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Claude Haiku 4.5 vs GPT-5.6 Terra

Compare Claude Haiku 4.5 and GPT-5.6 Terra 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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OpenAIGPT-5.6 Terra
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Claude Haiku 4.5 vs GPT-5.6 Terra: 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 Terra

GPT-5.6 Terra is the mid-tier reasoning model in OpenAI's GPT-5.6 family, which also includes the flagship Sol and the lightweight Luna. Introduced in a limited preview on June 26, 2026, and made broadly available on July 9, 2026, Terra accepts text and image input and produces text output, supporting vision, function calling, tool use, and agentic workflows. It is designed as a balanced option for everyday professional and production workloads — including coding assistance, document analysis, customer support, and multi-step agent tasks — where both output quality and cost efficiency matter. OpenAI positions Terra as delivering performance competitive with GPT-5.5 at approximately half the price, with a context window of around 1,050,000 tokens. On Terminal-Bench 2.1, Terra scores 84.3%, matching Claude Fable 5 on that benchmark. Under OpenAI's Preparedness Framework, Terra is rated High for cybersecurity and biological capabilities, meaning it demonstrates meaningful capability in those domains without reaching the Critical threshold.

GPT-5.6 introduces a new naming convention in which the generation number (5.6) is paired with a durable capability tier name (Sol, Terra, or Luna), allowing each tier to advance on its own schedule. Terra carries the API identifier gpt-5.6-terra and supports the same reasoning effort controls available across the family, including adjustable reasoning depth. The model includes prompt caching with explicit cache breakpoints and a 30-minute minimum cache life, with cache writes billed at 1.25x the uncached input rate and cache reads receiving a 90% discount. GPT-5.6 Terra is a proprietary, closed-weights model served through the OpenAI API, Codex, and ChatGPT.

Claude Haiku 4.5 vs GPT-5.6 Terra Comparison Table

PropertyClaude Haiku 4.5GPT-5.6 Terra
OrganizationAnthropicOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateOct 2025Jul 2026
Context Window200K1.1M
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$1.00$2.50
Output $/1M$5.00$15.00
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
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