Which model is better: DeepSeek V3 or GLM-5.3-Flash?
GLM-5.3-Flash is 14.3% cheaper for input tokens ($0.12 vs. $0.14 per 1M tokens) and $0.40 vs. $0.28 for output tokens (1.2x cost advantage). In terms of operational performance, GLM-5.3-Flash delivers lower response latency at 90ms TTFT (320ms faster than DeepSeek V3) with 175 vs. 78 tok/s throughput. GLM-5.3-Flash leads coding evaluations at 52.8% SWE-bench Verified vs. 42.0% for DeepSeek V3 and 63.5% vs. 59.1% 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
DeepSeek V3
GLM-5.3-Flash
Net Delta / Advantage
Input Cost / 1M Tokens
$0.14
$0.12
GLM-5.3-Flash is 1.2x cheaper
Output Cost / 1M Tokens
$0.28
$0.40
DeepSeek V3 (30.0% lower)
Prompt Cache Read / 1M
$0.014
$0.030
DeepSeek V3 is 2.1x cheaper
Prompt Cache Write / 1M
$0.14
$0.12
GLM-5.3-Flash is 1.2x cheaper
Context Window (Max Output)
131k (8k out)
262k (16k out)
GLM-5.3-Flash (2.0x larger context)
Avg TTFT Response Latency
410ms
90ms
GLM-5.3-Flash (320ms faster)
Generation Speed (Throughput)
78 tok/s
175 tok/s
GLM-5.3-Flash (+97 tok/s faster)
SWE-bench Verified (Coding)
42%
52.8%
GLM-5.3-Flash (+10.8% lead)
GPQA Diamond (PhD STEM)
59.1%
63.5%
GLM-5.3-Flash (+4.4% lead)
MMLU-Pro (Multi-Discipline)
75.9%
78.4%
GLM-5.3-Flash (+2.5% lead)
MATH-500 (Quantitative Logic)
90.2%
84%
DeepSeek V3 (+6.2% lead)
Inference Value Score (IVR)
75 / 100
76.7 / 100
GLM-5.3-Flash (+1.7 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).
GLM-5.3-Flash is cheaper at $0.12/1M input tokens ($0.40/1M output) compared to $0.14/1M input ($0.28/1M output) for DeepSeek V3.
Which model has lower TTFT latency and higher tok/s throughput: DeepSeek V3 or GLM-5.3-Flash?
GLM-5.3-Flash delivers faster first-token latency at 90ms TTFT (175 tok/s throughput) compared to 410ms TTFT (78 tok/s) for DeepSeek V3.
How do DeepSeek V3 and GLM-5.3-Flash compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, DeepSeek V3 scores 42% versus 52.8% for GLM-5.3-Flash. On GPQA Diamond graduate-level STEM reasoning, DeepSeek V3 scores 59.1% versus 63.5% for GLM-5.3-Flash (and 90.2% vs 84% on MATH-500).
When should you choose DeepSeek V3?
Choose DeepSeek V3 (DeepSeek) for cost-sensitive batch text processing, drafting, and open-weight self-hosted inference — offering a 131k context window, 410ms TTFT, and $0.14/1M input pricing ($0.014/1M cached reads).
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).
