Which model is better: OpenAI o4-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 (560ms faster than OpenAI o4-mini) with 140 vs. 125 tok/s throughput. OpenAI o4-mini leads coding evaluations at 68.1% SWE-bench Verified vs. 58.4% for Qwen 3.8 27B and 81.4% 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
OpenAI o4-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
$0.275
$0.050
Qwen 3.8 27B is 5.5x 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 o4-mini (1.6x larger context)
Avg TTFT Response Latency
680ms
120ms
Qwen 3.8 27B (560ms faster)
Generation Speed (Throughput)
125 tok/s
140 tok/s
Qwen 3.8 27B (+15 tok/s faster)
SWE-bench Verified (Coding)
68.1%
58.4%
OpenAI o4-mini (+9.7% lead)
GPQA Diamond (PhD STEM)
81.4%
68%
OpenAI o4-mini (+13.4% lead)
MMLU-Pro (Multi-Discipline)
85.6%
81.2%
OpenAI o4-mini (+4.4% lead)
MATH-500 (Quantitative Logic)
97.8%
86.4%
OpenAI o4-mini (+11.4% lead)
Inference Value Score (IVR)
63.7 / 100
74.9 / 100
Qwen 3.8 27B (+11.2 pts)
Interactive Monthly Token Economics & ROI Forecaster
Model production workloads across prompt, completion, and cached context tokens, or switch models on the fly.
Choose 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 o4-mini.
Which model has lower TTFT latency and higher tok/s throughput: OpenAI o4-mini or Qwen 3.8 27B?
Qwen 3.8 27B delivers faster first-token latency at 120ms TTFT (140 tok/s throughput) compared to 680ms TTFT (125 tok/s) for OpenAI o4-mini.
How do OpenAI o4-mini and Qwen 3.8 27B compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, OpenAI o4-mini scores 68.1% versus 58.4% for Qwen 3.8 27B. On GPQA Diamond graduate-level STEM reasoning, OpenAI o4-mini scores 81.4% versus 68% for Qwen 3.8 27B (and 97.8% vs 86.4% on MATH-500).
When should you choose OpenAI o4-mini?
Choose OpenAI o4-mini (OpenAI) for automated PR review, high-volume agentic coding loops, SQL/data analysis, and visual document reasoning — offering a 204k context window, 680ms TTFT, and $1.10/1M input pricing ($0.275/1M 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).
