GLM-5.3 is 72.0% cheaper for input tokens ($0.35 vs. $1.25 per 1M tokens) and $1.00 vs. $10.00 for output tokens (3.6x cost advantage). In terms of operational performance, GLM-5.3 delivers lower response latency at 160ms TTFT (540ms faster than Gemini 2.5 Pro) with 120 vs. 80 tok/s throughput. Gemini 2.5 Pro leads coding evaluations at 63.8% SWE-bench Verified vs. 56.0% for GLM-5.3 and 74.0% 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
Gemini 2.5 Pro
GLM-5.3
Net Delta / Advantage
Input Cost / 1M Tokens
$1.25
$0.35
GLM-5.3 is 3.6x cheaper
Output Cost / 1M Tokens
$10.00
$1.00
GLM-5.3 (90.0% lower)
Prompt Cache Read / 1M
$0.313
$0.090
GLM-5.3 is 3.5x cheaper
Prompt Cache Write / 1M
$1.25
$0.35
GLM-5.3 is 3.6x cheaper
Context Window (Max Output)
1,024k (66k out)
131k (16k out)
Gemini 2.5 Pro (7.8x larger context)
Avg TTFT Response Latency
700ms
160ms
GLM-5.3 (540ms faster)
Generation Speed (Throughput)
80 tok/s
120 tok/s
GLM-5.3 (+40 tok/s faster)
SWE-bench Verified (Coding)
63.8%
56%
Gemini 2.5 Pro (+7.8% lead)
GPQA Diamond (PhD STEM)
74%
67%
Gemini 2.5 Pro (+7.0% lead)
MMLU-Pro (Multi-Discipline)
85.2%
80.5%
Gemini 2.5 Pro (+4.7% lead)
MATH-500 (Quantitative Logic)
95%
88%
Gemini 2.5 Pro (+7.0% lead)
Inference Value Score (IVR)
56.8 / 100
69.9 / 100
GLM-5.3 (+13.1 pts)
Interactive Monthly Token Economics & ROI Forecaster
Model production workloads across prompt, completion, and cached context tokens, or switch models on the fly.
Choose Gemini 2.5 Pro (Google) for long-context document and video analysis, large codebase ingestion, and multimodal reasoning — offering a 1,024k context window, 700ms TTFT, and $1.25/1M input pricing ($0.312/1M cached reads).
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).
GLM-5.3 is cheaper at $0.35/1M input tokens ($1.00/1M output) compared to $1.25/1M input ($10.00/1M output) for Gemini 2.5 Pro.
Which model has lower TTFT latency and higher tok/s throughput: Gemini 2.5 Pro or GLM-5.3?
GLM-5.3 delivers faster first-token latency at 160ms TTFT (120 tok/s throughput) compared to 700ms TTFT (80 tok/s) for Gemini 2.5 Pro.
How do Gemini 2.5 Pro and GLM-5.3 compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, Gemini 2.5 Pro scores 63.8% versus 56% for GLM-5.3. On GPQA Diamond graduate-level STEM reasoning, Gemini 2.5 Pro scores 74% versus 67% for GLM-5.3 (and 95% vs 88% on MATH-500).
When should you choose Gemini 2.5 Pro?
Choose Gemini 2.5 Pro (Google) for long-context document and video analysis, large codebase ingestion, and multimodal reasoning — offering a 1,024k context window, 700ms TTFT, and $1.25/1M input pricing ($0.312/1M cached reads).
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).
