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Claude Haiku 4.5 vs Claude Sonnet 5

Compare Claude Haiku 4.5 and Claude Sonnet 5 side-by-side. See how these vision models stack up in Image Captioning, Open Prompt, OCR, Classification, and Object Detection.

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AnthropicClaude Haiku 4.5
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AnthropicClaude Sonnet 5
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Models in this comparison

Claude Haiku 4.5 vs Claude Sonnet 5: Overview

Claude Haiku 4.5

Claude Haiku 4.5 is Anthropic’s lightweight model in the Claude 4.5 series, released in October 2025 under a proprietary license. Designed for speed and cost efficiency, it delivers near-frontier performance while maintaining Anthropic’s AI Safety Level 2 standard. Haiku 4.5 supports both text and multimodal (text and image) inputs, integrates tool use and extended reasoning, and features a 200,000 token context window, making it adept at handling long or complex workflows. Though the parameter count remains undisclosed, it achieves about 73.3% on SWE-bench Verified, reflecting strong coding and reasoning ability. Haiku 4.5 is ideal for developers and researchers seeking rapid, cost-effective model calls for analysis, coding, or multimodal understanding.

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.

Claude Haiku 4.5 vs Claude Sonnet 5 Comparison Table

PropertyClaude Haiku 4.5Claude Sonnet 5
OrganizationAnthropicAnthropic
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateOct 2025Jun 2026
Context Window200K1.0M
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$1.00$2.00
Output $/1M$5.00$10.00
Vision Tasks
CaptioningDemoDemo
ClassificationDemoDemo
Object DetectionDemoDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
Document Question Answering
Multi-Label Classification
Model Features
LLMs with Vision Capabilities
Multimodal Vision
Foundation Vision
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Visual Understanding
Overall Score
58.21%
70.15%
Avg Response Time3.15s3.90s
Median input tokensincl. image tokens2.2K2.1K
Median output tokens17461
Est. cost / taskon this benchmark$0.0030$0.0048
Defect Detection
80%(12/15)
73.3%(11/15)
Document Understanding
77.8%(7/9)
66.7%(6/9)
Object Counting
0%(0/10)
20%(2/10)
Object Understanding
71.4%(10/14)
92.9%(13/14)
Spatial Understanding
52.6%(10/19)
78.9%(15/19)
OCR
Overall Score
61.57%
83.84%
Avg Response Time2.13s2.77s
Median input tokensincl. image tokens735642
Median output tokens10164
Est. cost / taskon this benchmark$0.0012$0.0019
Focused Scene OCR
61.6%(61/99)
88.9%(88/99)
Handwritten Math
20%(2/10)
50%(5/10)
License Plate Recognition
66.7%(20/30)
90%(27/30)
Text Recognition
63.3%(19/30)
80%(24/30)
VQA & Extraction
65%(39/60)
80%(48/60)

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