Qwen 3.8 27B is 92.0% cheaper for input tokens ($0.20 vs. $2.50 per 1M tokens) and $0.60 vs. $10.00 for output tokens (12.5x cost advantage). In terms of operational performance, Qwen 3.8 27B delivers lower response latency at 120ms TTFT (220ms faster than GPT-5) with 140 vs. 105 tok/s throughput. GPT-5 leads coding evaluations at 74.9% SWE-bench Verified vs. 58.4% for Qwen 3.8 27B and 85.4% vs. 68.0% 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
GPT-5
Qwen 3.8 27B
Net Delta / Advantage
Input Cost / 1M Tokens
$2.50
$0.20
Qwen 3.8 27B is 12.5x cheaper
Output Cost / 1M Tokens
$10.00
$0.60
Qwen 3.8 27B (94.0% lower)
Prompt Cache Read / 1M
$0.625
$0.050
Qwen 3.8 27B is 12.5x cheaper
Prompt Cache Write / 1M
$2.50
$0.20
Qwen 3.8 27B is 12.5x cheaper
Context Window (Max Output)
410k (128k out)
131k (16k out)
GPT-5 (3.1x larger context)
Avg TTFT Response Latency
340ms
120ms
Qwen 3.8 27B (220ms faster)
Generation Speed (Throughput)
105 tok/s
140 tok/s
Qwen 3.8 27B (+35 tok/s faster)
SWE-bench Verified (Coding)
74.9%
58.4%
GPT-5 (+16.5% lead)
GPQA Diamond (PhD STEM)
85.4%
68%
GPT-5 (+17.4% lead)
MMLU-Pro (Multi-Discipline)
90.8%
81.2%
GPT-5 (+9.6% lead)
MATH-500 (Quantitative Logic)
98%
86.4%
GPT-5 (+11.6% lead)
Inference Value Score (IVR)
59.2 / 100
74.9 / 100
Qwen 3.8 27B (+15.7 pts)
Interactive Monthly Token Economics & ROI Forecaster
Model production workloads across prompt, completion, and cached context tokens, or switch models on the fly.
Choose 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 $2.50/1M input ($10.00/1M output) for GPT-5.
Which model has lower TTFT latency and higher tok/s throughput: GPT-5 or Qwen 3.8 27B?
Qwen 3.8 27B delivers faster first-token latency at 120ms TTFT (140 tok/s throughput) compared to 340ms TTFT (105 tok/s) for GPT-5.
How do GPT-5 and Qwen 3.8 27B compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, GPT-5 scores 74.9% versus 58.4% for Qwen 3.8 27B. On GPQA Diamond graduate-level STEM reasoning, GPT-5 scores 85.4% versus 68% for Qwen 3.8 27B (and 98% vs 86.4% on MATH-500).
When should you choose GPT-5?
Choose GPT-5 (OpenAI) for complex enterprise automation, full-stack software engineering, multimodal agent orchestration, and scientific synthesis — offering a 409k context window, 340ms TTFT, and $2.50/1M input pricing ($0.625/1M 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).
