InternVL3-78B

Name: InternVL3-78B
Author: OpenGVLab

by OpenGVLab

78B flagship multimodal LLM for image, video, and document understanding

450Kdl/month

78Bparams

HuggingFace Run it on your own data, free

Identifiers

Model ID

OpenGVLab/InternVL3-78B

Feature URI

mixpeek://image_extractor@v1/opengvlab_internvl3_78b_v1

Overview

InternVL3-78B is OpenGVLab's flagship open-source multimodal LLM, scaling the InternVL3 architecture to 78B parameters for state-of-the-art performance across image understanding, video comprehension, document analysis, and chart interpretation.

InternVL3-78B achieves top results among open-source MLLMs on general multimodal benchmarks, reasoning tasks, and agentic evaluations. On Mixpeek, it serves as the highest-quality option for scene description, visual Q&A, and structured extraction from complex visual content where accuracy matters more than latency.

Architecture

InternViT-6B vision encoder + InternLM3-78B language model with dynamic resolution support. 78B total parameters. Processes images at up to 4K resolution with tile-based encoding. Supports interleaved image-text and multi-frame video input.

Mixpeek SDK Integration

// No extractor parameter takes a Hugging Face model id (checked against
// GET /v1/discovery/extractors, which returns 13), so InternVL3-78B runs
// on your side and the output is upserted through POST
// /v1/namespaces/{namespace_id}/documents/upsert. On Enterprise the other
// path is to upload the weights instead: POST /v1/namespaces/{id}/models
// accepts the huggingface format and a custom plugin loads them.
const res = await fetch(
  "https://api.mixpeek.com/v1/namespaces/ns_your_namespace/documents/upsert",
  {
    method: "POST",
    headers: {
      Authorization: "Bearer API_KEY",
      "Content-Type": "application/json",
    },
    body: JSON.stringify({
      collection_id: "col_your_collection",
      documents: [
        {
          document_id: "asset-00412",
          // The model produces text, so it lands in payload. Give the
          // collection a text vector index and embed that text to make it
          // searchable rather than only filterable.
          payload: { extracted_text: modelOutput, source_key: "archive/2026/asset-00412" },
          vectors: { "image-embedding": embeddingOfModelOutput },
        },
      ],
    }),
  },
);

// Managed alternative, if this exact model is not the requirement:
// image_extractor@v1 runs google/siglip-base-patch16-224
// (768-d) over a bucket, with no inference of your own.