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Claude Haiku 5.5 vs GPT-5.4

Compare Claude Haiku 5.5 and GPT-5.4 side-by-side.

Compare Claude Haiku 5.5 vs GPT-5.4 live

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

These models don't share enough common tasks for a side-by-side demo. See the comparison table below for their capabilities.

Models in this comparison

OpenAI

Claude Haiku 5.5 vs GPT-5.4 Comparison Table

Evals updated October 8, 2026Pricing updated October 8, 2026

PropertyClaude Haiku 5.5GPT-5.4
OrganizationAnthropicOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateOct 2026Mar 2026
Context Window1.0M1.1M
ParametersundisclosedUnknown
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$0.100$2.50
Output $/1M$0.500$15.00
Vision Tasks
CaptioningSupportedDemo
Chart Question AnsweringSupportedSupported
ClassificationSupportedDemo
Document Question AnsweringSupportedSupported
Image TaggingSupportedSupported
Multi-Label ClassificationSupportedSupported
Object DetectionSupportedDemo
OCRSupportedDemo
Vision LanguageSupportedSupported
Visual Question AnsweringSupportedDemo
Model Features
Foundation VisionSupportedSupported
LLMs with Vision CapabilitiesSupportedSupported
Multimodal VisionSupportedSupported
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort
Overall
77.2%
Not evaluated
Avg cost / sample$0.0005–
Avg speed / sample13.23s–
By task
Object Detection (low)
65.8%
±0.6, Mean of 3 runs, range 65.1 to 66.2
$0.0006
–
Object Detection (high)
68.2%
±1.0, Mean of 3 runs, range 67.2 to 69.2
$0.0010
–
Counting (low)
68.9%
±4.7, Mean of 3 runs, range 64.9 to 74.3
$0.0003
–
Counting (high)
73.0%
±1.3, Mean of 3 runs, range 71.6 to 74.3
$0.0004
–
Identification (low)
83.3%
±3.1, Mean of 3 runs, range 81.3 to 87.5
$0.0002
–
Identification (high)
86.5%
±1.6, Mean of 3 runs, range 84.4 to 87.5
$0.0003
–
OCR (low)
90.1%
±1.1, Mean of 3 runs, range 88.8 to 91.0
$0.0006
–
OCR (high)
88.0%
±1.3, Mean of 3 runs, range 87.0 to 89.6
$0.0009
–
Data Extraction (low)
85.9%
±1.5, Mean of 3 runs, range 84.5 to 87.6
$0.0002
–
Data Extraction (high)
87.3%
±0.5, Mean of 3 runs, range 86.6 to 87.6
$0.0002
–
Reasoning (low)
68.9%
±2.0, Mean of 3 runs, range 66.9 to 70.9
$0.0004
–
Reasoning (high)
74.8%
±2.6, Mean of 3 runs, range 72.2 to 77.5
$0.0006
–

Claude Haiku 5.5 vs GPT-5.4: Overview

Claude Haiku 5.5

Claude Haiku 5.5 is a proprietary multimodal language model from Anthropic and the smallest member of the Claude 5.5 family, released on October 7, 2026 after Claude Opus 5.5 and Claude Sonnet 5.5. It accepts text and image input and returns text, with a 1M token context window and up to 128K output tokens per request. It is the first Haiku-class model with an adjustable effort parameter: adaptive thinking is on by default and the model decides how much to reason, steered by effort levels from low to max with medium as the default. Its training data cutoff is June 2026. Pricing starts at $0.10 per million input tokens and $0.50 per million output tokens for prompts up to 100K tokens, which Anthropic reports is about 90% lower than Claude Haiku 4.5 for requests in that range.

Anthropic positions Haiku 5.5 for high-volume, latency-sensitive work such as classification, extraction, routing, summarization, and subagent tasks, and describes it as its fastest model to date at standard speed. On visual and agentic evaluations reported at launch, it scores 46.4% on Chartography, a chart reading benchmark, compared with 6.4% for Haiku 4.5 and 61.6% for Sonnet 5.5, and 72.4% on the offline subset of OSWorld 2.1, a screenshot driven computer use benchmark, compared with 15.7% for Haiku 4.5. It uses the same tokenizer as Claude Opus 4.7 and later models, so the same text counts as roughly 30% more tokens than on Haiku 4.5.

GPT-5.4

GPT-5.4 is a proprietary multimodal large language model developed by OpenAI and released on March 5, 2026. It is designed for professional workloads such as advanced software development, research, and agentic automation. The model combines the general reasoning capabilities of the GPT-5 series with software engineering improvements derived from GPT-5.3-Codex. In the API and Codex environments it supports context windows of up to 1 million tokens, enabling long-context reasoning and large-scale code or document workflows.

Compared with GPT-5.2, GPT-5.4 reduces false individual claims by 33% and lowers overall response errors by 18%, improving factual reliability across complex tasks. It is also the first general-purpose OpenAI release with native computer-use capabilities, allowing agents to interact with desktops, browsers, and external applications to complete multi-step workflows. The model family includes three variants: GPT-5.4 (standard), GPT-5.4 Pro for higher-performance workloads, and GPT-5.4 Thinking, a reasoning-oriented version in ChatGPT that presents an upfront plan before generating its response. The API also introduces a Tool Search system that allows models to retrieve tool definitions dynamically, reducing token usage in tool-heavy integrations.