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Claude 3.5 Haiku vs GPT-5.4 Nano

Compare Claude 3.5 Haiku and GPT-5.4 Nano side-by-side. See how these vision models stack up in Image Captioning, OCR, and Open Prompt.

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AnthropicClaude 3.5 Haiku

Claude 3.5 Haiku is deprecated and can no longer be run. Details and evals are still available on its model page.

OpenAIGPT-5.4 Nano
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Claude 3.5 Haiku vs GPT-5.4 Nano Comparison Table

Evals updated August 6, 2026Pricing updated August 7, 2026

PropertyClaude 3.5 HaikuGPT-5.4 Nano
OrganizationAnthropicOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateOct 2024Mar 2026
Context Window200K400K
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$0.200
Output $/1M$1.25
Vision Tasks
CaptioningDemo
Chart Question Answering
ClassificationDemo
Document Question Answering
Image Tagging
Multi-Label Classification
Object DetectionDemo
OCRDemo
Vision Language
Visual Question AnsweringDemo
Model Features
Foundation Vision
LLMs with Vision Capabilities
Multimodal Vision

Claude 3.5 Haiku vs GPT-5.4 Nano: Overview

Claude 3.5 Haiku

Claude 3.5 Haiku, released by Anthropic in October 2024, is the fastest member of the Claude 3.5 family, optimized for low-latency, high-throughput applications. It is a multimodal model that handles both text and image inputs and supports a large ~200,000-token context window. Haiku is designed to balance efficiency with intelligence, outperforming even Claude 3 Opus on several reasoning benchmarks while maintaining its hallmark speed.

Typical applications include real-time chatbots, code completion, large-scale data extraction, and content moderation—scenarios where rapid response and scalability are essential.

GPT-5.4 Nano

GPT-5.4 nano is a high-throughput model developed by OpenAI and released on March 17, 2026, as the efficiency-optimized entry in the GPT-5.4 family. Engineered for cost-sensitive production environments and latency-critical workloads, it features an expanded 400,000-token context window that enables the processing of large document batches or extensive logs in a single pass. The model is primarily optimized for text-heavy operations, serving as a premier engine for high-volume classification, data extraction, ranking, and the orchestration of lightweight sub-agents where speed and low per-token costs are the primary requirements.

While it supports text and image inputs, GPT-5.4 nano is designed as a text-first worker rather than a specialized visual reasoning tool. In multi-model architectures, it is best utilized for structured text tasks and simple coding sub-tasks, leaving intensive vision reasoning and UI navigation to its sibling, GPT-5.4 mini. Compared to the previous GPT-5 nano, this version provides a significant leap in reliability for structured outputs and tool calling, making it a dependable and economical choice for developers building scalable, automated pipelines that require rapid execution at the edge of the GPT-5.4 ecosystem.