Which is better: GLM-5.3-Flash or SWE-2?

Verdict: GLM-5.3-Flash ranks higher (#21 vs #22) and wins 6 of 9 shared benchmarks; SWE-2 costs less ($0/$0 vs $0.15/$0.50 per 1M tokens). AskClash compares GLM-5.3-Flash and SWE-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 GLM-5.3-Flash and SWE-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 GLM-5.3-Flash and SWE-2 when publicly disclosed.

LLM Comparison

GLM-5.3-Flash vs SWE-2: benchmark scores, pricing & comparison.

Side-by-side GLM-5.3-Flash vs SWE-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: GLM-5.3-Flash ranks higher (#21 vs #22) and wins 6 of 9 shared benchmarks; SWE-2 costs less ($0/$0 vs $0.15/$0.50 per 1M tokens).

Open live leaderboard GLM-5.3-Flash model page SWE-2 model page

Rank #21 vs #22AskClash overall scores 55.8 vs 55.3.

Pricing $0.15/$0.50 vs $0/$0Input and output token prices per 1M tokens when published.

Open Weight vs ProprietaryZ.AI vs Cognition.

GLM-5.3-Flash vs SWE-2 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	SWE-2
Overall Score	55.8	55.3
Leaderboard Rank	#21	#22
ACB	61.2	60.0
RWT	8.5	8.5
Coding Agent Index	—	51.9
HLE	55.3	43.5
GPQA	91.2	93.5
SWE-Atlas	—	66.1
Terminal-Bench	32.8	27.3
DeepSWE	63.4	73.0
GDPval-AA	1773.0	1668.0
MCP Atlas	—	84.2
Finance Agent	57.9	54.4
CharXiv	89.4	84.8
MMMU-Pro	—	81.6
Input Price (per 1M tokens)	$0.15	$0
Output Price (per 1M tokens)	$0.50	$0
Context Window	1M	256K
Benchmarks Published	11	13

GLM-5.3-Flash vs SWE-2 head-to-head charts

GLM-5.3-Flash leads 7 and SWE-2 leads 2 of 10 shared benchmarks. SWE-2 is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

GLM-5.3-FlashSWE-2

Overall

55.8GLM-5.3-Flash

55.3SWE-2

ACB

61.2GLM-5.3-Flash

60.0SWE-2

RWT

8.5GLM-5.3-Flash

8.5SWE-2

HLE

55.3GLM-5.3-Flash

43.5SWE-2

GPQA

91.2GLM-5.3-Flash

93.5SWE-2

Terminal-Bench

32.8GLM-5.3-Flash

27.3SWE-2

DeepSWE

63.4GLM-5.3-Flash

73.0SWE-2

GDPval-AA

1773.0GLM-5.3-Flash

1668.0SWE-2

Finance Agent

57.9GLM-5.3-Flash

54.4SWE-2

CharXiv

89.4GLM-5.3-Flash

84.8SWE-2

GLM-5.3-Flash

Input$0.15

Output$0.50

Workload$0.25

Context1M

SWE-2

Input$0

Output$0

Workload$0.00

Context256K

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

More GLM-5.3-Flash and SWE-2 comparisons

Explore how GLM-5.3-Flash and SWE-2 stack up against other top-ranked LLMs.

Claude Opus 5.5 vs GLM-5.3-Flash Claude Opus 5.5 vs SWE-2 Claude Sonnet 5.5 vs GLM-5.3-Flash Claude Sonnet 5.5 vs SWE-2 Claude Fable 5.1 vs GLM-5.3-Flash Claude Fable 5.1 vs SWE-2 Claude Opus 5 vs GLM-5.3-Flash Claude Opus 5 vs SWE-2 Claude Fable 5 vs GLM-5.3-Flash Claude Fable 5 vs SWE-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.