Roboflow

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.

Compare Claude Sonnet 5 vs Kimi K2.5 live

Run the same image across every model that supports a task and compare their outputs side-by-side.

Extract and compare text from images across multiple models.

Open OCR in the full playground
AnthropicClaude Sonnet 5
Run to compare this model.
MoonshotAIKimi K2.5
Run to compare this model.

Models in this comparison

MoonshotAI

Claude Sonnet 5 vs Kimi K2.5: Overview

Claude Sonnet 5

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

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

PropertyClaude Sonnet 5Kimi K2.5
OrganizationAnthropicMoonshot AI
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateJun 2026Jan 2026
Context Window1.0M256K
Parameters1T
LicenseProprietaryModified MIT
Pricing per 1M tokens
Input $/1M$2.00$0.375
Output $/1M$10.00$2.02
Vision Tasks
CaptioningDemoDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
ClassificationDemo
Document Question Answering
Multi-Label Classification
Object DetectionDemo
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 Time3.90s14.81s
Median input tokensincl. image tokens2.1K1.6K
Median output tokens61766
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 Time2.77s13.09s
Median input tokensincl. image tokens642119
Median output tokens64258
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