Which model is better: OpenAI o3-mini or Qwen 3.8 27B?
Qwen 3.8 27B is 81.8% cheaper for input tokens ($0.20 vs. $1.10 per 1M tokens) and $0.60 vs. $4.40 for output tokens (5.5x cost advantage). In terms of operational performance, Qwen 3.8 27B delivers lower response latency at 120ms TTFT (830ms faster than OpenAI o3-mini) with 140 vs. 98 tok/s throughput. Qwen 3.8 27B leads coding evaluations at 58.4% SWE-bench Verified vs. 49.3% for OpenAI o3-mini and 68.0% vs. 79.7% 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
OpenAI o3-mini
Qwen 3.8 27B
Net Delta / Advantage
Input Cost / 1M Tokens
$1.10
$0.20
Qwen 3.8 27B is 5.5x cheaper
Output Cost / 1M Tokens
$4.40
$0.60
Qwen 3.8 27B (86.4% lower)
Prompt Cache Read / 1M
$0.550
$0.050
Qwen 3.8 27B is 11.0x cheaper
Prompt Cache Write / 1M
$1.10
$0.20
Qwen 3.8 27B is 5.5x cheaper
Context Window (Max Output)
205k (100k out)
131k (16k out)
OpenAI o3-mini (1.6x larger context)
Avg TTFT Response Latency
950ms
120ms
Qwen 3.8 27B (830ms faster)
Generation Speed (Throughput)
98 tok/s
140 tok/s
Qwen 3.8 27B (+42 tok/s faster)
SWE-bench Verified (Coding)
49.3%
58.4%
Qwen 3.8 27B (+9.1% lead)
GPQA Diamond (PhD STEM)
79.7%
68%
OpenAI o3-mini (+11.7% lead)
MMLU-Pro (Multi-Discipline)
83.2%
81.2%
OpenAI o3-mini (+2.0% lead)
MATH-500 (Quantitative Logic)
97.9%
86.4%
OpenAI o3-mini (+11.5% lead)
Inference Value Score (IVR)
59.7 / 100
74.9 / 100
Qwen 3.8 27B (+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 OpenAI o3-mini (OpenAI) for competitive math reasoning, automated unit test generation, and structured logic pipelines — offering a 204k context window, 950ms TTFT, and $1.10/1M input pricing ($0.550/1M 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 $1.10/1M input ($4.40/1M output) for OpenAI o3-mini.
Which model has lower TTFT latency and higher tok/s throughput: OpenAI o3-mini or Qwen 3.8 27B?
Qwen 3.8 27B delivers faster first-token latency at 120ms TTFT (140 tok/s throughput) compared to 950ms TTFT (98 tok/s) for OpenAI o3-mini.
How do OpenAI o3-mini and Qwen 3.8 27B compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, OpenAI o3-mini scores 49.3% versus 58.4% for Qwen 3.8 27B. On GPQA Diamond graduate-level STEM reasoning, OpenAI o3-mini scores 79.7% versus 68% for Qwen 3.8 27B (and 97.9% vs 86.4% on MATH-500).
When should you choose OpenAI o3-mini?
Choose OpenAI o3-mini (OpenAI) for competitive math reasoning, automated unit test generation, and structured logic pipelines — offering a 204k context window, 950ms TTFT, and $1.10/1M input pricing ($0.550/1M 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).
