Developers

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

Multi-lingual, multi-granularity retrieval across dense, sparse, and multi-vector modes. BAAI, 568M.

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

R1 reasoning traces distilled into a Qwen backbone for math and code. DeepSeek, 14B.

Distilled Whisper large-v3 — near-parity transcription at roughly six times the speed. Distil-Whisper, 756M.

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

Commercially licensed generalist trained on 1T tokens of curated web data. TII, 40B.

Commercially licensed instruction model — lean resource footprint. TII, 7B.

Third-generation Falcon instruction model with a 32K context window. TII, 7B.

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.

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

Compact Gemma 3 base — punches above its weight in reasoning and code. Google, 4B.

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

Instruction-tuned Gemma 4 for multilingual reasoning, coding, and long context. Google, 31B.

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.

128K context and multilingual base with a wide fine-tuning ecosystem. Meta, 8B.

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

Image + text inputs on Llama's reasoning foundation, 128K context. 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.

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.

Sliding window and grouped-query attention for fast, memory-lean inference. Mistral AI, 7B.

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

Multimodal small model with vision input and a 128K context window. Mistral AI, 24B.

Sparse MoE: 13B active per token out of 45B total — quality at low per-token cost. Mistral AI, 45B.

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

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

Compact 3.8B instruction model with 128K context tuned for reasoning on constrained hardware. Microsoft, 3.8B.

General-purpose 32B text model with strong multilingual and code coverage. Alibaba, 32B.

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

Code-specialist 32B tuned for program synthesis, repair, and structured output. Alibaba, 32B.

Vision-language base — documents, charts, and multi-image Q&A. Alibaba, 32B.

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 base at scale — documents, charts, and scene understanding. Alibaba, 72B.

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

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

Mid-scale Qwen3.6 for general reasoning, coding, and multilingual generation. Alibaba, 27B.

Compact multilingual embeddings for retrieval on constrained hardware. Alibaba, 0.6B.

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

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

Hybrid thinking + vision — documents, charts, UI, and multi-image tasks. Alibaba, 32B.

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.

Image-to-video generation animating a still frame with coherent motion. Alibaba, 14B active.

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.

Anchor-free detector tuned for real-time inference at minimal parameter cost. Megvii, 0.9M.

Few-step distilled text-to-image generation. Tongyi-MAI.