Qwen 3.8 27B is 42.9% cheaper for input tokens ($0.20 vs. $0.35 per 1M tokens) and $0.60 vs. $1.00 for output tokens (1.7x cost advantage). In terms of operational performance, Qwen 3.8 27B delivers lower response latency at 120ms TTFT (40ms faster than GLM-5.3) with 140 vs. 120 tok/s throughput. Qwen 3.8 27B leads coding evaluations at 58.4% SWE-bench Verified vs. 56.0% for GLM-5.3 and 68.0% 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 27B
Net Delta / Advantage
Input Cost / 1M Tokens
$0.35
$0.20
Qwen 3.8 27B is 1.7x cheaper
Output Cost / 1M Tokens
$1.00
$0.60
Qwen 3.8 27B (40.0% lower)
Prompt Cache Read / 1M
$0.090
$0.050
Qwen 3.8 27B is 1.8x cheaper
Prompt Cache Write / 1M
$0.35
$0.20
Qwen 3.8 27B is 1.7x cheaper
Context Window (Max Output)
131k (16k out)
131k (16k out)
Equal Context Window
Avg TTFT Response Latency
160ms
120ms
Qwen 3.8 27B (40ms faster)
Generation Speed (Throughput)
120 tok/s
140 tok/s
Qwen 3.8 27B (+20 tok/s faster)
SWE-bench Verified (Coding)
56%
58.4%
Qwen 3.8 27B (+2.4% lead)
GPQA Diamond (PhD STEM)
67%
68%
Qwen 3.8 27B (+1.0% lead)
MMLU-Pro (Multi-Discipline)
80.5%
81.2%
Qwen 3.8 27B (+0.7% lead)
MATH-500 (Quantitative Logic)
88%
86.4%
GLM-5.3 (+1.6% lead)
Inference Value Score (IVR)
69.9 / 100
74.9 / 100
Qwen 3.8 27B (+5 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).
Choose Qwen 3.8 27B (Alibaba Cloud) for high-performance 27B open weights optimized for local deployment, code generation, and low-latency production pipelines — offering a 131k context window, 120ms TTFT, and $0.20/1M input pricing ($0.050/1M cached reads).
Qwen 3.8 27B is cheaper at $0.20/1M input tokens ($0.60/1M 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 27B?
Qwen 3.8 27B delivers faster first-token latency at 120ms TTFT (140 tok/s throughput) compared to 160ms TTFT (120 tok/s) for GLM-5.3.
How do GLM-5.3 and Qwen 3.8 27B compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, GLM-5.3 scores 56% versus 58.4% for Qwen 3.8 27B. On GPQA Diamond graduate-level STEM reasoning, GLM-5.3 scores 67% versus 68% for Qwen 3.8 27B (and 88% vs 86.4% 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 27B?
Choose Qwen 3.8 27B (Alibaba Cloud) for high-performance 27B open weights optimized for local deployment, code generation, and low-latency production pipelines — offering a 131k context window, 120ms TTFT, and $0.20/1M input pricing ($0.050/1M cached reads).
