qwen3-235b-a22b

qwen3-235b-a22b is a frontier-tier language model from Alibaba Cloud with 235B parameters. qwen3-235b-a22b 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 Alibaba Cloud's API. It is a reasoning model that uses extended chain-of-thought (CoT) processing before producing a final answer — significantly more capable on complex math, science, and multi-step logic tasks, but slower and more expensive per query than standard models. 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.

qwen3-235b-a22b is priced at $0.6/1M input tokens and $2.4/1M output tokens. Output tokens are 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, qwen3-235b-a22b costs approximately $1.80 per 1,000 API calls. Prompt caching reduces repeated input tokens to $0.15/1M — a 75% 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 131K-token context window handles most long-document tasks — roughly 75,000 words or a full novel — without requiring chunking or retrieval augmentation. Median latency (P50 time-to-first-token) is approximately 1200ms — better suited to batch or async workloads where latency is less critical. Throughput is approximately 55 tokens/second — on the slower side; plan for longer generation times on lengthy outputs. Maximum output length is 16K tokens — sufficient for long-form content generation, detailed code files, and extended reports.

qwen3-235b-a22b is best suited to reasoning, coding, chat. 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 qwen3-235b-a22b compares to similar models across these dimensions.

qwen3-235b-a22b sits in the frontier tier — the highest-capability models available, used when quality is the primary constraint and cost is secondary. As an open-weights model, qwen3-235b-a22b 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 qwen3-235b-a22b to alternatives, use the similar models section on this page to find models at comparable price points with different capability tradeoffs. The intelligence score (83/100) 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.