AI Model Wiki

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    Lesson 2 of 5

    Top Open-Source Models by Category

    From the course Open-Source AI Models: Self-Hosting & Deployment

    Here's a curated guide to the best open-source models across every major AI category as of early 2026.

    Large Language Models

    • Llama 4 (Meta) — The most popular open-source LLM family. Available in Scout (17B active params, 16 experts) and Maverick (17B active, 128 experts) variants. Exceptional multilingual performance, massive 10M token context window, and native multimodal support. Llama License.

    • Mistral Large 2 (Mistral AI) — 123B parameters. Strong at code, reasoning, and multilingual tasks. Competitive with GPT-4 on many benchmarks. Apache 2.0 licensed.

    • Qwen 3 (Alibaba) — Available from 0.6B to 235B parameters. Excellent multilingual support, especially for Asian languages. Standout performance on math and coding benchmarks.

    • DeepSeek V3 (DeepSeek) — 671B parameters with Mixture of Experts (only 37B active). Matches top proprietary models at a fraction of the inference cost. MIT licensed.

    • Gemma 3 (Google) — Available in 1B to 27B sizes. Lightweight but surprisingly capable. Great for on-device deployment and edge computing.

    Image Generation

    • FLUX.2 (Black Forest Labs) — Currently the best open-source image model. Excellent prompt adherence, photorealistic outputs, and fast generation. Available in Dev and Schnell variants.

    • Stable Diffusion 3.5 (Stability AI) — The classic open-source image generator. Massive ecosystem of extensions, LoRAs, and community fine-tunes.

    Audio & Speech

    • Whisper v4 (OpenAI) — Industry-standard speech recognition. Supports 100+ languages with exceptional accuracy. Apache 2.0 licensed.

    • Bark (Suno) — Text-to-speech with realistic voice generation, including music and sound effects.

    Video

    • CogVideoX (Tsinghua) — Open-source text-to-video model producing coherent 6-second clips.

    Choosing the Right Model

    Consider these factors:

    1. Task fit — Does the model excel at your specific use case?
    2. Size vs. hardware — Can your infrastructure run it?
    3. License — Does it allow your commercial use case?
    4. Community — Is there active development and support?
    5. Fine-tuning — Is there a fine-tuning ecosystem (LoRAs, datasets)?

    Key Takeaways

    • •LLMs: Llama 4, Mistral Large 2, Qwen 3, DeepSeek V3, and Gemma 3 lead the pack
    • •Image: FLUX.2 and Stable Diffusion 3.5 dominate open-source image generation
    • •Audio: Whisper v4 is the standard for speech recognition; Bark for text-to-speech
    • •Choose based on task fit, hardware requirements, license, and community support
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