Kimi K3 vs Qwen3 VL 235B A22B Instruct
Compare Kimi K3 and Qwen3 VL 235B A22B Instruct side-by-side. See how these vision models stack up in Open Prompt, Image Captioning, and OCR.
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Kimi K3 vs Qwen3 VL 235B A22B Instruct Comparison Table
Evals updated July 10, 2026Pricing updated July 21, 2026
| Property | Kimi K3 | Qwen3 VL 235B A22B Instruct |
|---|---|---|
| Organization | Moonshot AI | Qwen |
| Category | open | open |
| Modality | multimodal | multimodal |
| Release Date | Jul 2026 | Sep 2025 |
| Context Window | 1.0M | 256K |
| Parameters | 2.8T | 235B |
| License | Modified MIT | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $3.00 | $0.210 |
| Output $/1M | $15.00 | $1.90 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Object Detection | Demo | |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| classification | Demo | |
| Document Question Answering | ||
| Model Features | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks | ||
| Overall | Not evaluated | 66.4% |
| Object Detection | – | 42.3% |
| Counting | – | 47.3% |
| Identification | – | 90.6% |
| OCR | – | 88.1% |
| Data Extraction | – | 86.6% |
| Reasoning | – | 43.5% |
| Avg cost / sample | – | $0.0007 |
| Avg speed / sample | – | 8.2s |
Kimi K3 vs Qwen3 VL 235B A22B Instruct: Overview
Kimi K3 is a sparse Mixture-of-Experts large language model developed by Moonshot AI, with 2.8 trillion total parameters and a 1-million-token context window. The model activates 16 out of 896 experts per token using the Stable LatentMoE framework, and is built on two architectural innovations: Kimi Delta Attention (KDA), a hybrid linear attention mechanism that enables up to 6.3x faster decoding in long-context settings, and Attention Residuals (AttnRes), which selectively retrieves representations across model depth and delivers roughly 25% higher training efficiency. Together with refined training and data recipes, these structural advances yield approximately 2.5x better overall scaling efficiency compared to its predecessor Kimi K2. The model applies quantization-aware training from the supervised fine-tuning stage onward, using MXFP4 weights with MXFP8 activations for hardware compatibility. Thinking mode is always enabled at launch, with reasoning effort configurable via the reasoning_effort field.
Kimi K3 supports native visual understanding alongside text, accepting image inputs for tasks that combine software engineering and visual reasoning. It targets long-horizon coding, knowledge work, and agentic workflows, and ships in two variants: K3 Max for general chat and agent tasks, and K3 Swarm Max for large-scale parallel processing across many coordinated sub-agents. The model is compatible with the OpenAI SDK via an OpenAI-compatible API. Full model weights are scheduled for release by July 27, 2026 under a Modified MIT license, following the open-weight pattern established by the Kimi K2 model family. A technical report with full architecture, training, and evaluation details is expected to accompany the weights release.
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.