Developers

Purpose-built for semantic search, dense retrieval, and RAG. BAAI, 335M.

Mixture-of-experts reasoning powerhouse rivaling closed frontier models on math and code. DeepSeek, 671B.

Compound-scaled CNN delivering state-of-the-art accuracy at a fraction of the parameters. Google, 66M.

Flow-matching transformer for photorealistic text-to-image with strong prompt adherence. Black Forest Labs, 12B.

FLUX distilled to 4 steps — full quality, fraction of the compute. Black Forest Labs, 12B.

Tiny and capable — instruction-tuned for edge and on-device use. Google, 1B.

Largest Gemma 3 base — 140-language reasoning, coding, long context. Google, 27B.

Largest Gemma 3 — 140-language reasoning, coding, long context. Google, 27B.

Punches above its weight in reasoning and code. Google, 4B.

Open GPT-style model for deep reasoning in self-hosted deployments. GPT-OSS, 120B.

Open GPT-style generation at a practical self-hosted scale. GPT-OSS, 20B.

Multilingual instruction following, tool use, and function calling. Meta, 8B.

Charts, image Q&A, visual documents — multimodal on a single accelerator. Meta, 11B.

Instruction-tuned to run anywhere — on-device at 1B. Meta, 1B.

Lightweight agent backbone for low-latency, resource-constrained deployments. Meta, 3B.

Image + text inputs at full scale on Llama's reasoning foundation. Meta, 90B.

Document analysis, chart reading, OCR-level image understanding at full scale. Meta, 90B.

Llama 3.1 refined — better math, code, and multilingual, same 128K context. Meta, 70B.

Stronger structured tasks, tool use, and reasoning than prior Llama generations. Meta, 70B.

Function-calling and instruction following for production and agentic use. Mistral AI, 7B.

Inverted residuals and linear bottlenecks — strong accuracy at near-zero inference cost. Google, 3.4M.

Text-to-video focused on motion quality and temporal coherence. Genmo, 10B.

Preview text-to-video from natural language prompts. Motif, 6B.

Instruction-tuned across Chinese, English, coding, and structured output at scale. Alibaba, 72B.

Tuned for code, math, and structured output at an efficient scale. Alibaba, 7B.

Document understanding, chart analysis, and multi-image Q&A. Alibaba, 32B.

Vision-language for low-latency multimodal deployment on constrained hardware. Alibaba, 3B.

Vision-language at scale — documents, charts, and scene understanding. Alibaba, 72B.

Visual Q&A, OCR, and document parsing in a practical VLM footprint. Alibaba, 7B.

Toggleable chain-of-thought for on-demand deep reasoning. Alibaba, 32B.

Reasoning depth on demand without a throughput penalty. Alibaba, 8B.

Multilingual embeddings for retrieval and semantic similarity. Alibaba, 4B.

Higher-capacity multilingual embeddings for retrieval and reranking. Alibaba, 8B.

Chain-of-thought with self-reflection for math, science, and logic. Alibaba, 32B.

Skip connections that made deep networks trainable — the image classification baseline. Microsoft Research, 25M.

Dual-encoder base for high-resolution synthesis and fine-tuning pipelines. Stability AI, 6.6B.

Mix-transformer segmentation without positional encoding — accurate at low compute. NVIDIA, 3.8M.

Unified encoder-decoder for natural text-to-speech synthesis. Microsoft, 307M.

MMDiT architecture — strong text rendering and prompt control for image generation. Stability AI, 8B.

Skip connections preserve spatial detail for precise segmentation — VGG backbone. 31M.

Image patches + self-attention, no convolutions — the original vision transformer. Google, 86M.

One-Shot Aggregation avoids DenseNet's redundant paths for better accuracy-per-FLOP. 11.2M.

Causal video transformer for text-to-video with strong motion coherence. Alibaba, 14B.

99 languages, 680K hours of training audio — built for robust speech recognition. OpenAI, 1.5B.