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 (260ms faster than Gemini 3.1 Pro) with 120 vs. 98 tok/s throughput. Gemini 3.1 Pro leads coding evaluations at 71.8% SWE-bench Verified vs. 56.0% for GLM-5.3 and 82.4% 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 3.1 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
$0.310
$0.090
GLM-5.3 is 3.4x cheaper
Prompt Cache Write / 1M
$1.25
$0.35
GLM-5.3 is 3.6x cheaper
Context Window (Max Output)
1,049k (66k out)
131k (16k out)
Gemini 3.1 Pro (8.0x larger context)
Avg TTFT Response Latency
420ms
160ms
GLM-5.3 (260ms faster)
Generation Speed (Throughput)
98 tok/s
120 tok/s
GLM-5.3 (+22 tok/s faster)
SWE-bench Verified (Coding)
71.8%
56%
Gemini 3.1 Pro (+15.8% lead)
GPQA Diamond (PhD STEM)
82.4%
67%
Gemini 3.1 Pro (+15.4% lead)
MMLU-Pro (Multi-Discipline)
89.5%
80.5%
Gemini 3.1 Pro (+9.0% lead)
MATH-500 (Quantitative Logic)
96.8%
88%
Gemini 3.1 Pro (+8.8% lead)
Inference Value Score (IVR)
60 / 100
69.9 / 100
GLM-5.3 (+9.9 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 3.1 Pro (Google) for complex enterprise architecture planning, multi-file code refactoring, legal/financial corpus auditing, and multimodal research — offering a 1,048k context window, 420ms TTFT, and $1.25/1M input pricing ($0.310/1M 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 3.1 Pro.
Which model has lower TTFT latency and higher tok/s throughput: Gemini 3.1 Pro or GLM-5.3?
GLM-5.3 delivers faster first-token latency at 160ms TTFT (120 tok/s throughput) compared to 420ms TTFT (98 tok/s) for Gemini 3.1 Pro.
How do Gemini 3.1 Pro and GLM-5.3 compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, Gemini 3.1 Pro scores 71.8% versus 56% for GLM-5.3. On GPQA Diamond graduate-level STEM reasoning, Gemini 3.1 Pro scores 82.4% versus 67% for GLM-5.3 (and 96.8% vs 88% on MATH-500).
When should you choose Gemini 3.1 Pro?
Choose Gemini 3.1 Pro (Google) for complex enterprise architecture planning, multi-file code refactoring, legal/financial corpus auditing, and multimodal research — offering a 1,048k context window, 420ms TTFT, and $1.25/1M input pricing ($0.310/1M 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).
