How do GLM-5.3-Flash and Qwen3.8 Max 0902 compare on coding benchmarks?

The comparison table shows SWE-bench, SWE-Pro, SWE-Atlas, Terminal-Bench, DeepSWE, and coding agent index scores for both GLM-5.3-Flash and Qwen3.8 Max 0902 when publicly disclosed.

LLM Comparison

GLM-5.3-Flash vs Qwen3.8 Max 0902: benchmark scores, pricing & comparison.

Q: Which is better: GLM-5.3-Flash or Qwen3.8 Max 0902?

AskClash compares GLM-5.3-Flash and Qwen3.8 Max 0902 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.

Side-by-side GLM-5.3-Flash vs Qwen3.8 Max 0902 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 GLM-5.3-Flash model page Qwen3.8 Max 0902 model page

Rank #12 vs #27AskClash overall scores 61.9 vs 39.0.

Pricing $0.15/$0.50 vs $2.00/$6.00Input and output token prices per 1M tokens when published.

Open Weight vs ProprietaryZ.AI vs Alibaba.

GLM-5.3-Flash vs Qwen3.8 Max 0902 benchmark comparison

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

Metric	GLM-5.3-Flash	Qwen3.8 Max 0902
Overall Score	61.9	39.0
Leaderboard Rank	#12	#27
ACB	61.2	—
RWT	8.5	—
HLE	55.3	43.6
GPQA	91.2	92.6
IFEval	—	82.8
SWE-Pro	—	67.7
SWE-Atlas	—	66.3
Terminal-Bench	84.3	86.6
DeepSWE	63.4	69.3
GDPval-AA	1773.0	—
OSWorld	—	86.1
Finance Agent	57.9	—
CharXiv	89.4	88.4
MMMU-Pro	—	82.7
MRCR	—	92.9
Input Price (per 1M tokens)	$0.15	$2.00
Output Price (per 1M tokens)	$0.50	$6.00
Context Window	1M	1M
Benchmarks Published	9	11

GLM-5.3-Flash vs Qwen3.8 Max 0902 head-to-head charts

GLM-5.3-Flash leads 3 and Qwen3.8 Max 0902 leads 3 of 6 shared benchmarks. GLM-5.3-Flash is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

GLM-5.3-FlashQwen3.8 Max 0902

Overall

61.9GLM-5.3-Flash

39.0Qwen3.8 Max 0902

HLE

55.3GLM-5.3-Flash

43.6Qwen3.8 Max 0902

GPQA

91.2GLM-5.3-Flash

92.6Qwen3.8 Max 0902

Terminal-Bench

84.3GLM-5.3-Flash

86.6Qwen3.8 Max 0902

DeepSWE

63.4GLM-5.3-Flash

69.3Qwen3.8 Max 0902

CharXiv

89.4GLM-5.3-Flash

88.4Qwen3.8 Max 0902

GLM-5.3-Flash

Input$0.15

Output$0.50

Workload$0.25

Context1M

Qwen3.8 Max 0902

Input$2.00

Output$6.00

Workload$3.20

Context1M

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

More GLM-5.3-Flash and Qwen3.8 Max 0902 comparisons

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Claude Fable 5.1 vs GLM-5.3-Flash Claude Fable 5.1 vs Qwen3.8 Max 0902 Claude Fable 5 vs GLM-5.3-Flash Claude Fable 5 vs Qwen3.8 Max 0902 GPT-5.6 Sol vs GLM-5.3-Flash GPT-5.6 Sol vs Qwen3.8 Max 0902 Claude Opus 5 vs GLM-5.3-Flash Claude Opus 5 vs Qwen3.8 Max 0902 Grok 4.6 vs GLM-5.3-Flash Grok 4.6 vs Qwen3.8 Max 0902

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.