Qwen3.5 9B API Benchmarks: Latency, Throughput & Cost
Blog post from Deepinfra
Qwen3.5 9B, the latest model in Alibaba's Qwen3.5 Small Model Series, is a multimodal model combining Gated Delta Networks and a sparse Mixture-of-Experts system to achieve enhanced throughput and reduced latency. This architecture allows for simultaneous processing of visual and textual tokens, resulting in improved spatial reasoning and OCR accuracy. The model's performance is further optimized through Scaled Reinforcement Learning, enhancing its reasoning, fact retrieval, and mathematical capabilities. Evaluations show that DeepInfra outperforms Together.ai in terms of speed and cost-effectiveness for most applications, delivering a 2.2x faster output speed and a 27% lower blended price, making it the preferred provider for production-scale deployments. However, Together.ai offers a slight advantage in initial latency, which may appeal to applications requiring sub-second responsiveness. Both providers support the full context window and feature function calling, ensuring no technical limitations for users.
| Trend | Post Mentions | Total Month Mentions | Posts | Companies | MoM |
|---|---|---|---|---|---|
| LLM | 4 | 6,889 | 1,263 | 265 | -9% |
| AI Agents | 2 | 5,835 | 1,407 | 272 | -21% |
| AI Model Fine-tuning | 1 | 472 | 158 | 73 | -60% |
| RAG | 1 | 1,231 | 278 | 99 | -38% |
| Real-time | 1 | 7,450 | 1,704 | 292 | -47% |
| Reinforcement learning | 1 | 109 | 54 | 27 | -40% |
| Vector Search | 1 | 1,977 | 499 | 171 | -39% |
Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.