qwen3-coder-next is a efficient-tier language model from Alibaba Cloud. qwen3-coder-next 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-coder-next is priced at $0.2/1M input tokens and $1.5/1M output tokens. Output tokens are 7.5× 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-coder-next costs approximately $0.95 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 $0.2/1M input rate.
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. Median latency (P50 time-to-first-token) is approximately 400ms — suitable for interactive applications where sub-second response is acceptable. Throughput is approximately 150 tokens/second — adequate for streaming at comfortable reading speed. Maximum output length is 33K tokens — sufficient for long-form content generation, detailed code files, and extended reports.
qwen3-coder-next is best suited to coding, cost. Supported capabilities include function calling / tool use for agentic workflows, streaming token output for real-time UIs. 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-coder-next compares to similar models across these dimensions.
qwen3-coder-next sits in the efficient tier — optimized for speed and cost while maintaining solid quality, ideal for high-volume or latency-sensitive workloads. Unlike open-weights alternatives, qwen3-coder-next is only available via Alibaba Cloud's API — there is no self-hosted option, which simplifies deployment but creates vendor dependency. When comparing qwen3-coder-next to alternatives, use the similar models section on this page to find models at comparable price points with different capability tradeoffs. The intelligence score (80/100) 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 217 efficient models tracked. Avg: $0.321/1M.
| Model ID | qwen3-coder-next |
| Provider | Alibaba Cloud |
| Model Family | Qwen3 Coder |
| Tier | Efficient |
| Context Window | 262,144 tokens (262K) |
| Max Output Tokens | 32,768 tokens |
| Input Price | $0.2 / 1M tokens |
| Output Price | $1.5 / 1M tokens |
| Cached Input Price | Not available |
| Throughput | 150 tokens/sec |
| Latency (P50) | 400ms |
| Intelligence Score | 80/100 |
| 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 | No |
| 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-coder-next? These GPUs are well-matched for this model's compute and memory requirements.