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GPT-5.4 Nano vs Claude Haiku 4.5+ 1 other

Compare GPT-5.4 Nano, Claude Haiku 4.5, and 1 other vision model side-by-side. Test these models on OCR, Image Captioning, Classification, Object Detection, and Open Prompt in the Playground.

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OpenAIGPT-5.4 Nano
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AnthropicClaude Haiku 4.5
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GoogleGemini 2.5 Flash-Lite
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Models in this comparison

Model Overviews

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.

GPT-5.4 Nano vs Claude Haiku 4.5 Comparison Table + 1 other

PropertyGPT-5.4 NanoClaude Haiku 4.5Gemini 2.5 Flash-Lite
OrganizationOpenAIAnthropicGoogle
Categoryclosedclosedclosed
Modalitymultimodalmultimodalmultimodal
Release DateMar 2026Oct 2025Jul 2025
Context Window400K200K1.0M
Parameters
LicenseProprietaryProprietaryProprietary
Pricing per 1M tokens
Input $/1M$0.200$1.00$0.100
Output $/1M$1.25$5.00$0.400
Vision Tasks
CaptioningDemoDemoDemo
ClassificationDemoDemoDemo
Object DetectionDemoDemoDemo
OCRDemoDemoDemo
Vision Language
Visual Question AnsweringDemoDemoDemo
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
62.69%
58.21%
53.73%
Avg Response Time3.72s3.15s7.19s
Median input tokensincl. image tokens1.4K2.2K294
Median output tokens1051746
Est. cost / taskon this benchmark$0.0004$0.0030<$0.0001
Defect Detection
80%(12/15)
80%(12/15)
66.7%(10/15)
Document Understanding
77.8%(7/9)
77.8%(7/9)
66.7%(6/9)
Object Counting
30%(3/10)
0%(0/10)
10%(1/10)
Object Understanding
64.3%(9/14)
71.4%(10/14)
71.4%(10/14)
Spatial Understanding
57.9%(11/19)
52.6%(10/19)
47.4%(9/19)
OCR
Overall Score
62.45%
61.57%
77.73%
Avg Response Time2.59s2.13s7.45s
Median input tokensincl. image tokens105735290
Median output tokens8710112
Est. cost / taskon this benchmark$0.0001$0.0012<$0.0001
Focused Scene OCR
55.6%(55/99)
61.6%(61/99)
75.8%(75/99)
Handwritten Math
20%(2/10)
20%(2/10)
70%(7/10)
License Plate Recognition
83.3%(25/30)
66.7%(20/30)
90%(27/30)
Text Recognition
70%(21/30)
63.3%(19/30)
80%(24/30)
VQA & Extraction
66.7%(40/60)
65%(39/60)
75%(45/60)

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