GPT-5.5 vs LLaVA-1.5
Compare GPT-5.5 and LLaVA-1.5 side-by-side.
Compare GPT-5.5 vs LLaVA-1.5 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
GPT-5.5 vs LLaVA-1.5: Overview
GPT-5.5 is a multimodal large language model released by OpenAI on April 23, 2026, engineered for autonomous, multi-step knowledge work and agentic workflows. It accepts text, images, and code as input, featuring enhanced spatial reasoning and visual grounding to support its computer use capabilities for operating software and navigating UI elements. Built to execute complex workflows end-to-end, the model interprets loosely defined tasks, selects appropriate tools, and performs self-verification with minimal user intervention. It is available in a standard version, a Thinking mode for extended reasoning budgets, and a Pro variant that uses parallel test-time compute for maximum precision on complex tasks.
Co-optimized with NVIDIA for GB200 NVL72 infrastructure, GPT-5.5 delivers per-token latency comparable to its predecessor GPT-5.4 while maintaining a 1-million-token context window. Despite increased capability, the model achieves greater token efficiency in coding and data analysis workflows, often completing tasks with fewer total tokens than previous versions. OpenAI reports a 60% reduction in hallucination rate compared to GPT-5.4, improving reliability for accuracy-sensitive applications. API access is available via the Responses and Chat Completions endpoints at $5 per million input tokens and $30 per million output tokens, double the unit price of GPT-5.4.
LLaVA-1.5 is an open-source large multimodal model released in October 2023 by researchers at the University of Wisconsin-Madison and Microsoft Research. It builds on the original LLaVA architecture by introducing targeted refinements: switching the vision encoder to CLIP-ViT-L at 336-pixel resolution, replacing the projection layer with a two-layer MLP, and adding academic-task-oriented visual question answering data with response formatting prompts during training. These modifications achieve state-of-the-art performance across 11 benchmarks at release, with training completing in approximately one day on a single 8-A100 node.
The model accepts an image paired with a text prompt and generates natural language responses, supporting visual question answering, image captioning, and open-ended visual conversation. LLaVA-1.5 is available in 7B and 13B parameter variants built on the Vicuna language model, and is distributed under the Llama 2 Community License due to its Llama-2-based foundation. The original LLaVA paper was presented as an oral at NeurIPS 2023. Subsequent releases in the series (LLaVA-NeXT (LLaVA-1.6), LLaVA-NeXT-Video, and LLaVA-OneVision) are separate models with their own release pages and build on this foundation with expanded OCR, video, and multi-image capabilities.
GPT-5.5 vs LLaVA-1.5 Comparison Table
| Property | GPT-5.5 | LLaVA-1.5 |
|---|---|---|
| Organization | OpenAI | Microsoft |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Apr 2026 | Oct 2023 |
| Context Window | 1.0M | — |
| Parameters | 7B, 13B | |
| License | Proprietary | Custom |
| Pricing per 1M tokens | ||
| Input $/1M | $5.00 | |
| Output $/1M | $30.00 | |
| Vision Tasks | ||
| Vision Language | ||
| Visual Question Answering | Demo | |
| Captioning | Demo | |
| Classification | Demo | |
| Object Detection | Demo | |
| OCR | Demo | |
| Model Features | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalspass/fail results · 67 prompts Score key:≥75%40–74%<40% | ||
| Overall Score | 77.61% | |
| Avg Response Time | 30.12s | |
| Median input tokensincl. image tokens | 1.4K | |
| Median output tokens | 138 | |
| Est. cost / taskon this benchmark | $0.011 | |
| Defect Detection | 86.7%(13/15) | |
| Document Understanding | 88.9%(8/9) | |
| Object Counting | 30%(3/10) | |
| Object Understanding | 92.9%(13/14) | |
| Spatial Understanding | 78.9%(15/19) | |
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