How do GPT-5.6 Luna and Qwen3.7 Max compare on coding benchmarks?

The comparison table shows SWE-bench, SWE-Pro, SWE-Atlas, Terminal-Bench, DeepSWE, and coding agent index scores for both GPT-5.6 Luna and Qwen3.7 Max when publicly disclosed.

LLM Comparison

GPT-5.6 Luna vs Qwen3.7 Max: benchmark scores, pricing & comparison.

Q: Which is better: GPT-5.6 Luna or Qwen3.7 Max?

AskClash compares GPT-5.6 Luna and Qwen3.7 Max 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 GPT-5.6 Luna vs Qwen3.7 Max 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 GPT-5.6 Luna model page Qwen3.7 Max model page

Rank #12 vs #15AskClash overall scores 66.2 vs 57.2.

Pricing $1.00/$6.00 vs $2.50/$7.50Input and output token prices per 1M tokens when published.

Proprietary vs ProprietaryOpenAI vs Alibaba.

GPT-5.6 Luna vs Qwen3.7 Max benchmark comparison

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

Metric	GPT-5.6 Luna	Qwen3.7 Max
Overall Score	66.2	57.2
Leaderboard Rank	#12	#15
RWT	9.5	7.5
Coding Agent Index	75.0	—
HLE	—	41.4
GPQA	92.3	92.4
IFEval	—	94.3
SWE-bench	—	80.4
SWE-Pro	62.7	—
SWE-Atlas	81.0	—
Terminal-Bench	84.7	69.7
DeepSWE	67.2	—
MCP Atlas	—	76.4
Finance Agent	55.0	48.4
MMMU-Pro	78.4	—
ARC-AGI 2	59.5	—
Tau2	—	94.7
MRCR	41.3	90.4
Input Price (per 1M tokens)	$1.00	$2.50
Output Price (per 1M tokens)	$6.00	$7.50
Context Window	1M	1M
Benchmarks Published	13	11

GPT-5.6 Luna vs Qwen3.7 Max head-to-head charts

GPT-5.6 Luna leads 4 and Qwen3.7 Max leads 2 of 6 shared benchmarks. GPT-5.6 Luna is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

GPT-5.6 LunaQwen3.7 Max

Overall

66.2GPT-5.6 Luna

57.2Qwen3.7 Max

RWT

9.5GPT-5.6 Luna

7.5Qwen3.7 Max

GPQA

92.3GPT-5.6 Luna

92.4Qwen3.7 Max

Terminal-Bench

84.7GPT-5.6 Luna

69.7Qwen3.7 Max

Finance Agent

55.0GPT-5.6 Luna

48.4Qwen3.7 Max

MRCR

41.3GPT-5.6 Luna

90.4Qwen3.7 Max

GPT-5.6 Luna

Input$1.00

Output$6.00

Workload$2.20

Context1M

Qwen3.7 Max

Input$2.50

Output$7.50

Workload$4.00

Context1M

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

More GPT-5.6 Luna and Qwen3.7 Max comparisons

Explore how GPT-5.6 Luna and Qwen3.7 Max stack up against other top-ranked LLMs.

Claude Opus 5 vs GPT-5.6 Luna Claude Opus 5 vs Qwen3.7 Max Claude Fable 5 vs GPT-5.6 Luna Claude Fable 5 vs Qwen3.7 Max GPT-5.6 Sol vs GPT-5.6 Luna GPT-5.6 Sol vs Qwen3.7 Max Kimi K3 vs GPT-5.6 Luna Kimi K3 vs Qwen3.7 Max Claude Opus 4.8 vs GPT-5.6 Luna Claude Opus 4.8 vs Qwen3.7 Max

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.