Changelog

Qwen3.5 Plus 2026-04-20

Qwen3.5 Plus (April 2026) is a large-scale multimodal language model from Alibaba, supporting text, image, and video inputs with text output. It features a 1M-token context window, enabling large-scale reasoning and multimodal workflows within a single interaction.

This updated version of Qwen3.5 Plus introduces tiered pricing beyond 256K tokens, making it suitable for high-context applications while maintaining flexibility for cost optimization in long-input scenarios.

Qwen3.6 Flash

Qwen3.6 Flash is a fast and efficient model from Alibaba's Qwen 3.6 series, supporting text, image, and video inputs with a 1M-token context window for high-context multimodal workflows.

Optimized for performance and cost efficiency, it features tiered pricing beyond 256K tokens and supports prompt caching with both cache creation and read pricing, making it well suited for large-scale, high-throughput applications.

Qwen3.6 Max Preview

Qwen3.6-Max-Preview is a proprietary frontier model from Alibaba Cloud built on a sparse Mixture-of-Experts (MoE) architecture with approximately 1 trillion parameters. It is optimized for agentic coding, tool use, and long-context reasoning, supporting a 262K token context window.

The model includes an integrated thinking mode that preserves reasoning across multi-turn interactions, along with support for structured outputs and function calling.

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GPT-5.5

GPT-5.5 is OpenAI's frontier model for complex professional workloads, building on GPT-5.4 with stronger reasoning, higher reliability, and improved token efficiency on challenging tasks. It supports text and image inputs and features a 1M+ token context window (≈922K input, 128K output) for large-scale, high-context workflows.

Designed for advanced applications, GPT-5.5 excels in reasoning, coding, and multimodal workflows, enabling efficient execution of complex, multi-step tasks within a single system.

GPT-5.5 Pro

GPT-5.5 Pro is OpenAI's high-capability model optimized for deep reasoning and accuracy on complex, high-stakes workloads. It supports text and image inputs and features a 1M+ token context window (≈922K input, 128K output) for handling large-scale, long-context tasks.

Designed for long-horizon problem solving, agentic coding, and precise multi-step execution, GPT-5.5 Pro delivers strong reliability and performance across advanced engineering, research, and complex workflow scenarios.

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DeepSeek V4 Pro

DeepSeek V4 Pro is a large-scale Mixture-of-Experts (MoE) model with 1.6T total parameters and 49B activated per token, supporting a 1M-token context window for advanced reasoning and long-horizon workflows.

It delivers strong performance across knowledge, mathematics, and software engineering tasks, making it suitable for complex, real-world applications.

DeepSeek V4 Flash

DeepSeek V4 Flash is an efficiency-optimized Mixture-of-Experts (MoE) model with 284B total parameters and 13B activated per token, designed for fast inference and high-throughput workloads.

It supports a 1M-token context window, enabling large-scale reasoning and long-context processing.

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Qwen3.6-35B-A3B

Qwen3.6-35B-A3B is an open-weight Mixture-of-Experts (MoE) multimodal model designed for agentic coding and long-horizon workflows. It features ~35–36B total parameters with ~3B activated per token, enabling strong performance with high inference efficiency. The model supports text and image inputs with a ~260K token context window, and is optimized for repository-level reasoning, multi-step development, and tool-driven workflows.

With strong benchmark performance and improved coherence across extended tasks, Qwen3.6-35B-A3B is well suited for developer tools, coding agents, and real-world engineering applications that require both reasoning depth and efficiency.

Qwen3.6-27B

Qwen3.6-27B is an open-weight 27B-parameter dense multimodal model from the Qwen3.6 series, designed to deliver flagship-level coding and agentic performance at a practical deployment scale. It supports both text and image inputs and introduces improvements in agentic coding, repository-level reasoning, and iterative development workflows. Despite its relatively compact size, it achieves state-of-the-art results on coding benchmarks, outperforming much larger models in tasks such as SWE-bench and terminal-based workflows.

It also provides strong reasoning and multimodal capabilities, along with features like thinking preservation to maintain context across interactions, making it well suited for developer tools, coding agents, and real-world engineering tasks.

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MiMo-V2.5

MiMo-V2.5 is Xiaomi's native omnimodal model, delivering pro-level agentic performance at roughly half the inference cost. It surpasses MiMo-V2-Omni in multimodal perception, particularly in image and video understanding. With a 1M-token context window, it can handle complete documents, extended conversations, and complex task contexts in a single pass.

Combining strong reasoning, rich perception, and cost efficiency, MiMo-V2.5 is well suited for integration into advanced agent frameworks and real-world multimodal applications.

MiMo-V2.5-Pro

MiMo-V2.5-Pro is Xiaomi's flagship model, delivering top-tier performance in agentic capabilities, complex software engineering, and long-horizon tasks. It ranks highly on benchmarks such as ClawEval, GDPVal, and SWE-bench Pro, demonstrating strong real-world reliability. The model can autonomously complete professional tasks that would take human experts days or weeks, executing thousands of tool calls within a single workflow.

With a 1M-token context window, it is well suited for integration into advanced agent frameworks and large-scale task orchestration systems.

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