Qwen3 Max
Qwen3 Max is Alibaba's trillion-parameter MoE language model with a context window of 262.1K tokens, delivering competitive performance on coding, mathematics, and enterprise tool-use tasks.
import { streamText } from 'ai'
const result = streamText({ model: 'alibaba/qwen3-max', prompt: 'Why is the sky blue?'})Playground
Try out Qwen3 Max by Alibaba. Usage is billed to your team at API rates. Free users (those who haven't made a payment) get $5 of credits every 30 days.
Providers
Route requests across multiple providers. Copy a provider slug to set your preference. Visit the docs for more info. Using a provider means you agree to their terms, listed under Legal.
P50 throughput on live AI Gateway traffic, in tokens per second (TPS). Visit the docs for more info.
P50 time to first token (TTFT) on live AI Gateway traffic, in milliseconds. View the docs for more info.
Direct request success rate on AI Gateway and per-provider. Visit the docs for more info.
More models by Alibaba
| Model |
|---|
| 240K | 1.8s | 74tps | $1.30/M | $7.80/M | Read: $0.26/M Write: $1.63/M | — | — |  | 04/20/2026 | ||||
| 1M | 0.2s | 88tps | $0.50/M | $3.00/M | Read: $0.1/M Write: $0.63/M | — | — |  | 04/02/2026 | ||||
| 1M | 1.3s | 172tps | $0.10/M | $0.40/M | Read:$0.0/M Write:$0.13/M | — | — | | 02/24/2026 | ||||
| 1M | 1.6s | 110tps | $0.40/M | $2.40/M | Read: $0.04/M Write: $0.5/M | — | — | | 02/16/2026 | ||||
| 256K | 0.3s | 80tps | $0.50/M | $1.20/M | — | — |   | 07/22/2025 | |||||
| 262K | 0.1s | 87tps | $0.07/M | $0.46/M | Read:$0.6/M Write:— | — | — |    +1 | 04/01/2025 |
About Qwen3 Max
Qwen3 Max is the largest model in Alibaba's Qwen3 line, built on a mixture-of-experts (MoE) architecture with over one trillion total parameters. The MoE design allocates computation selectively, enabling performance without activating the full parameter count on every token.
The context window of 262.1K tokens makes it practical for tasks that earlier-generation models had to split across multiple calls: ingesting entire codebases, indexing long legal or financial documents, or tracking dependencies across extended multi-turn conversations. Context caching further reduces the cost of repeatedly processing the same long prefix.
Qwen3 Max performs strongly on structured-output and tool-use benchmarks, recording 74.8 on Tau2-Bench and 79.3% accuracy on LiveBench. On software engineering tasks measured by SWE-bench Verified, Qwen3 Max scored 69.6. These results reflect a consistent emphasis on reliability for enterprise tasks: JSON generation, HTML/CSS formatting, API function calling, and multi-step agentic workflows where predictable output structure matters.
Alibaba positions Qwen3 Max with native bilingual strength in Chinese and English, alongside broad multilingual support. The model is available via API only. Weights aren't publicly released.
What To Consider When Choosing a Provider
- Configuration: For regulated industries requiring data-residency guarantees, cross-reference the geographic deployment region of your chosen provider against applicable compliance frameworks before routing production traffic.
- Zero Data Retention: AI Gateway does not currently support Zero Data Retention for this model. See the documentation for models that support ZDR.
- Authentication: AI Gateway authenticates requests using an API key or OIDC token. You do not need to manage provider credentials directly.
When to Use Qwen3 Max
Best For
- Structured enterprise automation: High-volume workloads that require reliable JSON, XML, or formatted report output
- Long-document analysis: Contracts, scientific papers, and codebases where the full context must remain in-window
- Multi-step function calling: Complex agentic workflows that chain multiple tool invocations
- Professional-grade quantitative work: Mathematical reasoning and quantitative problem-solving at expert difficulty
- Bilingual Chinese-English applications: Products where both languages need equal-quality handling
Consider Alternatives When
- Visible chain-of-thought needed: Consider Qwen3-Max-Thinking when you need extended reasoning with visible step traces
- Creative and conversational writing: Open-ended storytelling or conversational warmth is the primary requirement
- Strict token budgets: A smaller open-weight model may meet your quality bar at lower cost per token
- Latency-critical workloads: Response latency is more important than depth of reasoning
Conclusion
Qwen3 Max brings trillion-parameter scale to tasks that benefit most from it: long-context document work, structured enterprise output, and complex tool use. Its context window of 262.1K tokens and strong benchmark results make it a credible choice for production deployments where reliability and breadth of capability take precedence over speed.
Frequently Asked Questions
How many parameters does Qwen3 Max have?
The model exceeds one trillion total parameters. It's served as a closed-weight API, and model weights aren't available for download.
What is the context window for Qwen3 Max?
The context window is 262.1K tokens. This supports long document analysis and extended multi-turn sessions.
How does Qwen3 Max handle context caching?
The model supports context caching, allowing repeated long prompts, such as a large system prompt or document, to be processed once and reused across many requests, reducing latency and cost.
What is the difference between Qwen3 Max and Qwen3-Max-Thinking?
Qwen3 Max is optimized for fast, high-quality responses without extended internal reasoning traces. Qwen3-Max-Thinking adds a dedicated thinking mode where the model works through complex problems step by step, making it better suited to hard math, competitive coding, and scientific reasoning at the cost of higher token usage.
Does Qwen3 Max support function calling?
Yes. Qwen3 Max was specifically evaluated on tool-use benchmarks (Tau2-Bench: 74.8) and is designed for multi-step agentic workflows involving structured API calls.
Can Qwen3 Max generate outputs in both Chinese and English?
Yes. Alibaba positions Qwen3 Max with strong native support for both Chinese and English, alongside broad multilingual capability.
How does Qwen3 Max score on coding benchmarks?
On SWE-bench Verified, Qwen3 Max recorded a score of 69.6, placing it competitively among other models evaluated on software engineering tasks.