Which is better: Kimi K3.1 Pro or GLM-5.2?

Verdict: Kimi K3.1 Pro ranks higher (#4 vs #108) and wins 5 of 5 shared benchmarks; GLM-5.2 costs less ($1.40/$4.40 vs $2.00/$10.0 per 1M tokens). AskClash compares Kimi K3.1 Pro 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 Kimi K3.1 Pro 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 Kimi K3.1 Pro and GLM-5.2 when publicly disclosed.

LLM Comparison

Kimi K3.1 Pro vs GLM-5.2: benchmark scores, pricing & comparison.

Side-by-side Kimi K3.1 Pro 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: Kimi K3.1 Pro ranks higher (#4 vs #108) and wins 5 of 5 shared benchmarks; GLM-5.2 costs less ($1.40/$4.40 vs $2.00/$10.0 per 1M tokens).

Open live leaderboard Kimi K3.1 Pro model page GLM-5.2 model page

Rank #4 vs #108AskClash overall scores 75.5 vs 46.6.

Pricing $2.00/$10.0 vs $1.40/$4.40Input and output token prices per 1M tokens when published.

Open Weight vs Open WeightMoonshot AI vs Z.AI.

Kimi K3.1 Pro 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	Kimi K3.1 Pro	GLM-5.2
Overall Score	75.5	46.6
Leaderboard Rank	#4	#108
RWT	—	8.5
Coding Agent Index	—	74.4
HLE	70.0	54.7
GPQA	94.2	91.2
IFEval	—	73.3
SWE-Pro	—	62.1
SWE-Atlas	—	74.4
Terminal-Bench	65.7	—
DeepSWE	75.5	43.8
GDPval-AA	1846.0	1371.7
MCP Atlas	—	76.8
Finance Agent	58.6	49.7
CharXiv	89.0	—
MMMU-Pro	84.1	—
ARC-AGI 2	—	22.8
Tau2	—	99.1
Input Price (per 1M tokens)	$2.00	$1.40
Output Price (per 1M tokens)	$10.0	$4.40
Context Window	1M	1M
Benchmarks Published	9	13

Kimi K3.1 Pro vs GLM-5.2 head-to-head charts

Kimi K3.1 Pro leads 6 and GLM-5.2 leads 0 of 6 shared benchmarks. GLM-5.2 is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

Kimi K3.1 ProGLM-5.2

Overall

75.5Kimi K3.1 Pro

46.6GLM-5.2

HLE

70.0Kimi K3.1 Pro

54.7GLM-5.2

GPQA

94.2Kimi K3.1 Pro

91.2GLM-5.2

DeepSWE

75.5Kimi K3.1 Pro

43.8GLM-5.2

GDPval-AA

1846.0Kimi K3.1 Pro

1371.7GLM-5.2

Finance Agent

58.6Kimi K3.1 Pro

49.7GLM-5.2

Kimi K3.1 Pro

Input$2.00

Output$10.0

Workload$4.00

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 Kimi K3.1 Pro and GLM-5.2 comparisons

Explore how Kimi K3.1 Pro and GLM-5.2 stack up against other top-ranked LLMs.

Claude Opus 5.5 vs Kimi K3.1 Pro Claude Opus 5.5 vs GLM-5.2 Claude Fable 5.1 vs Kimi K3.1 Pro Claude Fable 5.1 vs GLM-5.2 Claude Sonnet 5.5 vs Kimi K3.1 Pro Claude Sonnet 5.5 vs GLM-5.2 Kimi K3.1 Pro vs Claude Opus 5 Claude Opus 5 vs GLM-5.2 Kimi K3.1 Pro vs Claude Fable 5 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.