Claude Sonnet 4.6 vs LLaVA-1.5
Compare Claude Sonnet 4.6 and LLaVA-1.5 side-by-side.
Compare Claude Sonnet 4.6 vs LLaVA-1.5 live
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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 Sonnet 4.6 vs LLaVA-1.5: Overview
Claude Sonnet 4.6 is Anthropic's mid-tier large language model, released February 17, 2026, designed to balance performance, cost, and versatility for professional and developer use. It supports text and vision-based tasks with advanced reasoning, agentic capabilities, and Adaptive Thinking — a mode where the model dynamically scales its internal reasoning depth. A beta context window of up to 1,000,000 tokens (200K standard) enables processing of entire codebases or document collections in a single request. Parameters are undisclosed.
Optimized for coding, computer use, long-context reasoning, agent planning, and knowledge work, Sonnet 4.6 delivers a full generational upgrade over Sonnet 4.5 and approaches Opus 4.5-level performance across many benchmarks at a fraction of the cost. It is the default model on Claude.ai, Claude Cowork, and is available via API and major cloud platforms — making it well suited for production workloads requiring strong reasoning without flagship pricing.
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.
Claude Sonnet 4.6 vs LLaVA-1.5 Comparison Table
| Property | Claude Sonnet 4.6 | LLaVA-1.5 |
|---|---|---|
| Organization | Anthropic | Microsoft |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Feb 2026 | Oct 2023 |
| Context Window | 1.0M | — |
| Parameters | 7B, 13B | |
| License | Proprietary | Custom |
| Pricing per 1M tokens | ||
| Input $/1M | $3.00 | |
| Output $/1M | $15.00 | |
| 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 | ||
| Foundation Vision | ||
Vision Evalspass/fail results · 67 prompts Score key:≥75%40–74%<40% | ||
| Visual Understanding | ||
| Overall Score | 70.15% | |
| Avg Response Time | 4.24s | |
| Median input tokensincl. image tokens | 2.2K | |
| Median output tokens | 105 | |
| Est. cost / taskon this benchmark | $0.0080 | |
| Defect Detection | 80%(12/15) | |
| Document Understanding | 77.8%(7/9) | |
| Object Counting | 30%(3/10) | |
| Object Understanding | 71.4%(10/14) | |
| Spatial Understanding | 78.9%(15/19) | |
| OCR | ||
| Overall Score | 81.66% | |
| Avg Response Time | 3.42s | |
| Median input tokensincl. image tokens | 736 | |
| Median output tokens | 85 | |
| Est. cost / taskon this benchmark | $0.0035 | |
| Focused Scene OCR | 85.9%(85/99) | |
| Handwritten Math | 50%(5/10) | |
| License Plate Recognition | 90%(27/30) | |
| Text Recognition | 86.7%(26/30) | |
| VQA & Extraction | 73.3%(44/60) | |
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