How do GLM-5.3-Flash and Qwen3.8 27B 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 27B when publicly disclosed.

LLM Comparison

GLM-5.3-Flash vs Qwen3.8 27B: benchmark scores, pricing & comparison.

Q: Which is better: GLM-5.3-Flash or Qwen3.8 27B?

AskClash compares GLM-5.3-Flash and Qwen3.8 27B 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 27B 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 27B model page

Rank #7 vs #25AskClash overall scores 71.8 vs 46.0.

Pricing $0.15/$0.50 vs $0.45/$3.20Input and output token prices per 1M tokens when published.

Open Weight vs Open WeightZ.AI vs Alibaba.

GLM-5.3-Flash vs Qwen3.8 27B 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 27B
Overall Score	71.8	46.0
Leaderboard Rank	#7	#25
ACB	61.2	56.4
RWT	8.5	—
HLE	55.3	30.8
GPQA	91.2	89.2
IFEval	—	79.5
SWE-Pro	—	61.7
Terminal-Bench	84.3	73.0
DeepSWE	63.4	42.2
GDPval-AA	1773.0	—
OSWorld	—	84.3
Finance Agent	—	48.6
CharXiv	89.4	90.2
Input Price (per 1M tokens)	$0.15	$0.45
Output Price (per 1M tokens)	$0.50	$3.20
Context Window	1M	262K
Benchmarks Published	8	11

GLM-5.3-Flash vs Qwen3.8 27B head-to-head charts

GLM-5.3-Flash leads 6 and Qwen3.8 27B leads 1 of 7 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 27B

Overall

71.8GLM-5.3-Flash

46.0Qwen3.8 27B

ACB

61.2GLM-5.3-Flash

56.4Qwen3.8 27B

HLE

55.3GLM-5.3-Flash

30.8Qwen3.8 27B

GPQA

91.2GLM-5.3-Flash

89.2Qwen3.8 27B

Terminal-Bench

84.3GLM-5.3-Flash

73.0Qwen3.8 27B

DeepSWE

63.4GLM-5.3-Flash

42.2Qwen3.8 27B

CharXiv

89.4GLM-5.3-Flash

90.2Qwen3.8 27B

GLM-5.3-Flash

Input$0.15

Output$0.50

Workload$0.25

Context1M

Qwen3.8 27B

Input$0.45

Output$3.20

Workload$1.09

Context262K

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 27B comparisons

Explore how GLM-5.3-Flash and Qwen3.8 27B stack up against other top-ranked LLMs.

Claude Opus 5 vs GLM-5.3-Flash Claude Opus 5 vs Qwen3.8 27B Claude Fable 5 vs GLM-5.3-Flash Claude Fable 5 vs Qwen3.8 27B GLM-5.3 vs GLM-5.3-Flash GLM-5.3 vs Qwen3.8 27B Grok 4.6 vs GLM-5.3-Flash Grok 4.6 vs Qwen3.8 27B GPT-5.6 Sol vs GLM-5.3-Flash GPT-5.6 Sol vs Qwen3.8 27B

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.