Which model is better: Gemini 3.1 Pro or Qwen 3.8 27B?
Qwen 3.8 27B is 84.0% cheaper for input tokens ($0.20 vs. $1.25 per 1M tokens) and $0.60 vs. $10.00 for output tokens (6.2x cost advantage). In terms of operational performance, Qwen 3.8 27B delivers lower response latency at 120ms TTFT (300ms faster than Gemini 3.1 Pro) with 140 vs. 98 tok/s throughput. Gemini 3.1 Pro leads coding evaluations at 71.8% SWE-bench Verified vs. 58.4% for Qwen 3.8 27B and 82.4% 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
Gemini 3.1 Pro
Qwen 3.8 27B
Net Delta / Advantage
Input Cost / 1M Tokens
$1.25
$0.20
Qwen 3.8 27B is 6.3x cheaper
Output Cost / 1M Tokens
$10.00
$0.60
Qwen 3.8 27B (94.0% lower)
Prompt Cache Read / 1M
$0.310
$0.050
Qwen 3.8 27B is 6.2x cheaper
Prompt Cache Write / 1M
$1.25
$0.20
Qwen 3.8 27B is 6.3x cheaper
Context Window (Max Output)
1,049k (66k out)
131k (16k out)
Gemini 3.1 Pro (8.0x larger context)
Avg TTFT Response Latency
420ms
120ms
Qwen 3.8 27B (300ms faster)
Generation Speed (Throughput)
98 tok/s
140 tok/s
Qwen 3.8 27B (+42 tok/s faster)
SWE-bench Verified (Coding)
71.8%
58.4%
Gemini 3.1 Pro (+13.4% lead)
GPQA Diamond (PhD STEM)
82.4%
68%
Gemini 3.1 Pro (+14.4% lead)
MMLU-Pro (Multi-Discipline)
89.5%
81.2%
Gemini 3.1 Pro (+8.3% lead)
MATH-500 (Quantitative Logic)
96.8%
86.4%
Gemini 3.1 Pro (+10.4% lead)
Inference Value Score (IVR)
60 / 100
74.9 / 100
Qwen 3.8 27B (+14.9 pts)
Interactive Monthly Token Economics & ROI Forecaster
Model production workloads across prompt, completion, and cached context tokens, or switch models on the fly.
Choose Gemini 3.1 Pro (Google) for complex enterprise architecture planning, multi-file code refactoring, legal/financial corpus auditing, and multimodal research — offering a 1,048k context window, 420ms TTFT, and $1.25/1M input pricing ($0.310/1M 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 $1.25/1M input ($10.00/1M output) for Gemini 3.1 Pro.
Which model has lower TTFT latency and higher tok/s throughput: Gemini 3.1 Pro or Qwen 3.8 27B?
Qwen 3.8 27B delivers faster first-token latency at 120ms TTFT (140 tok/s throughput) compared to 420ms TTFT (98 tok/s) for Gemini 3.1 Pro.
How do Gemini 3.1 Pro and Qwen 3.8 27B compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, Gemini 3.1 Pro scores 71.8% versus 58.4% for Qwen 3.8 27B. On GPQA Diamond graduate-level STEM reasoning, Gemini 3.1 Pro scores 82.4% versus 68% for Qwen 3.8 27B (and 96.8% vs 86.4% on MATH-500).
When should you choose Gemini 3.1 Pro?
Choose Gemini 3.1 Pro (Google) for complex enterprise architecture planning, multi-file code refactoring, legal/financial corpus auditing, and multimodal research — offering a 1,048k context window, 420ms TTFT, and $1.25/1M input pricing ($0.310/1M 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).
