ACB
61.2 score
LLM Leaderboard · Open Weight
GLM-5.3-Flash benchmarks — leaderboard rank, pricing, and comparisons.
GLM-5.3-Flash ranks #20 on the AskClash LLM leaderboard with an overall score of 59.1. It scores 32.8 on Terminal-Bench. Compare GLM-5.3-Flash vs GPT, Claude, Gemini, DeepSeek, open-weight, and frontier AI models using public benchmark scores, token pricing, context window, and access details.
Rank #20AskClash overall score: 59.1
$0.15 / $0.50Input and output token price, when published. Context: 1M.
Visit websiteVisit the model provider's website.
GLM-5.3-Flash benchmark snapshot
AskClash combines public LLM benchmark cells into a weighted percentile score and penalizes missing coverage so narrow rows do not dominate better-measured models.
Overall59.1
Benchmark cells11
Context1M
CreatorZ.AI
GLM-5.3-Flash public benchmark scores
Cached benchmark values can include HLE, GPQA, SWE-bench, SWE-Pro, SWE-Atlas, Terminal-Bench, MCP Atlas, MMMU-Pro, ARC-AGI-2, Tau2, and model-specific coding or agent scores.
RWT
8.5 score
HLE
55.3 score
GPQA
91.2 score
Terminal-Bench
32.8 score
DeepSWE
63.4 score
GDPval-AA
1773.0 score
Finance Agent
57.9 score
CharXiv
89.4 score
GLM-5.3-Flash vs other AI models
Use these comparison links to evaluate GLM-5.3-Flash against nearby LLMs by benchmark score, price, context window, and provider.
Related AI and tech coverage
Cached AskClash article matches that can provide release, provider, benchmark, pricing, or market context around this model.
Z.ai GLM-Legal Beats Claude Fable 5 and Kimi K3 on Harvey LAB
Z.ai’s GLM-Legal is a legal AI model built on GLM-5.3-Flash. It scores 19.2% all-pass on Harvey’s Legal Agent Benchmark, nearly 3x its base, at $0.15 per test. Access is by application. The post Z.ai GLM-Legal Beats Claude Fable 5 and Kimi K3 on Harvey LAB app
When the KV Cache Is Slower Than the GPU: Scaling Remote KV Tiers for Context-Parallel Prefill
arXiv:2610.21500v1 Announce Type: new Abstract: Reusing the attention KV cache across requests is a standard way to cut prefill cost in long-context LLM serving, and production systems increasingly back the per-engine cache with a remote tier shared by every e
Open models have real benefits. Do they outweigh the risks?
MIT Technology Review Explains: Let our writers untangle the complex, messy world of science and technology to help you understand what’s coming next. You can read more from the series here. Late last month, Anthropic released a report arguing that the model G
Last cached leaderboard date: August 26, 2026. This model page is generated from the AskClash LLM Leaderboard cache and linked from the live leaderboard.