Open Source AI Models by Category: LLMs, Vision, Embeddings, Speech, and Image (2026)

TL;DR

Published September 1, 2026. Every license, size, and context figure below was read from the model card or license file on that date.

Open source AI models are models whose weights you can download and run. The label covers five categories that matter in production, and in each one the best open model now ships under MIT or Apache 2.0 with no strings, or under a custom license whose conditions bind only inference resellers. The open LLMs sit six points under the closed frontier on the Artificial Analysis Intelligence Index (60 vs 66). The open speech, embedding, and image models are the default choices in their categories, not the budget option.

Two sentences on the fine print, then the models. Almost none of these meet the OSI Open Source AI Definition, because none publish training data; the license text on the checkpoint is what governs what you can do. The full license table, the open-source vs open-weight breakdown, and the API vs self-host vs dedicated cost crossover live on the open source LLM guide.

Coding and General LLMs

The largest category and the one with independent scoring. Artificial Analysis runs every model through one harness; the scores below are its Intelligence Index on September 1, 2026, with the closed leader (Claude Fable 5.1) at 66 for reference.

The four highlighted rows are served on Morph today. The table has one structural fact in it: every frontier-class open LLM is a mixture of experts with 13B to 104B active parameters, which is why 284B and 320B models serve at $0.278 and $0.45 per million output tokens. Qwen 3.8 27B is the exception, a dense model that fits one GPU and scores level with DeepSeek V4 Flash. Per-model pages: Kimi K3, GLM-5.3, GLM-5.3-Flash, DeepSeek V4 Flash, DeepSeek V4, gpt-oss. Ranked lists: best open source LLM and best open source coding model.

Vision-Language Models

A vision-language model takes images (and sometimes video) alongside text and answers in text. In 2026 the line between this category and the LLM category has mostly dissolved: three of the strongest open LLMs are natively multimodal, and the dedicated VLM families (Qwen3-VL) share an architecture with their text-only siblings.

For coding agents the useful VLM is the one that reads a screenshot of the rendered UI and edits the component. GLM-5.3-Flash is the first natively multimodal model in the GLM-5 series and is served on Morph with image input; Kimi K3's card lists video input as well. Qwen3-VL is the choice when you need Apache 2.0 and a vision model you can fine-tune. Gemma 4 31B is the single-GPU option. See MiniMax M3 for the video-input case.

Embedding and Reranking Models

Embedding models turn text into vectors for retrieval; rerankers score a query against candidate passages. This is the category where open models won outright: the top entries are small, permissively licensed, and run on CPU or a fraction of one GPU.

Pick by input length and dimension budget. Qwen3-Embedding-8B handles 32K tokens per input and lets you shrink the vector to 32 dimensions for cheap indexes. bge-m3 is the long-running multilingual default at 1024 dimensions and 8,192 tokens. nomic-embed v2 is the smallest and caps at 512 tokens, so it fits short-passage retrieval. All three include code among their languages. For local use, Ollama embedding models covers the quantized builds.

Speech: Transcription and Text to Speech

Two directions. Automatic speech recognition (audio to text) has two open families that set the reference points on the Hugging Face Open ASR Leaderboard. Text to speech now has open models under Apache 2.0 and MIT that are small enough to run per request.

Whisper large-v3 is the model to reach for when language coverage matters. Parakeet TDT 0.6B v3 is the model to reach for when throughput matters: a real-time factor above 3,000 means one GPU transcribes an hour of audio in about a second, and it handles files up to 24 minutes with full attention or 3 hours with local attention. On the output side, Kokoro is 82M parameters, about one twentieth the size of Whisper large-v3; Chatterbox Multilingual V3 is a 0.5B general-purpose model under MIT covering 23 languages.

Image Generation

The image category has the widest license spread, and the license, not the sample quality, is usually what decides. Two of the four leading models are Apache 2.0; the other two carry conditions that exclude most commercial products.

FLUX.1-schnell is the fastest path to a shippable image feature: Apache 2.0, 1 to 4 sampling steps, 12B parameters. Qwen-Image is the larger Apache 2.0 alternative at 20B parameters on an MMDiT architecture. FLUX.1-dev shares schnell's 12B architecture without the step distillation, but its license bars commercial use of the model itself. Stable Diffusion 3.5 Large is free only while your organization earns under $1M a year.

Serving Them: One API First, Then Dedicated

Downloading a checkpoint is the easy part. The categories above split into two serving situations.

LLMs and VLMs: call them today

Morph serves four of the models above on one OpenAI-compatible endpoint at the full 1M context, with prefix caching on by default, a standby tier at 50% off, and an OpenAI-compatible Batch API at the same rate. GLM-5.3-Flash is the multimodal entry in the lineup and takes image input through the standard chat-completions message format.

from openai import OpenAI

client = OpenAI(base_url="https://api.morphllm.com/v1", api_key="YOUR_MORPH_API_KEY")

resp = client.chat.completions.create(
    model="morph-glm53flash",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "The header overlaps the nav on mobile. Fix the CSS."},
            {"type": "image_url", "image_url": {"url": "data:image/png;base64,..."}},
        ],
    }],
)
print(resp.choices[0].message.content)

Everything else: the open serving stacks, or dedicated capacity

Embedding, speech, and image models do not speak chat completions, and each has a mature open stack: Text Embeddings Inference or vLLM for embedding models, NVIDIA NeMo for Parakeet and faster-whisper for Whisper, diffusers for FLUX.1 and Qwen-Image. Run them on your own GPUs when volume is steady and the data is sensitive, or on an API when it is not. When you need reserved, isolated capacity behind a single endpoint, dedicated inference provisions B200 or B300 GPUs billed by the minute, with the calculator comparing the monthly cost against per-token spend. The self-serve catalog covers the LLM lineup; for a VLM, embedding, or speech model on dedicated hardware, talk to us.

FAQ

What are open source AI models?

Models whose trained weights are published for download, across language, vision, embedding, speech, and image tasks. Nearly all are open-weight rather than OSI open source, since the training data stays private; the license file on the checkpoint governs use.

Which open source AI model is best for each category?

LLMs: Kimi K3 and GLM-5.3 (Intelligence Index 60), GLM-5.3-Flash for cost (57, MIT). Vision: Qwen3-VL-235B (Apache 2.0) or GLM-5.3-Flash. Embeddings: Qwen3-Embedding-8B or bge-m3. Speech to text: Whisper large-v3 for coverage, Parakeet TDT 0.6B v3 for throughput. Text to speech: Kokoro or Chatterbox. Image: FLUX.1-schnell or Qwen-Image, both Apache 2.0.

Can I use them commercially?

Apache 2.0 and MIT models, yes, with no conditions. Kimi K3 and GLM-5.3 attach revenue thresholds that bind inference resellers. FLUX.1-dev is non-commercial. Stable Diffusion 3.5 is free only under $1M annual revenue. The license table has the clauses.

Where do I download them?

Hugging Face, under each developer's organization; the Sources list below links every model card. For quantized local builds of the smaller LLMs, see best Ollama models.

How do I serve them in production?

LLMs and VLMs: an OpenAI-compatible API (Morph serves four at 1M context) or vLLM and SGLang on your own GPUs. Embedding, speech, and image models: their own open serving stacks, or dedicated inference when you need reserved capacity.

Related Resources

• Open Source LLM: Licenses, Models, and One API to Run Them
• Best Open Source LLMs (2026), Ranked
• Best Ollama Models and Ollama Embedding Models
• LLM Inference: Cost, Speed, Caching, and GPU Capacity
• Dedicated Inference and the cost calculator
• Morph open models