개발자

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

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

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

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

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

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

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

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.

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.

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.