GPT-5.6 Sol vs LLaVA-1.5
Compare GPT-5.6 Sol and LLaVA-1.5 side-by-side.
Compare GPT-5.6 Sol 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-5.6 Sol vs LLaVA-1.5: Overview
GPT-5.6 Sol is the flagship model in OpenAI's GPT-5.6 family, which also includes Terra (a balanced everyday-work tier) and Luna (a fast, cost-efficient tier). Sol is designed for demanding reasoning, long-horizon agentic workflows, software engineering, computer use, scientific research, and cybersecurity tasks. It introduces two new capability modes: a "max" reasoning effort setting that allocates additional compute time for difficult problems, and an "ultra" mode that coordinates multiple subagents in parallel to accelerate complex, multi-step work. The model supports native multimodal input, allowing it to process screenshots, diagrams, charts, documents, and photographs alongside text. A reported context window of approximately 1.5 million tokens enables processing of large codebases, lengthy research documents, and extended agentic sessions.
GPT-5.6 Sol was announced on June 26, 2026, initially in a limited preview for trusted partners, and reached general availability on July 9, 2026. On the Agents' Last Exam benchmark, which evaluates long-running professional workflows across 55 fields, Sol scores 53.6. On Terminal-Bench 2.1, which tests command-line agentic coding workflows, Sol Ultra achieves 91.9%. The model also demonstrates gains in life sciences evaluations, including long-horizon genomics and quantitative biology analyses. OpenAI paired the release with its most extensive safety evaluation to date, combining human red teaming with large-scale automated testing, and classified Sol as High capability in both cybersecurity and biological risk under its Preparedness Framework, though it does not cross the Critical threshold in either category.
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-5.6 Sol vs LLaVA-1.5 Comparison Table
| Property | GPT-5.6 Sol | LLaVA-1.5 |
|---|---|---|
| Organization | OpenAI | Microsoft |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Jul 2026 | Oct 2023 |
| Context Window | 1.5M | — |
| 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 | |
| Chart Question Answering | ||
| classification | Demo | |
| Document Question Answering | ||
| object-detection | Demo | |
| OCR | Demo | |
| Model Features | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||