LLM Comparison

Kimi K3 vs GLM-5.3: benchmark scores, pricing & comparison.

Side-by-side Kimi K3 vs GLM-5.3 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 ranks higher (#13 vs #14); GLM-5.3 wins 6 of 8 shared benchmarks; GLM-5.3 costs less ($1.40/$4.40 vs $3.00/$15.0 per 1M tokens).

Rank #13 vs #14AskClash overall scores 65.9 vs 65.9.
Pricing $3.00/$15.0 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 vs GLM-5.3 benchmark comparison

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

MetricKimi K3GLM-5.3
Overall Score65.965.9
Leaderboard Rank#13#14
ACB57.762.2
RWT8.08.5
Coding Agent Index51.9—
HLE43.562.5
GPQA93.591.7
SWE-Atlas66.1—
Terminal-Bench88.388.2
DeepSWE68.569.0
GDPval-AA1668.01769.0
MCP Atlas84.2—
Finance Agent54.455.8
CharXiv84.8—
MMMU-Pro81.6—
ARC-AGI 260.4—
Input Price (per 1M tokens)$3.00$1.40
Output Price (per 1M tokens)$15.0$4.40
Context Window1M1M
Benchmarks Published1610

Kimi K3 vs GLM-5.3 head-to-head charts

Kimi K3 leads 3 and GLM-5.3 leads 6 of 9 shared benchmarks. GLM-5.3 is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

Kimi K3GLM-5.3
Overall
65.9Kimi K3
65.9GLM-5.3
ACB
57.7Kimi K3
62.2GLM-5.3
RWT
8.0Kimi K3
8.5GLM-5.3
HLE
43.5Kimi K3
62.5GLM-5.3
GPQA
93.5Kimi K3
91.7GLM-5.3
Terminal-Bench
88.3Kimi K3
88.2GLM-5.3
DeepSWE
68.5Kimi K3
69.0GLM-5.3
GDPval-AA
1668.0Kimi K3
1769.0GLM-5.3
Finance Agent
54.4Kimi K3
55.8GLM-5.3
Kimi K3
Input$3.00
Output$15.0
Workload$6.00
Context1M
GLM-5.3
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 and GLM-5.3 comparisons

Explore how Kimi K3 and GLM-5.3 stack up against other top-ranked LLMs.

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.