Which model is better: DeepSeek R1 or Qwen 3.8 Flash Next?
Qwen 3.8 Flash Next is 70.9% cheaper for input tokens ($0.16 vs. $0.55 per 1M tokens) and $0.47 vs. $2.19 for output tokens (3.4x cost advantage). In terms of operational performance, Qwen 3.8 Flash Next delivers lower response latency at 280ms TTFT (1170ms faster than DeepSeek R1) with 185 vs. 58 tok/s throughput. Qwen 3.8 Flash Next leads coding evaluations at 81.0% SWE-bench Verified vs. 49.2% for DeepSeek R1 and 91.7% vs. 71.5% 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
DeepSeek R1
Qwen 3.8 Flash Next
Net Delta / Advantage
Input Cost / 1M Tokens
$0.55
$0.16
Qwen 3.8 Flash Next is 3.4x cheaper
Output Cost / 1M Tokens
$2.19
$0.47
Qwen 3.8 Flash Next (78.5% lower)
Prompt Cache Read / 1M
$0.140
$0.016
Qwen 3.8 Flash Next is 8.8x cheaper
Prompt Cache Write / 1M
$0.55
$0.20
Qwen 3.8 Flash Next is 2.8x cheaper
Context Window (Max Output)
131k (33k out)
268k (66k out)
Qwen 3.8 Flash Next (2.0x larger context)
Avg TTFT Response Latency
1450ms
280ms
Qwen 3.8 Flash Next (1170ms faster)
Generation Speed (Throughput)
58 tok/s
185 tok/s
Qwen 3.8 Flash Next (+127 tok/s faster)
SWE-bench Verified (Coding)
49.2%
81%
Qwen 3.8 Flash Next (+31.8% lead)
GPQA Diamond (PhD STEM)
71.5%
91.7%
Qwen 3.8 Flash Next (+20.2% lead)
MMLU-Pro (Multi-Discipline)
84%
88.4%
Qwen 3.8 Flash Next (+4.4% lead)
MATH-500 (Quantitative Logic)
97.3%
95.7%
DeepSeek R1 (+1.6% lead)
Inference Value Score (IVR)
64.8 / 100
85.1 / 100
Qwen 3.8 Flash Next (+20.3 pts)
Interactive Monthly Token Economics & ROI Forecaster
Model production workloads across prompt, completion, and cached context tokens, or switch models on the fly.
Which is cheaper: DeepSeek R1 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 $0.55/1M input ($2.19/1M output) for DeepSeek R1.
Which model has lower TTFT latency and higher tok/s throughput: DeepSeek R1 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 1450ms TTFT (58 tok/s) for DeepSeek R1.
How do DeepSeek R1 and Qwen 3.8 Flash Next compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, DeepSeek R1 scores 49.2% versus 81% for Qwen 3.8 Flash Next. On GPQA Diamond graduate-level STEM reasoning, DeepSeek R1 scores 71.5% versus 91.7% for Qwen 3.8 Flash Next (and 97.3% vs 95.7% on MATH-500).
When should you choose DeepSeek R1?
Choose DeepSeek R1 (DeepSeek) for open-weight chain-of-thought reasoning, synthetic data distillation, and complex mathematical verification — offering a 131k context window, 1450ms TTFT, and $0.55/1M input pricing ($0.140/1M 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).
