💻 Coding (SWE) WinnerGPT-6.1 Sol 86.4% SWE-bench · 96.8% HumanEval
🧠 STEM Reasoning WinnerGPT-6.1 Sol 92.4% GPQA · 98.8% MATH-500
Which model is better: GPT-6.1 Sol or Qwen 3.8 27B?
Qwen 3.8 27B is 90.0% cheaper for input tokens ($0.20 vs. $2.00 per 1M tokens) and $0.60 vs. $10.00 for output tokens (10.0x cost advantage). In terms of operational performance, Qwen 3.8 27B delivers lower response latency at 120ms TTFT (400ms faster than GPT-6.1 Sol) with 140 vs. 135 tok/s throughput. GPT-6.1 Sol leads coding evaluations at 86.4% SWE-bench Verified vs. 58.4% for Qwen 3.8 27B and 92.4% 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-6.1 Sol
Qwen 3.8 27B
Net Delta / Advantage
Input Cost / 1M Tokens
$2.00
$0.20
Qwen 3.8 27B is 10.0x cheaper
Output Cost / 1M Tokens
$10.00
$0.60
Qwen 3.8 27B (94.0% lower)
Prompt Cache Read / 1M
$0.100
$0.050
Qwen 3.8 27B is 2.0x cheaper
Prompt Cache Write / 1M
$2.00
$0.20
Qwen 3.8 27B is 10.0x cheaper
Context Window (Max Output)
1,075k (128k out)
131k (16k out)
GPT-6.1 Sol (8.2x larger context)
Avg TTFT Response Latency
520ms
120ms
Qwen 3.8 27B (400ms faster)
Generation Speed (Throughput)
135 tok/s
140 tok/s
Qwen 3.8 27B (+5 tok/s faster)
SWE-bench Verified (Coding)
86.4%
58.4%
GPT-6.1 Sol (+28.0% lead)
GPQA Diamond (PhD STEM)
92.4%
68%
GPT-6.1 Sol (+24.4% lead)
MMLU-Pro (Multi-Discipline)
91%
81.2%
GPT-6.1 Sol (+9.8% lead)
MATH-500 (Quantitative Logic)
98.8%
86.4%
GPT-6.1 Sol (+12.4% lead)
Inference Value Score (IVR)
62.9 / 100
74.9 / 100
Qwen 3.8 27B (+12 pts)
Interactive Monthly Token Economics & ROI Forecaster
Model production workloads across prompt, completion, and cached context tokens, or switch models on the fly.
Choose GPT-6.1 Sol (OpenAI) for delivers near-GPT-6 Astra frontier accuracy at one-fifth the token cost with a 95% cache read discount for high-efficiency agentic coding, computer use, and enterprise workflow automation — offering a 1,075k context window, 520ms TTFT, and $2.00/1M input pricing ($0.100/1M 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 $2.00/1M input ($10.00/1M output) for GPT-6.1 Sol.
Which model has lower TTFT latency and higher tok/s throughput: GPT-6.1 Sol or Qwen 3.8 27B?
Qwen 3.8 27B delivers faster first-token latency at 120ms TTFT (140 tok/s throughput) compared to 520ms TTFT (135 tok/s) for GPT-6.1 Sol.
How do GPT-6.1 Sol and Qwen 3.8 27B compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, GPT-6.1 Sol scores 86.4% versus 58.4% for Qwen 3.8 27B. On GPQA Diamond graduate-level STEM reasoning, GPT-6.1 Sol scores 92.4% versus 68% for Qwen 3.8 27B (and 98.8% vs 86.4% on MATH-500).
When should you choose GPT-6.1 Sol?
Choose GPT-6.1 Sol (OpenAI) for delivers near-GPT-6 Astra frontier accuracy at one-fifth the token cost with a 95% cache read discount for high-efficiency agentic coding, computer use, and enterprise workflow automation — offering a 1,075k context window, 520ms TTFT, and $2.00/1M input pricing ($0.100/1M 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).
