Which is better: GPT-6 Luna or Qwen3.7 Max?

Verdict: GPT-6 Luna ranks higher (#99 vs #105); Qwen3.7 Max wins 2 of 3 shared benchmarks; GPT-6 Luna costs less ($0.10/$0.50 vs $2.50/$7.50 per 1M tokens). AskClash compares GPT-6 Luna and Qwen3.7 Max side by side across SWE-bench, GPQA, HLE, Terminal-Bench, coding agent scores, token pricing, AskClash ACB, and context window so you can see which model wins on each benchmark.

How do GPT-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-6 Luna and Qwen3.7 Max when publicly disclosed.

LLM Comparison

GPT-6 Luna vs Qwen3.7 Max: benchmark scores, pricing & comparison.

Side-by-side GPT-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.

Verdict: GPT-6 Luna ranks higher (#99 vs #105); Qwen3.7 Max wins 2 of 3 shared benchmarks; GPT-6 Luna costs less ($0.10/$0.50 vs $2.50/$7.50 per 1M tokens).

Open live leaderboard GPT-6 Luna model page Qwen3.7 Max model page

Rank #99 vs #105AskClash overall scores 49.8 vs 48.5.

Pricing $0.10/$0.50 vs $2.50/$7.50Input and output token prices per 1M tokens when published.

Proprietary vs ProprietaryOpenAI vs Alibaba.

GPT-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-6 Luna	Qwen3.7 Max
Overall Score	49.8	48.5
Leaderboard Rank	#99	#105
ACB	50.2	—
RWT	—	7.5
HLE	38.5	41.4
GPQA	—	92.4
IFEval	—	94.3
SWE-bench	—	80.4
DeepSWE	66.6	—
GDPval-AA	1431.9	1546.0
MCP Atlas	—	76.4
Finance Agent	49.9	48.4
MMMU-Pro	79.7	—
ARC-AGI 2	59.3	—
Tau2	—	94.7
MRCR	—	90.4
Input Price (per 1M tokens)	$0.10	$2.50
Output Price (per 1M tokens)	$0.50	$7.50
Context Window	1.05M	1M
Benchmarks Published	10	8

GPT-6 Luna vs Qwen3.7 Max head-to-head charts

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

GPT-6 LunaQwen3.7 Max

Overall

49.8GPT-6 Luna

48.5Qwen3.7 Max

HLE

38.5GPT-6 Luna

41.4Qwen3.7 Max

GDPval-AA

1431.9GPT-6 Luna

1546.0Qwen3.7 Max

Finance Agent

49.9GPT-6 Luna

48.4Qwen3.7 Max

GPT-6 Luna

Input$0.10

Output$0.50

Workload$0.20

Context1.05M

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-6 Luna and Qwen3.7 Max comparisons

Explore how GPT-6 Luna and Qwen3.7 Max stack up against other top-ranked LLMs.

Claude Opus 5.5 vs GPT-6 Luna Claude Opus 5.5 vs Qwen3.7 Max Claude Fable 5.1 vs GPT-6 Luna Claude Fable 5.1 vs Qwen3.7 Max Claude Sonnet 5.5 vs GPT-6 Luna Claude Sonnet 5.5 vs Qwen3.7 Max Claude Opus 5 vs GPT-6 Luna Claude Opus 5 vs Qwen3.7 Max Claude Fable 5 vs GPT-6 Luna Claude Fable 5 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.