Which model is better: GPT-5 or Qwen 3.8 Flash Next?
Qwen 3.8 Flash Next outperforms GPT-5 in capability while delivering a 15.6x cost advantage. Input costs 93.6% less ($0.16 vs. $2.50 per 1M) with $0.47 vs. $10.00 output pricing. Qwen also achieves lower latency (280ms TTFT, 60ms faster), higher throughput (185 vs. 105 tok/s), and leads SWE-bench Verified (81.0% vs. 74.9%) and GPQA Diamond (91.7% vs. 85.4%).
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-5
Qwen 3.8 Flash Next
Net Delta / Advantage
Input Cost / 1M Tokens
$2.50
$0.16
Qwen 3.8 Flash Next is 15.6x cheaper
Output Cost / 1M Tokens
$10.00
$0.47
Qwen 3.8 Flash Next (95.3% lower)
Prompt Cache Read / 1M
$0.625
$0.016
Qwen 3.8 Flash Next is 39.1x cheaper
Prompt Cache Write / 1M
$2.50
$0.20
Qwen 3.8 Flash Next is 12.5x cheaper
Context Window (Max Output)
410k (128k out)
268k (66k out)
GPT-5 (1.5x larger context)
Avg TTFT Response Latency
340ms
280ms
Qwen 3.8 Flash Next (60ms faster)
Generation Speed (Throughput)
105 tok/s
185 tok/s
Qwen 3.8 Flash Next (+80 tok/s faster)
SWE-bench Verified (Coding)
74.9%
81%
Qwen 3.8 Flash Next (+6.1% lead)
GPQA Diamond (PhD STEM)
85.4%
91.7%
Qwen 3.8 Flash Next (+6.3% lead)
MMLU-Pro (Multi-Discipline)
90.8%
88.4%
GPT-5 (+2.4% lead)
MATH-500 (Quantitative Logic)
98%
95.7%
GPT-5 (+2.3% lead)
Inference Value Score (IVR)
59.2 / 100
85.1 / 100
Qwen 3.8 Flash Next (+25.9 pts)
Interactive Monthly Token Economics & ROI Forecaster
Model production workloads across prompt, completion, and cached context tokens, or switch models on the fly.
Qwen 3.8 Flash Next is cheaper at $0.16/1M input tokens ($0.47/1M output) compared to $2.50/1M input ($10.00/1M output) for GPT-5.
Which model has lower TTFT latency and higher tok/s throughput: GPT-5 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 340ms TTFT (105 tok/s) for GPT-5.
How do GPT-5 and Qwen 3.8 Flash Next compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, GPT-5 scores 74.9% versus 81% for Qwen 3.8 Flash Next. On GPQA Diamond graduate-level STEM reasoning, GPT-5 scores 85.4% versus 91.7% for Qwen 3.8 Flash Next (and 98% vs 95.7% on MATH-500).
When should you choose GPT-5?
Choose GPT-5 (OpenAI) for complex enterprise automation, full-stack software engineering, multimodal agent orchestration, and scientific synthesis — offering a 409k context window, 340ms TTFT, and $2.50/1M input pricing ($0.625/1M 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).
