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Claude Opus 4.6 vs Kimi K3

Compare Claude Opus 4.6 and Kimi K3 side-by-side. See how these vision models stack up in Open Prompt, OCR, Object Detection, Classification, and Image Captioning.

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AnthropicClaude Opus 4.6
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MoonshotAIKimi K3
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Models in this comparison

MoonshotAI

Claude Opus 4.6 vs Kimi K3 Comparison Table

Evals updated July 10, 2026Pricing updated July 17, 2026

PropertyClaude Opus 4.6 Kimi K3
OrganizationAnthropicMoonshot AI
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateFeb 2026Jul 2026
Context Window1.0M1.0M
Parameters2.8T
LicenseProprietaryModified MIT
Pricing per 1M tokens
Input $/1M$5.00
Output $/1M$25.00
Vision Tasks
CaptioningDemoDemo
ClassificationDemoDemo
Object DetectionDemoDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
Document Question Answering
Model Features
LLMs with Vision Capabilities
Multimodal Vision
Foundation Vision

Claude Opus 4.6 vs Kimi K3: Overview

Claude Opus 4.6

Claude Opus 4.6 is the flagship large language model from Anthropic, released on 2026-02-05 for advanced reasoning, complex coding, and enterprise agent workflows. It supports text and image inputs via API, offers a 200K-token standard context window with a 1M-token beta option, and enables outputs up to 128K tokens, with adaptive reasoning and context compaction for sustained tasks.

As of 2026-02-17, Anthropic also released Claude Sonnet 4.6, extending the 1M-token context window to a broader tier. Opus remains positioned for maximum depth and benchmark performance, while Sonnet 4.6 brings long-context capability to more cost- and latency-sensitive production use cases.

Kimi K3

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.

Frequently Asked Questions

Claude Opus 4.6 is released under Proprietary, while Kimi K3 uses Modified MIT. Licensing often matters more than raw accuracy for commercial deployments, so check the terms against how you plan to ship.

Yes. The comparison demo on this page runs both models on the same image side by side for open prompts and OCR in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.