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Claude Haiku 4.5 vs GPT-5.4 Nano

Compare Claude Haiku 4.5 and GPT-5.4 Nano 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-5.4 Nano: 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-5.4 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.

Claude Haiku 4.5 vs GPT-5.4 Nano Comparison Table

PropertyClaude Haiku 4.5GPT-5.4 Nano
OrganizationAnthropicOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateOct 2025Mar 2026
Context Window200K400K
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$1.00$0.200
Output $/1M$5.00$1.25
Vision Tasks
CaptioningDemoDemo
ClassificationDemoDemo
Object DetectionDemoDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
Model Features
Foundation Vision
LLMs with Vision Capabilities
Multimodal Vision
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Visual Understanding
Overall Score
58.21%
62.69%
Avg Response Time3.15s3.72s
Median input tokensincl. image tokens2.2K1.4K
Median output tokens174105
Est. cost / taskon this benchmark$0.0030$0.0004
Defect Detection
80%(12/15)
80%(12/15)
Document Understanding
77.8%(7/9)
77.8%(7/9)
Object Counting
0%(0/10)
30%(3/10)
Object Understanding
71.4%(10/14)
64.3%(9/14)
Spatial Understanding
52.6%(10/19)
57.9%(11/19)
OCR
Overall Score
61.57%
62.45%
Avg Response Time2.13s2.59s
Median input tokensincl. image tokens735105
Median output tokens10187
Est. cost / taskon this benchmark$0.0012$0.0001
Focused Scene OCR
61.6%(61/99)
55.6%(55/99)
Handwritten Math
20%(2/10)
20%(2/10)
License Plate Recognition
66.7%(20/30)
83.3%(25/30)
Text Recognition
63.3%(19/30)
70%(21/30)
VQA & Extraction
65%(39/60)
66.7%(40/60)

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