Which model is better: DeepSeek R1 or Qwen 3.8 27B?
Qwen 3.8 27B is 63.6% cheaper for input tokens ($0.20 vs. $0.55 per 1M tokens) and $0.60 vs. $2.19 for output tokens (2.8x cost advantage). In terms of operational performance, Qwen 3.8 27B delivers lower response latency at 120ms TTFT (1330ms faster than DeepSeek R1) with 140 vs. 58 tok/s throughput. Qwen 3.8 27B leads coding evaluations at 58.4% SWE-bench Verified vs. 49.2% for DeepSeek R1 and 68.0% vs. 71.5% 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
DeepSeek R1
Qwen 3.8 27B
Net Delta / Advantage
Input Cost / 1M Tokens
$0.55
$0.20
Qwen 3.8 27B is 2.8x cheaper
Output Cost / 1M Tokens
$2.19
$0.60
Qwen 3.8 27B (72.6% lower)
Prompt Cache Read / 1M
$0.140
$0.050
Qwen 3.8 27B is 2.8x cheaper
Prompt Cache Write / 1M
$0.55
$0.20
Qwen 3.8 27B is 2.8x cheaper
Context Window (Max Output)
131k (33k out)
131k (16k out)
Equal Context Window
Avg TTFT Response Latency
1450ms
120ms
Qwen 3.8 27B (1330ms faster)
Generation Speed (Throughput)
58 tok/s
140 tok/s
Qwen 3.8 27B (+82 tok/s faster)
SWE-bench Verified (Coding)
49.2%
58.4%
Qwen 3.8 27B (+9.2% lead)
GPQA Diamond (PhD STEM)
71.5%
68%
DeepSeek R1 (+3.5% lead)
MMLU-Pro (Multi-Discipline)
84%
81.2%
DeepSeek R1 (+2.8% lead)
MATH-500 (Quantitative Logic)
97.3%
86.4%
DeepSeek R1 (+10.9% lead)
Inference Value Score (IVR)
64.8 / 100
74.9 / 100
Qwen 3.8 27B (+10.1 pts)
Interactive Monthly Token Economics & ROI Forecaster
Model production workloads across prompt, completion, and cached context tokens, or switch models on the fly.
Choose 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 $0.55/1M input ($2.19/1M output) for DeepSeek R1.
Which model has lower TTFT latency and higher tok/s throughput: DeepSeek R1 or Qwen 3.8 27B?
Qwen 3.8 27B delivers faster first-token latency at 120ms TTFT (140 tok/s throughput) compared to 1450ms TTFT (58 tok/s) for DeepSeek R1.
How do DeepSeek R1 and Qwen 3.8 27B compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, DeepSeek R1 scores 49.2% versus 58.4% for Qwen 3.8 27B. On GPQA Diamond graduate-level STEM reasoning, DeepSeek R1 scores 71.5% versus 68% for Qwen 3.8 27B (and 97.3% vs 86.4% on MATH-500).
When should you choose DeepSeek R1?
Choose DeepSeek R1 (DeepSeek) for open-weight chain-of-thought reasoning, synthetic data distillation, and complex mathematical verification — offering a 131k context window, 1450ms TTFT, and $0.55/1M input pricing ($0.140/1M 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).
