qwen3.5-397b

qwen3.5-397b is a frontier-tier language model from CloudGPU. qwen3.5-397b is a proprietary model available exclusively through CloudGPU'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.

qwen3.5-397b is priced at $0.54/1M input tokens and $3.24/1M output tokens. Output tokens are 6× 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, qwen3.5-397b costs approximately $2.16 per 1,000 API calls. Prompt caching reduces repeated input tokens to $0.3/1M — a 44% 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 0-token context window is standard for this tier — adequate for most chat and completion tasks, but may require chunking for long-document workloads.

qwen3.5-397b 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 qwen3.5-397b compares to similar models across these dimensions.

qwen3.5-397b sits in the frontier tier — the highest-capability models available, used when quality is the primary constraint and cost is secondary. Unlike open-weights alternatives, qwen3.5-397b is only available via CloudGPU's API — there is no self-hosted option, which simplifies deployment but creates vendor dependency. When comparing qwen3.5-397b 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.