LLM Comparison

GLM-5.3-Flash vs Kimi K2.7: benchmark scores, pricing & comparison.

Side-by-side GLM-5.3-Flash vs Kimi K2.7 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 (#19 vs #41) and wins 6 of 6 shared benchmarks; GLM-5.3-Flash costs less ($0.15/$0.50 vs $0.95/$4.00 per 1M tokens).

Rank #19 vs #41AskClash overall scores 59.6 vs 42.0.
Pricing $0.15/$0.50 vs $0.95/$4.00Input and output token prices per 1M tokens when published.
Open Weight vs Open WeightZ.AI vs Moonshot AI.

GLM-5.3-Flash vs Kimi K2.7 benchmark comparison

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

MetricGLM-5.3-FlashKimi K2.7
Overall Score59.642.0
Leaderboard Rank#19#41
ACB61.243.0
RWT8.57.5
HLE55.335.0
GPQA91.289.6
IFEval—63.1
Terminal-Bench32.8—
DeepSWE63.430.5
GDPval-AA1773.01037.2
MCP Atlas—76.0
Finance Agent57.9—
CharXiv89.4—
Tau2—90.1
Input Price (per 1M tokens)$0.15$0.95
Output Price (per 1M tokens)$0.50$4.00
Context Window1M256K
Benchmarks Published119

GLM-5.3-Flash vs Kimi K2.7 head-to-head charts

GLM-5.3-Flash leads 7 and Kimi K2.7 leads 0 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-FlashKimi K2.7
Overall
59.6GLM-5.3-Flash
42.0Kimi K2.7
ACB
61.2GLM-5.3-Flash
43.0Kimi K2.7
RWT
8.5GLM-5.3-Flash
7.5Kimi K2.7
HLE
55.3GLM-5.3-Flash
35.0Kimi K2.7
GPQA
91.2GLM-5.3-Flash
89.6Kimi K2.7
DeepSWE
63.4GLM-5.3-Flash
30.5Kimi K2.7
GDPval-AA
1773.0GLM-5.3-Flash
1037.2Kimi K2.7
GLM-5.3-Flash
Input$0.15
Output$0.50
Workload$0.25
Context1M
Kimi K2.7
Input$0.95
Output$4.00
Workload$1.75
Context256K

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

More GLM-5.3-Flash and Kimi K2.7 comparisons

Explore how GLM-5.3-Flash and Kimi K2.7 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.