LLaVA 1.6 Vicuna 13B

Community
Multimodal

LLaVA 1.6 (LLaVA-NeXT) with a Vicuna-13B language backbone. Open vision-language chat model that describes images, answers questions, reads charts and reasons about scenes. Version 1.6 adds higher input resolution and better OCR and reasoning than LLaVA 1.5.

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Send a single prompt and stream a response inline. Hit Cmd+Enter to submit.
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TL;DR·Last updated June 24, 2026

LLaVA 1.6 Vicuna 13B is multimodal AI model from Community, priced at €0.000 per 1M input tokens with a 4.1K tokens context window.

About this model

LLaVA 1.6, also known as LLaVA-NeXT, connects a CLIP vision encoder to a Vicuna-13B chat model. It takes an image and a free-form prompt and returns conversational answers, making it a flexible option for detailed captioning, visual question answering, chart and document reading, and step-by-step visual reasoning. The 1.6 release improved input resolution and OCR over earlier LLaVA versions.
Try LLaVA 1.6 Vicuna 13B

0.7

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Pricing

Price per Generation
Per generation€2.00

API Integration

Use our OpenAI-compatible API to integrate LLaVA 1.6 Vicuna 13B into your application.

Install
npm install railwail
JavaScript / TypeScript
import railwail from "railwail";

const rw = railwail("YOUR_API_KEY");

// Simple — just pass a string
const reply = await rw.run("llava-1-6-vicuna-13b", "Hello! What can you do?");
console.log(reply);

// With message history
const reply2 = await rw.run("llava-1-6-vicuna-13b", [
  { role: "system", content: "You are a helpful assistant." },
  { role: "user", content: "Explain quantum computing simply." },
]);
console.log(reply2);

// Full response with usage info
const res = await rw.chat("llava-1-6-vicuna-13b", [
  { role: "user", content: "Hello!" },
], { temperature: 0.7, max_tokens: 500 });
console.log(res.choices[0].message.content);
console.log(res.usage);
Specifications
Price
€2.00
Context window
4,096 tokens
Max output
1,024 tokens
Developer
Community
Category
Multimodal
Supported Formats
image
text
Tags
replicate
llava
captioning
vqa
vision-understanding
open-weights
image

Frequently asked questions

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