Which model is better: GLM-5.3 or Qwen 3.8 Flash Next?
Qwen 3.8 Flash Next is 54.3% cheaper for input tokens ($0.16 vs. $0.35 per 1M tokens) and $0.47 vs. $1.00 for output tokens (2.2x cost advantage). In terms of operational performance, GLM-5.3 delivers lower response latency at 160ms TTFT (120ms faster than Qwen 3.8 Flash Next) with 120 vs. 185 tok/s throughput. Qwen 3.8 Flash Next leads coding evaluations at 81.0% SWE-bench Verified vs. 56.0% for GLM-5.3 and 91.7% 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
GLM-5.3
Qwen 3.8 Flash Next
Net Delta / Advantage
Input Cost / 1M Tokens
$0.35
$0.16
Qwen 3.8 Flash Next is 2.2x cheaper
Output Cost / 1M Tokens
$1.00
$0.47
Qwen 3.8 Flash Next (53.0% lower)
Prompt Cache Read / 1M
$0.090
$0.016
Qwen 3.8 Flash Next is 5.6x cheaper
Prompt Cache Write / 1M
$0.35
$0.20
Qwen 3.8 Flash Next is 1.7x cheaper
Context Window (Max Output)
131k (16k out)
268k (66k out)
Qwen 3.8 Flash Next (2.0x larger context)
Avg TTFT Response Latency
160ms
280ms
GLM-5.3 (120ms faster)
Generation Speed (Throughput)
120 tok/s
185 tok/s
Qwen 3.8 Flash Next (+65 tok/s faster)
SWE-bench Verified (Coding)
56%
81%
Qwen 3.8 Flash Next (+25.0% lead)
GPQA Diamond (PhD STEM)
67%
91.7%
Qwen 3.8 Flash Next (+24.7% lead)
MMLU-Pro (Multi-Discipline)
80.5%
88.4%
Qwen 3.8 Flash Next (+7.9% lead)
MATH-500 (Quantitative Logic)
88%
95.7%
Qwen 3.8 Flash Next (+7.7% lead)
Inference Value Score (IVR)
69.9 / 100
85.1 / 100
Qwen 3.8 Flash Next (+15.2 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 (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).
Qwen 3.8 Flash Next is cheaper at $0.16/1M input tokens ($0.47/1M output) compared to $0.35/1M input ($1.00/1M output) for GLM-5.3.
Which model has lower TTFT latency and higher tok/s throughput: GLM-5.3 or Qwen 3.8 Flash Next?
GLM-5.3 delivers faster first-token latency at 160ms TTFT (120 tok/s throughput) compared to 280ms TTFT (185 tok/s) for Qwen 3.8 Flash Next.
How do GLM-5.3 and Qwen 3.8 Flash Next compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, GLM-5.3 scores 56% versus 81% for Qwen 3.8 Flash Next. On GPQA Diamond graduate-level STEM reasoning, GLM-5.3 scores 67% versus 91.7% for Qwen 3.8 Flash Next (and 88% vs 95.7% on MATH-500).
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).
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).
