Compare GPT‑5.4 Thinking vs. Qwen3.5 in 2026

Qwen3.5

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Description

GPT-5.4 Thinking is a specialized version of OpenAI’s GPT-5.4 model designed to deliver enhanced reasoning and structured problem-solving in ChatGPT. It integrates improvements in coding, professional knowledge work, and agent-based workflows into a single AI system. One of its key features is the ability to present a plan for its reasoning before generating a final answer. This allows users to review the direction of the response and make adjustments while the model is still working. By enabling this interactive process, GPT-5.4 Thinking helps produce more precise and relevant results. The model is particularly effective for tasks that require deep research or multi-step reasoning. It also maintains context across longer prompts and conversations, reducing confusion in complex discussions. GPT-5.4 Thinking improves how AI interacts with tools and software environments during problem-solving workflows. Its advanced reasoning capabilities allow it to handle analytical tasks with higher consistency and clarity. As a result, GPT-5.4 Thinking is designed to support professionals who need reliable AI assistance for complex work.

Description

Qwen3.5 represents a major advancement in open-weight multimodal AI models, engineered to function as a native vision-language agent system. Its flagship model, Qwen3.5-397B-A17B, leverages a hybrid architecture that fuses Gated DeltaNet linear attention with a high-sparsity mixture-of-experts framework, allowing only 17 billion parameters to activate during inference for improved speed and cost efficiency. Despite its sparse activation, the full 397-billion-parameter model achieves competitive performance across reasoning, coding, multilingual benchmarks, and complex agent evaluations. The hosted Qwen3.5-Plus version supports a one-million-token context window and includes built-in tool use for search, code interpretation, and adaptive reasoning. The model significantly expands multilingual coverage to 201 languages and dialects while improving encoding efficiency with a larger vocabulary. Native multimodal training enables strong performance in image understanding, video processing, document analysis, and spatial reasoning tasks. Its infrastructure includes FP8 precision pipelines and heterogeneous parallelism to boost throughput and reduce memory consumption. Reinforcement learning at scale enhances multi-step planning and general agent behavior across text and multimodal environments. Overall, Qwen3.5 positions itself as a high-efficiency foundation for autonomous digital agents capable of reasoning, searching, coding, and interacting with complex environments.