Which model is better: GLM-5.3-Flash or GPT-6 Astra?
GLM-5.3-Flash is 98.8% cheaper for input tokens ($0.12 vs. $10.00 per 1M tokens) and $0.40 vs. $50.00 for output tokens (83.3x cost advantage). In terms of operational performance, GLM-5.3-Flash delivers lower response latency at 90ms TTFT (650ms faster than GPT-6 Astra) with 175 vs. 74 tok/s throughput. GPT-6 Astra leads coding evaluations at 75.7% SWE-bench Verified vs. 52.8% for GLM-5.3-Flash and 96.0% 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
GPT-6 Astra
Net Delta / Advantage
Input Cost / 1M Tokens
$0.12
$10.00
GLM-5.3-Flash is 83.3x cheaper
Output Cost / 1M Tokens
$0.40
$50.00
GLM-5.3-Flash (99.2% lower)
Prompt Cache Read / 1M
$0.030
$1.000
GLM-5.3-Flash is 33.3x cheaper
Prompt Cache Write / 1M
$0.12
$12.50
GLM-5.3-Flash is 104.2x cheaper
Context Window (Max Output)
262k (16k out)
1,075k (128k out)
GPT-6 Astra (4.1x larger context)
Avg TTFT Response Latency
90ms
740ms
GLM-5.3-Flash (650ms faster)
Generation Speed (Throughput)
175 tok/s
74 tok/s
GLM-5.3-Flash (+101 tok/s faster)
SWE-bench Verified (Coding)
52.8%
75.7%
GPT-6 Astra (+22.9% lead)
GPQA Diamond (PhD STEM)
63.5%
96%
GPT-6 Astra (+32.5% lead)
MMLU-Pro (Multi-Discipline)
78.4%
91.2%
GPT-6 Astra (+12.8% lead)
MATH-500 (Quantitative Logic)
84%
98%
GPT-6 Astra (+14.0% lead)
Inference Value Score (IVR)
76.7 / 100
47.6 / 100
GLM-5.3-Flash (+29.1 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-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 $10.00/1M input ($50.00/1M output) for GPT-6 Astra.
Which model has lower TTFT latency and higher tok/s throughput: GLM-5.3-Flash or GPT-6 Astra?
GLM-5.3-Flash delivers faster first-token latency at 90ms TTFT (175 tok/s throughput) compared to 740ms TTFT (74 tok/s) for GPT-6 Astra.
How do GLM-5.3-Flash and GPT-6 Astra compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, GLM-5.3-Flash scores 52.8% versus 75.7% for GPT-6 Astra. On GPQA Diamond graduate-level STEM reasoning, GLM-5.3-Flash scores 63.5% versus 96% for GPT-6 Astra (and 84% vs 98% 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 GPT-6 Astra?
Choose GPT-6 Astra (OpenAI) for high-autonomy flagship reasoning model engineered for end-to-end computer and browser use, advanced scientific research, and complex multi-turn software engineering — offering a 1,075k context window, 740ms TTFT, and $10.00/1M input pricing ($1.000/1M cached reads).
