How do Qwen3.8 Max 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 Qwen3.8 Max and GLM-5.2 when publicly disclosed.

LLM Comparison

Qwen3.8 Max vs GLM-5.2: benchmark scores, pricing & comparison.

Q: Which is better: Qwen3.8 Max or GLM-5.2?

AskClash compares Qwen3.8 Max and GLM-5.2 side by side across SWE-bench, GPQA, HLE, Terminal-Bench, coding agent scores, token pricing, and context window so you can see which model wins on each benchmark.

Side-by-side Qwen3.8 Max 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.

Open live leaderboard Qwen3.8 Max model page GLM-5.2 model page

Rank #13 vs #18AskClash overall scores 64.0 vs 54.2.

Pricing $2.00/$6.00 vs $1.40/$4.40Input and output token prices per 1M tokens when published.

Proprietary vs Open WeightAlibaba vs Z.AI.

Qwen3.8 Max 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	Qwen3.8 Max	GLM-5.2
Overall Score	64.0	54.2
Leaderboard Rank	#13	#18
RWT	—	8.5
Coding Agent Index	—	74.4
HLE	43.6	54.7
GPQA	92.6	91.2
IFEval	82.8	73.3
SWE-Pro	67.7	62.1
SWE-Atlas	—	74.4
Terminal-Bench	86.6	82.7
DeepSWE	56.6	43.8
OSWorld	86.1	—
MCP Atlas	—	76.8
CharXiv	88.4	—
MMMU-Pro	82.3	—
Tau2	—	99.1
MRCR	92.9	—
Input Price (per 1M tokens)	$2.00	$1.40
Output Price (per 1M tokens)	$6.00	$4.40
Context Window	1M	1M
Benchmarks Published	10	11

Qwen3.8 Max vs GLM-5.2 head-to-head charts

Qwen3.8 Max leads 6 and GLM-5.2 leads 1 of 7 shared benchmarks. GLM-5.2 is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

Qwen3.8 MaxGLM-5.2

Overall

64.0Qwen3.8 Max

54.2GLM-5.2

HLE

43.6Qwen3.8 Max

54.7GLM-5.2

GPQA

92.6Qwen3.8 Max

91.2GLM-5.2

IFEval

82.8Qwen3.8 Max

73.3GLM-5.2

SWE-Pro

67.7Qwen3.8 Max

62.1GLM-5.2

Terminal-Bench

86.6Qwen3.8 Max

82.7GLM-5.2

DeepSWE

56.6Qwen3.8 Max

43.8GLM-5.2

Qwen3.8 Max

Input$2.00

Output$6.00

Workload$3.20

Context1M

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 Qwen3.8 Max and GLM-5.2 comparisons

Explore how Qwen3.8 Max and GLM-5.2 stack up against other top-ranked LLMs.

Claude Opus 5 vs Qwen3.8 Max Claude Opus 5 vs GLM-5.2 Claude Fable 5 vs Qwen3.8 Max Claude Fable 5 vs GLM-5.2 GPT-5.6 Sol vs Qwen3.8 Max GPT-5.6 Sol vs GLM-5.2 Kimi K3 vs Qwen3.8 Max Kimi K3 vs GLM-5.2 Claude Opus 4.8 vs Qwen3.8 Max Claude Opus 4.8 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.