> [!IMPORTANT] > 🌟 **The core work on this project is led by Glint Lab.** > For an interactive online experience, visit **ve2s.ai**. LLaVA-OneVision-1.5 is a fully open-source family of large multimodal models (LMMs) built to democratize multimodal training. Trained on native‑resolution images, it delivers state‑of‑the‑art performance at substantially lower cost. The project also releases high‑quality pretraining and SFT data, a complete and efficient training framework with recipes and configs, and comprehensive logs to support transparent, reproducible research. - The model leads on multiple multimodal benchmarks and generally surpasses Qwen2.5-VL. - Training on native-resolution images significantly improves its visual understanding. Here we show a code snippet to show you how to use the chat model with `transformers` and `qwen_vl_utils`:

Visual QA

Input Text · Image→ Output Text

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llava-onevision-qwen2-0.5b-ov

lmms-lab

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

Text generation

Input Text→ Output Text

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llava-onevision-qwen2-72b-ov-sft

lmms-lab

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Text generation

Input Text→ Output Text

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llava-onevision-qwen2-7b-ov

lmms-lab

Play with the model on the LLaVA OneVision Chat. 1. Model Summary 2. Use 3. Limitations 4. Training 5. License 6. Citation The LLaVA-OneVision models are 0.5/7/72B parameter models trained on LLaVA-OneVision, based on Qwen2 language model with a context window of 32K tokens.

Text generation

Input Text→ Output Text

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LLaVA-Video-7B-Qwen2

lmms-lab

The LLaVA-Video models are 7/72B parameter models trained on LLaVA-Video-178K and LLaVA-OneVision Dataset, based on Qwen2 language model with a context window of 32K tokens. The model was trained on LLaVA-Video-178K and LLaVA-OneVision Dataset, having the ability to interact with images, multi-image and videos, but specific to videos. We provide the simple generation process for using our model. For more details, you could refer to Github.

Category pending

Input Not verified→ Output Not verified

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LongVA-7B

lmms-lab

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Text generation

Input Text→ Output Text

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Qwen2-VL-2B-GRPO-8k

lmms-lab

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Category pending

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COST EXPLORER

Great models. Clear costs.

Cost analysis

Adjust monthly token usage to compare estimated API costs.

Monthly input tokens 10 MMonthly output tokens 2 M1 M = 1 million tokens · USD
Compares base text-token rates within the same channel

Uses collected, comparable USD-per-million-token rates. Snapshots exclude cache discounts, tiers, extra reasoning output and taxes. Check current source prices.

MODEL HANDBOOK

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Selection guide & FAQ

How should API prices be compared?+

Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.

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Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.

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