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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

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 Sonnet 4.6 vs LLaVA-1.5: Overview

Claude Sonnet 4.6

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

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

PropertyClaude Sonnet 4.6LLaVA-1.5
OrganizationAnthropicMicrosoft
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateFeb 2026Oct 2023
Context Window1.0M
Parameters7B, 13B
LicenseProprietaryCustom
Pricing per 1M tokens
Input $/1M$3.00
Output $/1M$15.00
Vision Tasks
Vision Language
Visual Question AnsweringDemo
CaptioningDemo
ClassificationDemo
Object DetectionDemo
OCRDemo
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 Time4.24s
Median input tokensincl. image tokens2.2K
Median output tokens105
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 Time3.42s
Median input tokensincl. image tokens736
Median output tokens85
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