Kimi-VL-A3B-Thinking - AI Vision Models Tool

Overview

Kimi-VL-A3B-Thinking is an open-source Mixture-of-Experts (MoE) vision-language model designed for long-context multimodal reasoning. The model targets tasks that require extended memory and stepwise reasoning — for example, image-and-video comprehension over long temporal spans, OCR on long documents, mathematical problem solving with chain-of-thought, and sustained multi-turn agent interactions. According to the Hugging Face model card, Kimi-VL-A3B-Thinking provides a 128K token context window while activating only ~2.8B LLM parameters at inference, enabling long-context processing with reduced active compute compared to a dense 16B model. The project is released under an MIT license and is built on top of moonshotai/Kimi-VL-A3B-Instruct (see the model page for lineage and weights). The public model page also reports community adoption metrics (downloads and likes) and exposes the model as an image-text-to-text pipeline on Hugging Face, making it straightforward to prototype multimodal prompts that combine images (or video frames) and long text contexts. For implementation details and the latest updates, refer to the official Hugging Face repository and model card.

Model Statistics

• Downloads: 20,995
• Likes: 444
• Pipeline: image-text-to-text
• Parameters: 16.4B

License: mit

Model Details

Architecture and parameterization: Kimi-VL-A3B-Thinking is a mixture-of-experts (MoE) vision-language model. The model card lists a total parameter footprint of 16.4B parameters (including expert weights), but only ~2.8B parameters are typically activated per forward pass thanks to MoE routing. This design reduces active compute and memory for long-context inference while retaining a large capacity ensemble of experts. Context and modalities: The model supports an extended 128K token context window for text, enabling long-document and long-dialogue reasoning. Input modalities advertised include images and video (frame sequences), plus OCR-style document inputs. The pipeline is published on Hugging Face as an image-text-to-text model, indicating end-to-end multimodal input handling and natural language outputs. Capabilities: Kimi-VL-A3B-Thinking emphasizes extended chain-of-thought reasoning, multi-step mathematical reasoning, OCR extraction on long documents, and multi-turn agent workflows (maintaining context across many turns). It inherits instruction-tuned behaviors from the moonshotai/Kimi-VL-A3B-Instruct base. Compatibility and license: The model is available under an MIT license on Hugging Face. It can be used via the Hugging Face Hub and standard inference tooling (pipelines and Transformers-compatible loading), though production integration (device mapping, batching, and accelerated inference) will depend on your runtime and hardware.

Key Features

• 128K token context window for long documents and dialogs
• Mixture-of-Experts design: ~2.8B activated parameters per inference
• Total model footprint: 16.4B parameters (expert ensemble)
• Multimodal image and video understanding via image-text-to-text pipeline
• Extended chain-of-thought reasoning for math and stepwise tasks
• OCR-capable document comprehension and line-item extraction
• Supports multi-turn agent flows, preserving long dialogue context

Example Usage

Example (python):

from transformers import pipeline

# Load the multimodal pipeline exposed by the model on Hugging Face
pipe = pipeline(
    task="image-text-to-text",
    model="moonshotai/Kimi-VL-A3B-Thinking"
)

# Example: short image + long text context prompt
image_path = "./photo_of_receipt.jpg"
long_context = """
Here is the full scanned receipt text and prior dialog history. Please extract line items, quantities, prices, compute totals, and explain calculations step-by-step.
[PASTE LONG OCR OR HISTORY HERE]
"""

prompt = long_context + "\n
Refer to the image and produce an itemized list with totals and chain-of-thought reasoning."

# Inference (returns model-generated text describing the image + reasoning)
result = pipe({"image": image_path, "prompt": prompt})
print(result)

# For video/frame-based workflows, supply a list of frames (paths or PIL images)
# frames = ["frame1.jpg", "frame2.jpg", ...]
# result = pipe({"image": frames, "prompt": "Summarize the events across frames with timestamps."})

Benchmarks

Hugging Face downloads: 20,995 (Source: https://huggingface.co/moonshotai/Kimi-VL-A3B-Thinking)

Hugging Face likes: 444 (Source: https://huggingface.co/moonshotai/Kimi-VL-A3B-Thinking)

Total parameter count: 16.4B (Source: https://huggingface.co/moonshotai/Kimi-VL-A3B-Thinking)

Activated parameters (per inference): ≈2.8B (Source: https://huggingface.co/moonshotai/Kimi-VL-A3B-Thinking)

Context window: 128K tokens (Source: https://huggingface.co/moonshotai/Kimi-VL-A3B-Thinking)

Serving pipeline type: image-text-to-text (Source: https://huggingface.co/moonshotai/Kimi-VL-A3B-Thinking)

Key Information

• Category: Vision Models
• Type: AI Vision Models Tool