Which model is better: GLM-5.3 or Llama 4 Maverick?
Llama 4 Maverick is 42.9% cheaper for input tokens ($0.20 vs. $0.35 per 1M tokens) and $0.60 vs. $1.00 for output tokens (1.7x cost advantage). In terms of operational performance, Llama 4 Maverick delivers lower response latency at 135ms TTFT (25ms faster than GLM-5.3) with 175 vs. 120 tok/s throughput. Llama 4 Maverick leads coding evaluations at 62.4% SWE-bench Verified vs. 56.0% for GLM-5.3 and 73.8% vs. 67.0% 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
Llama 4 Maverick
Net Delta / Advantage
Input Cost / 1M Tokens
$0.35
$0.20
Llama 4 Maverick is 1.7x cheaper
Output Cost / 1M Tokens
$1.00
$0.60
Llama 4 Maverick (40.0% lower)
Prompt Cache Read / 1M
$0.090
$0.050
Llama 4 Maverick is 1.8x cheaper
Prompt Cache Write / 1M
$0.35
$0.20
Llama 4 Maverick is 1.7x cheaper
Context Window (Max Output)
131k (16k out)
1,049k (33k out)
Llama 4 Maverick (8.0x larger context)
Avg TTFT Response Latency
160ms
135ms
Llama 4 Maverick (25ms faster)
Generation Speed (Throughput)
120 tok/s
175 tok/s
Llama 4 Maverick (+55 tok/s faster)
SWE-bench Verified (Coding)
56%
62.4%
Llama 4 Maverick (+6.4% lead)
GPQA Diamond (PhD STEM)
67%
73.8%
Llama 4 Maverick (+6.8% lead)
MMLU-Pro (Multi-Discipline)
80.5%
84.5%
Llama 4 Maverick (+4.0% lead)
MATH-500 (Quantitative Logic)
88%
91%
Llama 4 Maverick (+3.0% lead)
Inference Value Score (IVR)
69.9 / 100
77 / 100
Llama 4 Maverick (+7.1 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 (Zhipu AI) for bilingual flagship foundation model optimized for cross-lingual reasoning, long-form structured analysis, and enterprise tool execution — offering a 131k context window, 160ms TTFT, and $0.35/1M input pricing ($0.090/1M cached reads).
Llama 4 Maverick is cheaper at $0.20/1M input tokens ($0.60/1M output) compared to $0.35/1M input ($1.00/1M output) for GLM-5.3.
Which model has lower TTFT latency and higher tok/s throughput: GLM-5.3 or Llama 4 Maverick?
Llama 4 Maverick delivers faster first-token latency at 135ms TTFT (175 tok/s throughput) compared to 160ms TTFT (120 tok/s) for GLM-5.3.
How do GLM-5.3 and Llama 4 Maverick compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, GLM-5.3 scores 56% versus 62.4% for Llama 4 Maverick. On GPQA Diamond graduate-level STEM reasoning, GLM-5.3 scores 67% versus 73.8% for Llama 4 Maverick (and 88% vs 91% on MATH-500).
When should you choose GLM-5.3?
Choose GLM-5.3 (Zhipu AI) for bilingual flagship foundation model optimized for cross-lingual reasoning, long-form structured analysis, and enterprise tool execution — offering a 131k context window, 160ms TTFT, and $0.35/1M input pricing ($0.090/1M 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).
