DeepSeek V4 Flash Vision (Exp) vs Qwen 3.8 Flash Next
Live Unit Economics, Prompt Cache Pricing, TTFT Latency & Multi-Benchmark Analysis
Which model is better: DeepSeek V4 Flash Vision (Exp) or Qwen 3.8 Flash Next?
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 V4 Flash Vision (Exp) | Qwen 3.8 Flash Next | Net Delta / Advantage |
|---|---|---|---|
| Input Cost / 1M Tokens | $0.22 | $0.16 | Qwen 3.8 Flash Next is 1.4x cheaper |
| Output Cost / 1M Tokens | $0.65 | $0.47 | Qwen 3.8 Flash Next (27.7% lower) |
| Prompt Cache Read / 1M | $0.007 | $0.016 | DeepSeek V4 Flash Vision (Exp) is 2.3x cheaper |
| Prompt Cache Write / 1M | $0.22 | $0.20 | Qwen 3.8 Flash Next is 1.1x cheaper |
| Context Window (Max Output) | 1,049k (128k out) | 268k (66k out) | DeepSeek V4 Flash Vision (Exp) (3.9x larger context) |
| Avg TTFT Response Latency | 310ms | 280ms | Qwen 3.8 Flash Next (30ms faster) |
| Generation Speed (Throughput) | 165 tok/s | 185 tok/s | Qwen 3.8 Flash Next (+20 tok/s faster) |
| SWE-bench Verified (Coding) | 77.4% | 81% | Qwen 3.8 Flash Next (+3.6% lead) |
| GPQA Diamond (PhD STEM) | 91.3% | 91.7% | Qwen 3.8 Flash Next (+0.4% lead) |
| MMLU-Pro (Multi-Discipline) | 87.3% | 88.4% | Qwen 3.8 Flash Next (+1.1% lead) |
| MATH-500 (Quantitative Logic) | 96.4% | 95.7% | DeepSeek V4 Flash Vision (Exp) (+0.7% lead) |
| Inference Value Score (IVR) | 81.6 / 100 | 85.1 / 100 | Qwen 3.8 Flash Next (+3.5 pts) |
Interactive Monthly Token Economics & ROI Forecaster
Model production workloads across prompt, completion, and cached context tokens, or switch models on the fly.
Estimated Monthly Spend
$0.22/1M in · $0.65/1M out
Prompt Cache: $0.007/1M read · $0.22/1M write
$0.16/1M in · $0.47/1M out
Prompt Cache: $0.016/1M read · $0.20/1M write
When to Choose DeepSeek V4 Flash Vision (Exp)
Choose DeepSeek V4 Flash Vision (Exp) (DeepSeek) for ultra-low-cost open-weight multimodal MoE built for high-throughput visual document OCR, chart interpretation, and real-time autonomous coding agents — offering a 1,048k context window, 310ms TTFT, and $0.22/1M input pricing ($0.007/1M cached reads).
When to 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).
Related Questions & Decision Factors
Which is cheaper: DeepSeek V4 Flash Vision (Exp) 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 $0.22/1M input ($0.65/1M output) for DeepSeek V4 Flash Vision (Exp).
Which model has lower TTFT latency and higher tok/s throughput: DeepSeek V4 Flash Vision (Exp) 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 310ms TTFT (165 tok/s) for DeepSeek V4 Flash Vision (Exp).
How do DeepSeek V4 Flash Vision (Exp) and Qwen 3.8 Flash Next compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, DeepSeek V4 Flash Vision (Exp) scores 77.4% versus 81% for Qwen 3.8 Flash Next. On GPQA Diamond graduate-level STEM reasoning, DeepSeek V4 Flash Vision (Exp) scores 91.3% versus 91.7% for Qwen 3.8 Flash Next (and 96.4% vs 95.7% on MATH-500).
When should you choose DeepSeek V4 Flash Vision (Exp)?
Choose DeepSeek V4 Flash Vision (Exp) (DeepSeek) for ultra-low-cost open-weight multimodal MoE built for high-throughput visual document OCR, chart interpretation, and real-time autonomous coding agents — offering a 1,048k context window, 310ms TTFT, and $0.22/1M input pricing ($0.007/1M 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).