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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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
| Property | GPT-5.4 Nano | Claude Haiku 4.5 | Gemini 2.5 Flash-Lite |
|---|---|---|---|
| Organization | OpenAI | Anthropic | |
| Category | closed | closed | closed |
| Modality | multimodal | multimodal | multimodal |
| Release Date | Mar 2026 | Oct 2025 | Jul 2025 |
| Context Window | 400K | 200K | 1.0M |
| Parameters | |||
| License | Proprietary | Proprietary | Proprietary |
| Pricing per 1M tokens | |||
| Input $/1M | $0.200 | $1.00 | $0.100 |
| Output $/1M | $1.25 | $5.00 | $0.400 |
| Vision Tasks | |||
| Captioning | Demo | Demo | Demo |
| Classification | Demo | Demo | Demo |
| Object Detection | Demo | Demo | Demo |
| OCR | Demo | Demo | Demo |
| Vision Language | |||
| Visual Question Answering | Demo | Demo | Demo |
| 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 Time | 3.72s | 3.15s | 7.19s |
| Median input tokensincl. image tokens | 1.4K | 2.2K | 294 |
| Median output tokens | 105 | 174 | 6 |
| 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 Time | 2.59s | 2.13s | 7.45s |
| Median input tokensincl. image tokens | 105 | 735 | 290 |
| Median output tokens | 87 | 101 | 12 |
| 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