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Claude Haiku 4.5 vs GPT-5.6 Sol

Compare Claude Haiku 4.5 and GPT-5.6 Sol 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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Claude Haiku 4.5 vs GPT-5.6 Sol: 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.

GPT-5.6 Sol

GPT-5.6 Sol is the flagship model in OpenAI's GPT-5.6 family, which also includes Terra (a balanced everyday-work tier) and Luna (a fast, cost-efficient tier). Sol is designed for demanding reasoning, long-horizon agentic workflows, software engineering, computer use, scientific research, and cybersecurity tasks. It introduces two new capability modes: a "max" reasoning effort setting that allocates additional compute time for difficult problems, and an "ultra" mode that coordinates multiple subagents in parallel to accelerate complex, multi-step work. The model supports native multimodal input, allowing it to process screenshots, diagrams, charts, documents, and photographs alongside text. A reported context window of approximately 1.5 million tokens enables processing of large codebases, lengthy research documents, and extended agentic sessions.

GPT-5.6 Sol was announced on June 26, 2026, initially in a limited preview for trusted partners, and reached general availability on July 9, 2026. On the Agents' Last Exam benchmark, which evaluates long-running professional workflows across 55 fields, Sol scores 53.6. On Terminal-Bench 2.1, which tests command-line agentic coding workflows, Sol Ultra achieves 91.9%. The model also demonstrates gains in life sciences evaluations, including long-horizon genomics and quantitative biology analyses. OpenAI paired the release with its most extensive safety evaluation to date, combining human red teaming with large-scale automated testing, and classified Sol as High capability in both cybersecurity and biological risk under its Preparedness Framework, though it does not cross the Critical threshold in either category.

Claude Haiku 4.5 vs GPT-5.6 Sol Comparison Table

PropertyClaude Haiku 4.5GPT-5.6 Sol
OrganizationAnthropicOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateOct 2025Jul 2026
Context Window200K1.5M
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$1.00$5.00
Output $/1M$5.00$30.00
Vision Tasks
CaptioningDemoDemo
ClassificationDemoDemo
Object DetectionDemoDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
Chart Question Answering
Document Question Answering
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%
Avg Response Time3.15s
Median input tokensincl. image tokens2.2K
Median output tokens174
Est. cost / taskon this benchmark$0.0030
Defect Detection
80%(12/15)
Document Understanding
77.8%(7/9)
Object Counting
0%(0/10)
Object Understanding
71.4%(10/14)
Spatial Understanding
52.6%(10/19)
OCR
Overall Score
61.57%
Avg Response Time2.13s
Median input tokensincl. image tokens735
Median output tokens101
Est. cost / taskon this benchmark$0.0012
Focused Scene OCR
61.6%(61/99)
Handwritten Math
20%(2/10)
License Plate Recognition
66.7%(20/30)
Text Recognition
63.3%(19/30)
VQA & Extraction
65%(39/60)

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