qwen3.5-plus is a advanced-tier language model from Alibaba Cloud. qwen3.5-plus is a proprietary model available exclusively through Alibaba Cloud'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-plus is priced at $0.4/1M input tokens and $2.4/1M 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-plus costs approximately $1.60 per 1,000 API calls. Alibaba Cloud does not publish a prompt caching rate for this model, so repeated long contexts are billed at the standard $0.4/1M input rate.
Its 1.0M-token context window is among the largest available — sufficient for processing entire codebases, book-length documents, or hours of transcribed audio in a single API call. Median latency (P50 time-to-first-token) is approximately 400ms — suitable for interactive applications where sub-second response is acceptable. Throughput is approximately 160 tokens/second — fast enough to stream responses at reading speed with headroom for concurrent requests. Maximum output length is 33K tokens — sufficient for long-form content generation, detailed code files, and extended reports.
qwen3.5-plus is best suited to chat, coding, vision. Supported capabilities include vision inputs (images alongside text), function calling / tool use for agentic workflows, streaming token output for real-time UIs. Vision support enables document analysis, screenshot understanding, diagram interpretation, and visual question answering — images are converted to tokens at the standard input rate based on resolution. Function calling support makes it suitable for building AI agents, structured data extraction pipelines, and any workflow requiring the model to invoke external APIs or return typed JSON. 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-plus compares to similar models across these dimensions.
qwen3.5-plus sits in the advanced tier — strong general-purpose models balancing capability and cost, suitable for most production applications. Unlike open-weights alternatives, qwen3.5-plus is only available via Alibaba Cloud's API — there is no self-hosted option, which simplifies deployment but creates vendor dependency. When comparing qwen3.5-plus to alternatives, use the similar models section on this page to find models at comparable price points with different capability tradeoffs. The intelligence score (84/100) 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.
Composite quality score based on reasoning, coding, and instruction-following benchmarks.
Input price vs 184 advanced models tracked. Avg: $1.38/1M.
| Model ID | qwen3.5-plus |
| Provider | Alibaba Cloud |
| Model Family | Qwen3.5 |
| Tier | Advanced |
| Context Window | 1,000,000 tokens (1.0M) |
| Max Output Tokens | 32,768 tokens |
| Input Price | $0.4 / 1M tokens |
| Output Price | $2.4 / 1M tokens |
| Cached Input Price | Not available |
| Throughput | 160 tokens/sec |
| Latency (P50) | 400ms |
| Intelligence Score | 84/100 |
| LiveBench Score | — |
| Cost per Task (LiveBench) | — |
| Data Confidence | — |
| HLE Score | — |
| GPQA Diamond | — |
| SWE-bench Score | — |
| OSWorld Score | — |
| BrowseComp Score | — |
| Terminal-Bench Score | — |
| AutoBench Score | — |
| Parameters | — |
| Vision Support | Yes |
| Function Calling | Yes |
| Streaming | Yes |
| Reasoning Model | No |
| Open Weights | No |
| Data Source | Static (verified) |
| Last Updated | 2026-07-01 |
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Self-hosting qwen3.5-plus? These GPUs are well-matched for this model's compute and memory requirements.