Claude Sonnet 5 vs Kimi K2.5
Compare Claude Sonnet 5 and Kimi K2.5 side-by-side. See how these vision models stack up in Open Prompt, OCR, and Image Captioning.
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Claude Sonnet 5 vs Kimi K2.5: Overview
Claude Sonnet 5 is a mid-tier large language model from Anthropic, released on June 30, 2026, as the latest model in the Sonnet series and a direct successor to Claude Sonnet 4.6. It is a hybrid reasoning model designed primarily for agentic workflows, software coding, and professional tasks. The model features a 1 million token context window, a 128k maximum output token limit, and runs adaptive thinking by default, giving API users fine-grained control over reasoning effort across five levels (low, medium, high, max, and extra-high). It uses an updated tokenizer shared with Opus 4.7 and later models, which produces approximately 30% more tokens for equivalent text compared to earlier Claude models. On benchmarks, Sonnet 5 scores 63.2% on agentic coding and 81.2% on OSWorld, narrowing the gap with Opus 4.8 while remaining at Sonnet-tier pricing.
The model supports text and image input with text output, and accepts tools including browsers and terminals for autonomous multi-step task execution. Anthropic's safety evaluations report that Sonnet 5 shows a lower rate of undesirable behaviors than Sonnet 4.6 and is generally safer in agentic contexts, with improved resistance to prompt injection and reduced sycophancy. Cybersecurity safeguards equivalent to those on Opus 4.7 and 4.8 are active, though Anthropic notes the model was not deliberately trained on cybersecurity tasks. The model is proprietary and API-only, with no open weights.
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.
Claude Sonnet 5 vs Kimi K2.5 Comparison Table
| Property | Claude Sonnet 5 | Kimi K2.5 |
|---|---|---|
| Organization | Anthropic | Moonshot AI |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Jun 2026 | Jan 2026 |
| Context Window | 1.0M | 256K |
| Parameters | 1T | |
| License | Proprietary | Modified MIT |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | $0.375 |
| Output $/1M | $10.00 | $2.02 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Classification | Demo | |
| Document Question Answering | ||
| Multi-Label Classification | ||
| Object Detection | Demo | |
| Model Features | ||
| Multimodal Vision | ||
| LLMs with Vision Capabilities | ||
Vision Evalspass/fail results · 67 prompts Score key:≥75%40–74%<40% | ||
| Visual Understanding | ||
| Overall Score | 70.15% | 35.82% |
| Avg Response Time | 3.90s | 14.81s |
| Median input tokensincl. image tokens | 2.1K | 1.6K |
| Median output tokens | 61 | 766 |
| Est. cost / taskon this benchmark | $0.0048 | $0.0021 |
| Defect Detection | 73.3%(11/15) | 46.7%(7/15) |
| Document Understanding | 66.7%(6/9) | 55.6%(5/9) |
| Object Counting | 20%(2/10) | 10%(1/10) |
| Object Understanding | 92.9%(13/14) | 42.9%(6/14) |
| Spatial Understanding | 78.9%(15/19) | 26.3%(5/19) |
| OCR | ||
| Overall Score | 83.84% | 19.65% |
| Avg Response Time | 2.77s | 13.09s |
| Median input tokensincl. image tokens | 642 | 119 |
| Median output tokens | 64 | 258 |
| Est. cost / taskon this benchmark | $0.0019 | $0.0006 |
| Focused Scene OCR | 88.9%(88/99) | 10.1%(10/99) |
| Handwritten Math | 50%(5/10) | 50%(5/10) |
| License Plate Recognition | 90%(27/30) | 6.7%(2/30) |
| Text Recognition | 80%(24/30) | 26.7%(8/30) |
| VQA & Extraction | 80%(48/60) | 33.3%(20/60) |
Output tokens (incl. reasoning) and est. cost / task are measured on this benchmark from a single low-temperature run, and shown only for models whose run covered at least 90% of prompts. Methodology