Kimi K2.5 vs Qwen3 VL 235B A22B Instruct
Compare Kimi K2.5 and Qwen3 VL 235B A22B Instruct side-by-side. See how these vision models stack up in Image Captioning, OCR, and Open Prompt.
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Kimi K2.5 vs Qwen3 VL 235B A22B Instruct Comparison Table
Evals updated August 6, 2026Pricing updated August 9, 2026
| Property | Kimi K2.5 | Qwen3 VL 235B A22B Instruct |
|---|---|---|
| Organization | Moonshot AI | Qwen |
| Category | open | open |
| Modality | multimodal | multimodal |
| Release Date | Jan 2026 | Sep 2025 |
| Context Window | 256K | 256K |
| Parameters | 1T | 235B |
| License | Modified MIT | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $0.570 | $0.210 |
| Output $/1M | $2.85 | $1.90 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | ||
| Classification | ||
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Object Detection | ||
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | Not evaluated | 65.8% |
| Avg cost / sample | – | $0.0007 |
| Avg speed / sample | – | 9.17s |
| By task | ||
| Object Detection | – | 52.2% $0.0014 |
| Counting | – | 47.3% $0.0002 |
| Identification | – | 90.6% $0.0002 |
| OCR | – | 88.1% $0.0010 |
| Data Extraction | – | 86.6% $0.0002 |
| Reasoning (low) | – | 29.8% $0.0002 |
| Reasoning (high) | – | 33.8% $0.0002 |
Kimi K2.5 vs Qwen3 VL 235B A22B Instruct: Overview
Kimi K2.5 is a frontier-scale multimodal AI model developed by Moonshot AI and released on January 27, 2026. As a significant advancement within the Kimi K2 family, it utilizes a sparse Mixture-of-Experts (MoE) architecture with 1 trillion total parameters (32 billion active per inference) and a massive 256K-token context window. The model features native multimodal integration via a 400M-parameter MoonViT encoder, allowing it to process text, images, and video frames simultaneously. Built for both speed and depth, it offers "Instant" and "Thinking" modes, the latter of which excels at expert-level reasoning, scoring 50.2% on the Humanity’s Last Exam (HLE) benchmark when equipped with tools.
The model is released under a Modified MIT License, which remains open-weight but requires attribution for high-revenue commercial entities. It introduces an "Agent Swarm" paradigm capable of coordinating up to 100 specialized sub-agents for parallel workflows, significantly reducing latency in complex research tasks. For vision tasks, Kimi K2.5 demonstrates strong autonomous visual debugging capabilities, where it can inspect its own generated UI outputs against visual specifications to iteratively refine frontend code. This makes it a powerful choice for developers testing automated UI reconstruction, high-fidelity OCR document processing, and multi-step agentic research grounded in complex visual data.
Qwen3 VL 235B A22B Instruct is a flagship multimodal vision-language model developed by Qwen (Alibaba Cloud), designed for instruction-following tasks that combine advanced text generation with visual understanding. It serves as a high-end open-weight model for developers and researchers building multimodal AI systems that require strong reasoning, perception, and long-context capabilities.
The model supports interleaved text and image inputs, very long context windows (up to roughly 256K tokens), and efficient inference through a mixture-of-experts architecture with about 22B active parameters out of 235B total. In today’s landscape, it competes with top-tier proprietary vision-language models while offering the advantages of open weights and flexible deployment. Typical applications include multimodal assistants, document and image analysis, visual reasoning, and large-context instruction-based workflows.