Which model is better: GLM-5.3-Flash or Qwen 3.8 Flash Next?
GLM-5.3-Flash is 25.0% cheaper for input tokens ($0.12 vs. $0.16 per 1M tokens) and $0.40 vs. $0.47 for output tokens (1.3x cost advantage). In terms of operational performance, GLM-5.3-Flash delivers lower response latency at 90ms TTFT (190ms faster than Qwen 3.8 Flash Next) with 175 vs. 185 tok/s throughput. Qwen 3.8 Flash Next leads coding evaluations at 81.0% SWE-bench Verified vs. 52.8% for GLM-5.3-Flash and 91.7% 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
Qwen 3.8 Flash Next
Net Delta / Advantage
Input Cost / 1M Tokens
$0.12
$0.16
GLM-5.3-Flash is 1.3x cheaper
Output Cost / 1M Tokens
$0.40
$0.47
GLM-5.3-Flash (14.9% lower)
Prompt Cache Read / 1M
$0.030
$0.016
Qwen 3.8 Flash Next is 1.9x cheaper
Prompt Cache Write / 1M
$0.12
$0.20
GLM-5.3-Flash is 1.7x cheaper
Context Window (Max Output)
262k (16k out)
268k (66k out)
Qwen 3.8 Flash Next (1.0x larger context)
Avg TTFT Response Latency
90ms
280ms
GLM-5.3-Flash (190ms faster)
Generation Speed (Throughput)
175 tok/s
185 tok/s
Qwen 3.8 Flash Next (+10 tok/s faster)
SWE-bench Verified (Coding)
52.8%
81%
Qwen 3.8 Flash Next (+28.2% lead)
GPQA Diamond (PhD STEM)
63.5%
91.7%
Qwen 3.8 Flash Next (+28.2% lead)
MMLU-Pro (Multi-Discipline)
78.4%
88.4%
Qwen 3.8 Flash Next (+10.0% lead)
MATH-500 (Quantitative Logic)
84%
95.7%
Qwen 3.8 Flash Next (+11.7% lead)
Inference Value Score (IVR)
76.7 / 100
85.1 / 100
Qwen 3.8 Flash Next (+8.4 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).
Which is cheaper: GLM-5.3-Flash or Qwen 3.8 Flash Next?
GLM-5.3-Flash is cheaper at $0.12/1M input tokens ($0.40/1M output) compared to $0.16/1M input ($0.47/1M output) for Qwen 3.8 Flash Next.
Which model has lower TTFT latency and higher tok/s throughput: GLM-5.3-Flash or Qwen 3.8 Flash Next?
GLM-5.3-Flash delivers faster first-token latency at 90ms TTFT (175 tok/s throughput) compared to 280ms TTFT (185 tok/s) for Qwen 3.8 Flash Next.
How do GLM-5.3-Flash and Qwen 3.8 Flash Next compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, GLM-5.3-Flash scores 52.8% versus 81% for Qwen 3.8 Flash Next. On GPQA Diamond graduate-level STEM reasoning, GLM-5.3-Flash scores 63.5% versus 91.7% for Qwen 3.8 Flash Next (and 84% vs 95.7% 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 Qwen 3.8 Flash Next?
Choose Qwen 3.8 Flash Next (Alibaba Cloud) for next-generation open-weight multimodal MoE activating only 6B parameters per token for ultra-low-cost enterprise office automation, tool calling, and multilingual repository engineering — offering a 268k context window, 280ms TTFT, and $0.16/1M input pricing ($0.016/1M cached reads).
