Which model is better: GLM-5.3-Flash or Llama 4 Maverick?
GLM-5.3-Flash is 40.0% cheaper for input tokens ($0.12 vs. $0.20 per 1M tokens) and $0.40 vs. $0.60 for output tokens (1.7x cost advantage). In terms of operational performance, GLM-5.3-Flash delivers lower response latency at 90ms TTFT (45ms faster than Llama 4 Maverick) with 175 vs. 175 tok/s throughput. Llama 4 Maverick leads coding evaluations at 62.4% SWE-bench Verified vs. 52.8% for GLM-5.3-Flash and 73.8% vs. 63.5% on GPQA Diamond.
Verified daily via automated API latency tests and official documentation.
Zero-Shot RAG Verified
Verified Performance & Unit Economics Delta (12 Dimensions)
Daily synchronized metrics across token pricing, prompt cache economics, latency, throughput, and 5 capability benchmarks.
Metric / Dimension
GLM-5.3-Flash
Llama 4 Maverick
Net Delta / Advantage
Input Cost / 1M Tokens
$0.12
$0.20
GLM-5.3-Flash is 1.7x cheaper
Output Cost / 1M Tokens
$0.40
$0.60
GLM-5.3-Flash (33.3% lower)
Prompt Cache Read / 1M
$0.030
$0.050
GLM-5.3-Flash is 1.7x cheaper
Prompt Cache Write / 1M
$0.12
$0.20
GLM-5.3-Flash is 1.7x cheaper
Context Window (Max Output)
262k (16k out)
1,049k (33k out)
Llama 4 Maverick (4.0x larger context)
Avg TTFT Response Latency
90ms
135ms
GLM-5.3-Flash (45ms faster)
Generation Speed (Throughput)
175 tok/s
175 tok/s
Equal Throughput
SWE-bench Verified (Coding)
52.8%
62.4%
Llama 4 Maverick (+9.6% lead)
GPQA Diamond (PhD STEM)
63.5%
73.8%
Llama 4 Maverick (+10.3% lead)
MMLU-Pro (Multi-Discipline)
78.4%
84.5%
Llama 4 Maverick (+6.1% lead)
MATH-500 (Quantitative Logic)
84%
91%
Llama 4 Maverick (+7.0% lead)
Inference Value Score (IVR)
76.7 / 100
77 / 100
Llama 4 Maverick (+0.3 pts)
Interactive Monthly Token Economics & ROI Forecaster
Model production workloads across prompt, completion, and cached context tokens, or switch models on the fly.
Choose GLM-5.3-Flash (Zhipu AI) for sub-100ms ultra-fast inference model designed for real-time customer support, document classification, and agentic workflows — offering a 262k context window, 90ms TTFT, and $0.12/1M input pricing ($0.030/1M cached reads).
Which is cheaper: GLM-5.3-Flash or Llama 4 Maverick?
GLM-5.3-Flash is cheaper at $0.12/1M input tokens ($0.40/1M output) compared to $0.20/1M input ($0.60/1M output) for Llama 4 Maverick.
Which model has lower TTFT latency and higher tok/s throughput: GLM-5.3-Flash or Llama 4 Maverick?
GLM-5.3-Flash delivers faster first-token latency at 90ms TTFT (175 tok/s throughput) compared to 135ms TTFT (175 tok/s) for Llama 4 Maverick.
How do GLM-5.3-Flash and Llama 4 Maverick compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, GLM-5.3-Flash scores 52.8% versus 62.4% for Llama 4 Maverick. On GPQA Diamond graduate-level STEM reasoning, GLM-5.3-Flash scores 63.5% versus 73.8% for Llama 4 Maverick (and 84% vs 91% on MATH-500).
When should you choose GLM-5.3-Flash?
Choose GLM-5.3-Flash (Zhipu AI) for sub-100ms ultra-fast inference model designed for real-time customer support, document classification, and agentic workflows — offering a 262k context window, 90ms TTFT, and $0.12/1M input pricing ($0.030/1M cached reads).
When should you choose Llama 4 Maverick?
Choose Llama 4 Maverick (Meta) for high-throughput open-weight multimodal assistants, enterprise RAG, and low-cost cloud inference on Groq and Cerebras — offering a 1,048k context window, 135ms TTFT, and $0.20/1M input pricing ($0.050/1M cached reads).
