comparison

Qwen3.8 Max vs GPT-5.6 Luna: benchmark scores, pricing & comparison.

Q: Which is better: Qwen3.8 Max or GPT-5.6 Luna?

AskClash compares Qwen3.8 Max and GPT-5.6 Luna side by side across SWE-bench, GPQA, HLE, Terminal-Bench, coding agent scores, token pricing, and context window so you can see which model wins on each benchmark.

Side-by-side Qwen3.8 Max vs GPT-5.6 Luna comparison across SWE-bench, GPQA, HLE, Terminal-Bench, coding agent scores, token pricing, context window, and AskClash RWT. Green marks the winner on each benchmark.

Open live leaderboard Qwen3.8 Max model page GPT-5.6 Luna model page

Rank #5 vs #13AskClash overall scores 75.8 vs 65.5.

Pricing $2.00/$6.00 vs $1.00/$6.00Input and output token prices per 1M tokens when published.

Proprietary vs ProprietaryAlibaba vs OpenAI.

Qwen3.8 Max vs GPT-5.6 Luna benchmark comparison

Green cells highlight the winning model for each metric. Scores are cached from the AskClash LLM leaderboard snapshot.

Metric	Qwen3.8 Max	GPT-5.6 Luna
Overall Score	75.8	65.5
Leaderboard Rank	#5	#13
RWT	—	9.5
Coding Agent Index	—	75.0
HLE	43.6	—
GPQA	92.6	92.3
IFEval	82.8	—
SWE-Pro	67.7	62.7
SWE-Atlas	—	81.0
Terminal-Bench	86.6	84.7
DeepSWE	56.6	67.2
OSWorld	86.1	—
Finance Agent	—	55.0
CharXiv	88.4	—
MMMU-Pro	82.3	78.4
ARC-AGI 2	—	59.5
MRCR	92.9	41.3
Input Price (per 1M tokens)	$2.00	$1.00
Output Price (per 1M tokens)	$6.00	$6.00
Context Window	1M	1M
Benchmarks Published	11	13

Qwen3.8 Max vs GPT-5.6 Luna head-to-head charts

Qwen3.8 Max leads 6 and GPT-5.6 Luna leads 1 of 7 shared benchmarks. GPT-5.6 Luna is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

Qwen3.8 MaxGPT-5.6 Luna

Overall

75.8Qwen3.8 Max

65.5GPT-5.6 Luna

GPQA

92.6Qwen3.8 Max

92.3GPT-5.6 Luna

SWE-Pro

67.7Qwen3.8 Max

62.7GPT-5.6 Luna

Terminal-Bench

86.6Qwen3.8 Max

84.7GPT-5.6 Luna

DeepSWE

56.6Qwen3.8 Max

67.2GPT-5.6 Luna

MMMU-Pro

82.3Qwen3.8 Max

78.4GPT-5.6 Luna

MRCR

92.9Qwen3.8 Max

41.3GPT-5.6 Luna

Qwen3.8 Max

Input$2.00

Output$6.00

Workload$3.20

Context1M

GPT-5.6 Luna

Input$1.00

Output$6.00

Workload$2.20

Context1M

Workload = published cost of 1M input + 200K output tokens. Open the live leaderboard for interactive compare charts.

More Qwen3.8 Max and GPT-5.6 Luna comparisons

Explore how Qwen3.8 Max and GPT-5.6 Luna stack up against other top-ranked LLMs.

Claude Opus 5 vs Qwen3.8 Max Claude Opus 5 vs GPT-5.6 Luna Claude Fable 5 vs Qwen3.8 Max Claude Fable 5 vs GPT-5.6 Luna GPT-5.6 Sol vs Qwen3.8 Max GPT-5.6 Sol vs GPT-5.6 Luna Kimi K3 vs Qwen3.8 Max Kimi K3 vs GPT-5.6 Luna Qwen3.8 Max vs Claude Opus 4.8 Claude Opus 4.8 vs GPT-5.6 Luna

How to read this comparison

Benchmark scores

Higher is better for all benchmark scores (SWE-bench, GPQA, HLE, Terminal-Bench, etc.). Green marks the model with the higher score.

Token pricing

Lower is better for input and output prices. Green marks the cheaper model per 1M tokens.

Coverage matters

Models with fewer disclosed benchmark cells may have inflated percentile scores. Check the benchmark cell count for context.

This comparison page is generated from the AskClash LLM leaderboard cache. Open the live leaderboard for real-time scores and interactive filtering.