💻 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 Flash Next?
Qwen 3.8 Flash Next is 92.0% cheaper for input tokens ($0.16 vs. $2.00 per 1M tokens) and $0.47 vs. $10.00 for output tokens (12.5x cost advantage). In terms of operational performance, Qwen 3.8 Flash Next delivers lower response latency at 280ms TTFT (240ms faster than GPT-6.1 Sol) with 185 vs. 135 tok/s throughput. GPT-6.1 Sol leads coding evaluations at 86.4% SWE-bench Verified vs. 81.0% for Qwen 3.8 Flash Next and 92.4% vs. 91.7% 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
GPT-6.1 Sol
Qwen 3.8 Flash Next
Net Delta / Advantage
Input Cost / 1M Tokens
$2.00
$0.16
Qwen 3.8 Flash Next is 12.5x cheaper
Output Cost / 1M Tokens
$10.00
$0.47
Qwen 3.8 Flash Next (95.3% lower)
Prompt Cache Read / 1M
$0.100
$0.016
Qwen 3.8 Flash Next is 6.3x cheaper
Prompt Cache Write / 1M
$2.00
$0.20
Qwen 3.8 Flash Next is 10.0x cheaper
Context Window (Max Output)
1,075k (128k out)
268k (66k out)
GPT-6.1 Sol (4.0x larger context)
Avg TTFT Response Latency
520ms
280ms
Qwen 3.8 Flash Next (240ms faster)
Generation Speed (Throughput)
135 tok/s
185 tok/s
Qwen 3.8 Flash Next (+50 tok/s faster)
SWE-bench Verified (Coding)
86.4%
81%
GPT-6.1 Sol (+5.4% lead)
GPQA Diamond (PhD STEM)
92.4%
91.7%
GPT-6.1 Sol (+0.7% lead)
MMLU-Pro (Multi-Discipline)
91%
88.4%
GPT-6.1 Sol (+2.6% lead)
MATH-500 (Quantitative Logic)
98.8%
95.7%
GPT-6.1 Sol (+3.1% lead)
Inference Value Score (IVR)
62.9 / 100
85.1 / 100
Qwen 3.8 Flash Next (+22.2 pts)
Interactive Monthly Token Economics & ROI Forecaster
Model production workloads across prompt, completion, and cached context tokens, or switch models on the fly.
Choose 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).
Which is cheaper: GPT-6.1 Sol or Qwen 3.8 Flash Next?
Qwen 3.8 Flash Next is cheaper at $0.16/1M input tokens ($0.47/1M 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 Flash Next?
Qwen 3.8 Flash Next delivers faster first-token latency at 280ms TTFT (185 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 Flash Next compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, GPT-6.1 Sol scores 86.4% versus 81% for Qwen 3.8 Flash Next. On GPQA Diamond graduate-level STEM reasoning, GPT-6.1 Sol scores 92.4% versus 91.7% for Qwen 3.8 Flash Next (and 98.8% vs 95.7% 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 Flash Next?
Choose Qwen 3.8 Flash Next (Alibaba Cloud) for next-generation open-weight multimodal MoE activating only 6B parameters per token for ultra-low-cost enterprise office automation, tool calling, and multilingual repository engineering — offering a 268k context window, 280ms TTFT, and $0.16/1M input pricing ($0.016/1M cached reads).
