Alibaba Releases Qwen3.8 Max (0902), a 2.4-Trillion-Parameter MoE Model With 1M-Token Context

Alibaba Ships Qwen3.8 Max (0902) Snapshot

Alibaba's Qwen team has released Qwen3.8 Max (0902), an updated snapshot of its Qwen3.8 Max model. According to the model listing on OpenRouter, it is a 2.4-trillion-parameter mixture-of-experts (MoE) model with a 1-million-token context window that accepts text, image, and video input and returns text output.

The model has reasoning enabled by default, with configurable reasoning effort exposed through the API. Alibaba states the snapshot was post-trained specifically for coding and agentic work, including multi-step software projects, multi-tool orchestration, and long-horizon task execution. It also targets chart reasoning, document parsing, and multimodal understanding across long documents and extended video.

Pricing and Access

The model is priced at $2 per 1M input tokens and $6 per 1M output tokens through Alibaba Cloud Intelligence, the listed provider on OpenRouter. Cache read pricing is listed at $0.25 per 1M tokens, with 5-minute cache creation at $2.50 per 1M tokens and 5-minute cache read at $0.17 per 1M tokens. The listing shows an availability window of 3 days at the time of publication, with uptime data not yet available.

Capabilities

According to Alibaba, Qwen3.8 Max (0902) supports:

• Tool calling and structured outputs for agentic workflows
• Multi-step, multi-tool task orchestration
• Long-horizon task execution for extended software projects
• Chart reasoning and document parsing
• Multimodal understanding over long documents and extended video
• Configurable reasoning effort

No independent benchmark scores were included in the source listing, so performance claims on coding, agentic, or multimodal tasks remain unverified pending third-party evaluation.

What This Means

A 2.4-trillion-parameter MoE model with a 1M-token context window puts Qwen3.8 Max (0902) in the same scale tier as the largest frontier models currently shipping from US labs, at least on paper. The pricing — $2/$6 per 1M tokens — undercuts many Western frontier-model equivalents, continuing Alibaba's pattern of aggressive pricing on high-capability checkpoints.

The emphasis on agentic and coding post-training, combined with default-on reasoning and tool calling, signals Qwen is positioning this snapshot for developers building autonomous coding agents and long-running multi-step workflows rather than general chat use cases. The lack of published benchmark scores in this listing means buyers will need to run their own evaluations before committing production traffic. As with prior Qwen snapshots, expect independent benchmark comparisons against GPT and Claude model families to surface within days of wider availability.