Which is better: GPT-6 Luna or GLM-5.2?

Verdict: GPT-6 Luna ranks higher (#99 vs #106) and wins 4 of 5 shared benchmarks; GPT-6 Luna costs less ($0.10/$0.50 vs $1.40/$4.40 per 1M tokens). AskClash compares GPT-6 Luna and GLM-5.2 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 GLM-5.2 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 GLM-5.2 when publicly disclosed.

LLM Comparison

GPT-6 Luna vs GLM-5.2: benchmark scores, pricing & comparison.

Side-by-side GPT-6 Luna vs GLM-5.2 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 #106) and wins 4 of 5 shared benchmarks; GPT-6 Luna costs less ($0.10/$0.50 vs $1.40/$4.40 per 1M tokens).

Open live leaderboard GPT-6 Luna model page GLM-5.2 model page

Rank #99 vs #106AskClash overall scores 49.8 vs 47.7.

Pricing $0.10/$0.50 vs $1.40/$4.40Input and output token prices per 1M tokens when published.

Proprietary vs Open WeightOpenAI vs Z.AI.

GPT-6 Luna vs GLM-5.2 benchmark comparison

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

Metric	GPT-6 Luna	GLM-5.2
Overall Score	49.8	47.7
Leaderboard Rank	#99	#106
ACB	50.2	—
RWT	—	8.5
Coding Agent Index	—	74.4
HLE	38.5	54.7
GPQA	—	91.2
IFEval	—	73.3
SWE-Pro	—	62.1
SWE-Atlas	—	74.4
DeepSWE	66.6	43.8
GDPval-AA	1431.9	1372.0
MCP Atlas	—	76.8
Finance Agent	49.9	49.7
MMMU-Pro	79.7	—
ARC-AGI 2	59.3	22.8
Tau2	—	99.1
Input Price (per 1M tokens)	$0.10	$1.40
Output Price (per 1M tokens)	$0.50	$4.40
Context Window	1.05M	1M
Benchmarks Published	10	13

GPT-6 Luna vs GLM-5.2 head-to-head charts

GPT-6 Luna leads 5 and GLM-5.2 leads 1 of 6 shared benchmarks. GPT-6 Luna is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

GPT-6 LunaGLM-5.2

Overall

49.8GPT-6 Luna

47.7GLM-5.2

HLE

38.5GPT-6 Luna

54.7GLM-5.2

DeepSWE

66.6GPT-6 Luna

43.8GLM-5.2

GDPval-AA

1431.9GPT-6 Luna

1372.0GLM-5.2

Finance Agent

49.9GPT-6 Luna

49.7GLM-5.2

ARC-AGI 2

59.3GPT-6 Luna

22.8GLM-5.2

GPT-6 Luna

Input$0.10

Output$0.50

Workload$0.20

Context1.05M

GLM-5.2

Input$1.40

Output$4.40

Workload$2.28

Context1M

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

More GPT-6 Luna and GLM-5.2 comparisons

Explore how GPT-6 Luna and GLM-5.2 stack up against other top-ranked LLMs.

Claude Opus 5.5 vs GPT-6 Luna Claude Opus 5.5 vs GLM-5.2 Claude Fable 5.1 vs GPT-6 Luna Claude Fable 5.1 vs GLM-5.2 Claude Sonnet 5.5 vs GPT-6 Luna Claude Sonnet 5.5 vs GLM-5.2 Claude Opus 5 vs GPT-6 Luna Claude Opus 5 vs GLM-5.2 Claude Fable 5 vs GPT-6 Luna Claude Fable 5 vs GLM-5.2

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.