qwen/qwen3.5-35b-a3b-dottxt

qwen/qwen3.5-35b-a3b-dottxt is a efficient-tier language model from RouterPlus. qwen/qwen3.5-35b-a3b-dottxt is an open-weights model, meaning the weights are publicly available for self-hosting, fine-tuning, and deployment on your own infrastructure — not just through RouterPlus's API. Qwen is Alibaba's open-weights model family, with strong multilingual performance (especially Chinese/English) and a wide parameter range from 0.5B to 72B.

qwen/qwen3.5-35b-a3b-dottxt is priced at $0.35/1M input tokens and $1.2/1M output tokens. Output tokens are 3.4× more expensive than input tokens — this ratio reflects the sequential nature of autoregressive generation, which requires a separate forward pass per output token versus parallel processing of all input tokens simultaneously. At a typical usage pattern of 1,000 input tokens and 500 output tokens per call, qwen/qwen3.5-35b-a3b-dottxt costs approximately $0.95 per 1,000 API calls. Prompt caching reduces repeated input tokens to $0.19999/1M — a 43% discount that compounds significantly for applications with long shared system prompts, multi-turn conversations, or document Q&A where the same context is reused across many queries.

Its 262K-token context window is in the top tier for production models, enabling full-document analysis, long multi-turn conversations, and large codebase understanding without chunking.

qwen/qwen3.5-35b-a3b-dottxt is best suited to general language tasks. Supported capabilities include streaming token output for real-time UIs. When evaluating this model for a specific workload, the most important factors are: context window (does your input fit?), output price (how long are your generations?), and latency (is this interactive or batch?). The pricing table above shows how qwen/qwen3.5-35b-a3b-dottxt compares to similar models across these dimensions.

qwen/qwen3.5-35b-a3b-dottxt sits in the efficient tier — optimized for speed and cost while maintaining solid quality, ideal for high-volume or latency-sensitive workloads. As an open-weights model, qwen/qwen3.5-35b-a3b-dottxt can be self-hosted on GPU compute (see the GPU rental comparison on this site) to eliminate per-token API costs entirely — the break-even point versus API pricing depends on your volume and the GPU hourly rate. When comparing qwen/qwen3.5-35b-a3b-dottxt to alternatives, use the similar models section on this page to find models at comparable price points with different capability tradeoffs. The intelligence score (N/A/100) reflects aggregate benchmark performance — a useful proxy for general capability, though real-world performance varies significantly by task type. Always test with your specific prompts and use case before committing to a model for production.