changelog

Alibaba releases Qwen3.5 Plus with 1M token context window at $0.40 per million input tokens

TL;DR

Alibaba released an updated version of Qwen3.5 Plus on April 27, 2026, with a 1 million token context window. The multimodal model accepts text, image, and video input and is priced at $0.40 per million input tokens and $2.40 per million output tokens, with tiered pricing above 256K tokens.

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Qwen3.5 Plus — Quick Specs

Context window1000K tokens
Input$0.3/1M tokens
Output$1.2/1M tokens

Alibaba releases Qwen3.5 Plus with 1M token context window at $0.40 per million input tokens

Alibaba released an updated version of Qwen3.5 Plus on April 27, 2026, expanding its context window to 1 million tokens. The multimodal model accepts text, image, and video input and produces text output.

Pricing and specifications

The model is priced at $0.40 per million input tokens and $2.40 per million output tokens. According to the model page, tiered pricing applies above 256K tokens, though specific tier rates are not disclosed.

Qwen3.5 Plus supports three input modalities:

  • Text
  • Image
  • Video

The model outputs text only.

Context window expansion

The 1 million token context window represents a significant expansion for the Qwen series. This capacity allows the model to process substantially longer documents, codebases, or multi-turn conversations within a single context.

The model is currently available through OpenRouter, which routes requests across multiple providers with automatic fallbacks. No other distribution channels were disclosed in the announcement.

What this means

Qwen3.5 Plus's pricing positions it in the mid-range of multimodal models with extended context windows. At $0.40 per million input tokens, it's more expensive than text-only models but competitive for multimodal capabilities. The tiered pricing structure suggests Alibaba is targeting use cases that require long context while managing compute costs for shorter inputs. The lack of disclosed benchmark scores or technical specifications makes direct performance comparison with competing models difficult.

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