Moonshot AI: Kimi K2.5

MiMo V2.6 Pro is the flagship omni-modal foundation model in Xiaomi's MiMo V2.6 series, released as open weights alongside a Flash variant and a 9B distillation of Qwen3.5. It uses a sparse mixture-of-experts transformer with 1.02 trillion total parameters and roughly 42 billion activated per token, paired with a hybrid attention design that interleaves sliding-window and global attention layers to support a context window of about one million tokens. Dedicated encoders handle non-text inputs, including a vision encoder of roughly 681 million parameters and an audio tokenizer stack, so the model accepts text, images, video, and audio and returns text.Post-training centers on large-scale reinforcement learning across thousands of interactive environments, combined with agentic grading, self-correction cold start, and a multi-prefix multi-teacher on-policy distillation stage that extends behavior to tasks that are hard to verify automatically. The resulting model targets long-horizon agentic work such as software engineering, terminal and computer-use operation, tool calling, cybersecurity analysis, and visual coding, and it reports gains over the prior MiMo generation on SWE-bench Verified, Terminal Bench, and internal visual coding and cyber benchmarks.

Mistral Large 4, nicknamed Le Chonk, is a natively multimodal mixture-of-experts model from Mistral that accepts interleaved text and image input and produces text output. It uses a granular MoE design with roughly 1.05 trillion total parameters and 49 billion active per token, reported as 52 billion when embeddings and output layers are counted, paired with a 1.6 billion parameter vision encoder and a context window of one million tokens. The model is trained from scratch on about 3,800 NVIDIA Grace Blackwell GPUs in Mistral's European data centers and supports more than 160 languages. It behaves as a hybrid instruct and reasoning system, with a reasoning effort setting that selects between direct answers and longer deliberation, alongside function calling and structured output for agentic workflows.Image understanding is a focus of this generation, covering documents, charts, technical drawings and natural scenes, and the model emits bounding box coordinates for visual grounding queries. Reported grounding results include 42 percent on Dense200 and 73 percent on the DIOR-RSVG remote sensing benchmark. Mistral describes agentic vision workflows in which the model zooms into gigapixel satellite imagery or engineering drawings to verify details, and reports coding results such as 62 percent on DeepSWE. Figures published at preview time are preliminary because the reinforcement learning phase is still in progress.

Gemini 3.1 Pro is a proprietary multimodal model from Google’s Gemini 3 series, released in early 2026 and designed for advanced reasoning across large multimodal datasets. It accepts text, images, audio, video, and documents, supporting up to a 1-million-token input context with up to 64k output tokens. Compared with Gemini 3 Pro, it improves long-context synthesis and multi-step reasoning, enabling more reliable analysis of large documents, datasets, and software codebases.The model also advances visual understanding and grounding, allowing it to interpret UI screenshots, diagrams, and real-world scenes while referencing specific regions within images or video. These capabilities make Gemini 3.1 Pro well suited for multimodal workflows involving document processing, interface analysis, robotics research, and complex visual reasoning.

GPT-6.1 Sol is a reasoning model in OpenAI's GPT-6 series that accepts text and image input and returns text. It is an upgrade to GPT-6 Sol positioned to approach the intelligence of the larger GPT-6 Astra model on agentic coding, computer use, and professional knowledge work. The model exposes an adjustable reasoning effort control, ranging from low settings for simple turns to maximum settings for harder tasks, and can be driven with tool use enabled or disabled. It operates over a context window of roughly one million tokens and emits up to 128,000 output tokens in a single response, which supports long-running agent loops over large codebases and multi-document collections. Audio and video inputs are not supported.On the visual side, the model is evaluated on GDP.pdf, a benchmark that asks professional questions about complex PDF documents containing tables, charts, diagrams, and fine-print details, and on OSWorld 2.0, which measures agents operating graphical computer applications. OpenAI reports that GPT-6.1 Sol performs on par with or better than GPT-6 Sol across its image input safety evaluations, and that the share of responses containing a factual error at low reasoning effort falls from 11.4 percent to 7.7 percent.

Claude Opus 4.7 is a proprietary multimodal language model developed by Anthropic, released on April 16, 2026. It is designed for agentic coding, long-horizon task execution, and enterprise knowledge work. The model supports text and vision inputs and operates with a context window of up to 1,000,000 tokens. It introduces adaptive thinking, which dynamically allocates reasoning based on task complexity, along with configurable effort controls including a new xhigh setting that sits between the existing high and max levels. It achieves 87.6% on SWE-bench Verified and 78.0% on OSWorld-Verified, reflecting strong performance on autonomous software engineering and computer use tasks respectively.Compared to Claude Opus 4.6, version 4.7 shows improved instruction following and higher reliability in extended agentic tasks. Vision capabilities now support high-resolution inputs up to 2,576px on the long edge (~3.75 megapixels), more than three times the resolution of prior Claude models, enabling finer interpretation of dense diagrams, UI screenshots, and document layouts. These improvements, combined with self-verification on long-running tasks and a new task budget system for controlling agentic loops, make it well-suited for complex software engineering, technical analysis, and multimodal vision workflows.

Qwen-VL-Max is a proprietary vision-language model developed by Alibaba’s QwenLM team. Released on February 1, 2025, it is the flagship offering in the Qwen-VL family and sits above the VL-Plus tier in capability.The model supports text and image inputs and provides a context window of up to 131,072 tokens (with a maximum input size of 129,024 tokens), according to Alibaba Cloud Model Studio. While the parameter count for VL-Max has not been publicly disclosed, the broader Qwen2.5-VL series includes open-weight models scaling up to 72B parameters.Qwen-VL-Max is optimized for advanced multimodal applications such as document parsing, visual reasoning, multilingual analysis, and structured data extraction. Unlike the open Qwen2.5-VL variants, VL-Max is not available as open weights.