Claude Haiku 4.5 vs GPT-6 Luna
Compare Claude Haiku 4.5 and GPT-6 Luna side-by-side.
Compare Claude Haiku 4.5 vs GPT-6 Luna live
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Claude Haiku 4.5 vs GPT-6 Luna Comparison Table
Evals updated September 22, 2026Pricing updated September 22, 2026
| Property | Claude Haiku 4.5 | GPT-6 Luna |
|---|---|---|
| Organization | Anthropic | OpenAI |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Oct 2025 | Sep 2026 |
| Context Window | 200K | 1.1M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $1.00 | $0.100 |
| Output $/1M | $5.00 | $0.500 |
| Vision Tasks | ||
| Captioning | Demo | |
| Chart Question Answering | ||
| Classification | Demo | |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | Demo | |
| OCR | Demo | |
| Vision Language | ||
| Visual Question Answering | Demo | |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | Not evaluated | 68.6% |
| Avg cost / sample | – | $0.0004 |
| Avg speed / sample | – | 11.27s |
| By task | ||
| Object Detection (low) | – | 56.8% ±1.9, Mean of 3 runs, range 54.8 to 58.5 |
| Object Detection (high) | – | 64.1% ±0.5, Mean of 3 runs, range 63.6 to 64.5 |
| Counting (low) | – | 65.8% ±1.4, Mean of 3 runs, range 64.9 to 67.6 |
| Counting (high) | – | 64.4% ±2.0, Mean of 3 runs, range 62.2 to 66.2 |
| Identification (low) | – | 81.3% ±0.0, Mean of 3 runs, range 81.3 to 81.3 |
| Identification (high) | – | 80.2% ±1.6, Mean of 3 runs, range 78.1 to 81.3 |
| OCR (low) | – | 87.9% ±0.6, Mean of 3 runs, range 87.2 to 88.3 |
| OCR (high) | – | 88.5% ±0.6, Mean of 3 runs, range 87.9 to 89.2 |
| Data Extraction (low) | – | 68.0% ±3.1, Mean of 3 runs, range 65.0 to 71.1 |
| Data Extraction (high) | – | 66.7% ±0.5, Mean of 3 runs, range 66.0 to 67.0 |
| Reasoning (low) | – | 52.1% ±2.0, Mean of 3 runs, range 49.7 to 53.6 |
| Reasoning (high) | – | 60.7% ±1.7, Mean of 3 runs, range 58.9 to 62.3 |
Claude Haiku 4.5 vs GPT-6 Luna: Overview
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-6 Luna is the fast, cost-efficient tier of OpenAI's GPT-6 model family, sitting below GPT-6 Sol and the larger GPT-6 Astra model that opened the generation. It is a proprietary multimodal transformer that accepts text and image input and returns text, and it exposes an adjustable reasoning effort setting so the same model can run in a low-latency mode or spend additional inference compute on harder problems. OpenAI positions it for high-volume and latency-sensitive workloads such as conversational assistants, classification, and lightweight agentic pipelines, while noting that at higher reasoning effort it handles software engineering and computer-use tasks that previously required a Sol-tier model.
The model supports a context window of roughly 1,050,000 input tokens with a maximum output of 128,000 tokens, which allows long documents, extended agent traces, and large code repositories to be processed in a single request. OpenAI describes the GPT-6 generation as improving factual reliability and adopting a more concise communication style relative to the GPT-5.6 series, and attributes the efficiency of the Sol and Luna tiers to gains in caching and inference rather than to reduced capability. Architecture details, parameter counts, and training data are not published.