Claude Haiku 5.5 vs GPT-5.4 Mini
Compare Claude Haiku 5.5 and GPT-5.4 Mini side-by-side.
Compare Claude Haiku 5.5 vs GPT-5.4 Mini 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
Claude Haiku 5.5 vs GPT-5.4 Mini on Vision Evals
Claude Haiku 5.5 scores higher on 5 of the six Vision Evals tasks.
The widest gap is Object Detection, where Claude Haiku 5.5 leads 65.8% to 15.8%.
Overall, Claude Haiku 5.5 averages 77.2% (#20 of 61) against 64.7% (#46 of 61) for GPT-5.4 Mini.
Claude Haiku 5.5 is cheaper ($0.0005 vs $0.0030 per sample), while GPT-5.4 Mini is faster (5.3s vs 13.2s per sample).
Claude Haiku 5.5 vs GPT-5.4 Mini Comparison Table
Evals updated October 8, 2026Pricing updated October 8, 2026
| Property | Claude Haiku 5.5 | GPT-5.4 Mini |
|---|---|---|
| Organization | Anthropic | OpenAI |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Oct 2026 | Mar 2026 |
| Context Window | 1.0M | 400K |
| Parameters | undisclosed | Unknown |
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.100 | $0.750 |
| Output $/1M | $0.500 | $4.50 |
| Vision Tasks | ||
| Captioning | Supported | Demo |
| Chart Question Answering | Supported | Supported |
| Classification | Supported | Demo |
| Document Question Answering | Supported | Supported |
| Image Tagging | Supported | Supported |
| Multi-Label Classification | Supported | Supported |
| Object Detection | Supported | Demo |
| OCR | Supported | Demo |
| Vision Language | Supported | Supported |
| Visual Question Answering | Supported | Demo |
| Model Features | ||
| Foundation Vision | Supported | Supported |
| LLMs with Vision Capabilities | Supported | Supported |
| Multimodal Vision | Supported | Supported |
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 77.2% | 64.7% |
| Avg cost / sample | $0.0005 | $0.0030 |
| Avg speed / sample | 13.23s | 5.25s |
| By task | ||
| Object Detection (low) | 65.8% ±0.6, Mean of 3 runs, range 65.1 to 66.2 | 15.8% ±0.4, Mean of 3 runs, range 15.3 to 16.1 |
| Object Detection (high) | 68.2% ±1.0, Mean of 3 runs, range 67.2 to 69.2 | 16.6% ±0.8, Mean of 3 runs, range 15.8 to 17.4 |
| Counting (low) | 68.9% ±4.7, Mean of 3 runs, range 64.9 to 74.3 | 58.6% ±2.0, Mean of 3 runs, range 56.8 to 60.8 |
| Counting (high) | 73.0% ±1.3, Mean of 3 runs, range 71.6 to 74.3 | 64.9% ±2.0, Mean of 3 runs, range 63.5 to 67.6 |
| Identification (low) | 83.3% ±3.1, Mean of 3 runs, range 81.3 to 87.5 | 83.3% ±1.6, Mean of 3 runs, range 81.3 to 84.4 |
| Identification (high) | 86.5% ±1.6, Mean of 3 runs, range 84.4 to 87.5 | 82.3% ±3.1, Mean of 3 runs, range 78.1 to 84.4 |
| OCR (low) | 90.1% ±1.1, Mean of 3 runs, range 88.8 to 91.0 | 89.5% ±1.3, Mean of 3 runs, range 88.1 to 90.6 |
| OCR (high) | 88.0% ±1.3, Mean of 3 runs, range 87.0 to 89.6 | 89.0% ±1.8, Mean of 3 runs, range 87.7 to 91.2 |
| Data Extraction (low) | 85.9% ±1.5, Mean of 3 runs, range 84.5 to 87.6 | 84.2% ±2.1, Mean of 3 runs, range 82.5 to 86.6 |
| Data Extraction (high) | 87.3% ±0.5, Mean of 3 runs, range 86.6 to 87.6 | 82.1% ±2.1, Mean of 3 runs, range 80.4 to 84.5 |
| Reasoning (low) | 68.9% ±2.0, Mean of 3 runs, range 66.9 to 70.9 | 57.0% ±3.3, Mean of 3 runs, range 54.3 to 60.9 |
| Reasoning (high) | 74.8% ±2.6, Mean of 3 runs, range 72.2 to 77.5 | 64.0% ±1.3, Mean of 3 runs, range 62.9 to 65.6 |
Claude Haiku 5.5 vs GPT-5.4 Mini: Overview
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 mini is a fast, cost-efficient model developed by OpenAI and released on March 17, 2026, optimized for high-throughput workloads and subagent orchestration. It supports text and image inputs within a 400,000-token context window, making it ideal for processing extensive visual datasets and large codebases in a single request. Designed for low-latency production environments, the model integrates with key API features including function calling, web search, and tool-based computer use, allowing it to assist in automated workflows that require navigating digital interfaces.
Compared to the previous GPT-5 mini, this version runs more than twice as fast while approaching the performance levels of the flagship GPT-5.4 on reasoning and coding benchmarks. While the larger GPT-5.4 introduces native, state-of-the-art computer-use capabilities, GPT-5.4 mini provides a scalable alternative for interpreting screenshots and reasoning over dense UI layouts. For vision tasks on Playground, it excels at extracting structured information from visual documents and assisting in agentic tasks that involve real-time interpretation of software interfaces alongside text.
Frequently Asked Questions
On Roboflow's Vision Evals, Claude Haiku 5.5 performed better. It scores higher on 5 of the six vision tasks and averages 77.2% (#20 of 61) against 64.7% (#46 of 61) for GPT-5.4 Mini. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
Yes. On the Vision Evals Object Detection benchmark at low effort, Claude Haiku 5.5 leads with 65.8% against 15.8%. This is the widest gap between the two models across the benchmark's tasks.
Claude Haiku 5.5 is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0005 per sample against $0.0030. Claude Haiku 5.5 is priced at $0.10 per 1M input tokens and $0.50 per 1M output; GPT-5.4 Mini is priced at $0.75 per 1M input tokens and $4.50 per 1M output. Actual costs depend on your image sizes, prompts, and output length.
GPT-5.4 Mini is faster. Across Roboflow's Vision Evals it averaged 5.3s per inference against 13.2s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.