Codesota · Models2,268 models indexed · 49 match filter
Editorial · Models
Models with recorded evidence.
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Vendor:Areas overviewSpeakLeash · 263Alibaba · 104Google · 102OpenAI · 86Meta · 68Microsoft · 49Anthropic · 44DeepSeek · 34Mistral · 30mistralai · 19CYFRAGOVPL · 14NVIDIA · 14Zhipu AI · 13internlm · 10xAI · 10ByteDance · 9Baidu · 8ibm-granite · 8PLLuM · 8allenai · 7Amazon · 7MiniMax · 7Mistral AI · 7Remek · 7Shanghai AI Lab · 7utter-project · 7CohereForAI · 6Salesforce · 601-ai · 5Cohere · 5Moonshot AI · 5NousResearch · 5THUML · 5gguf-iq · 4IBM · 4Meituan · 4openchat · 4Stanford · 4THUDM · 4tiiuae · 4UC San Diego · 4VikParuchuri · 4Allen AI · 3BAAI · 3Du et al. · 3ForgeCode · 3Fudan University · 3gguf · 3gguf11bv30 · 3gguf7bv30 · 3IDEA Research · 3Liao et al. · 3Moonshot.AI · 3Nam Tuan Ly / NII · 3OpenDataLab · 3OPI-PG · 3upstage · 3ViCoS Lab Ljubljana · 3Xiaomi · 3Zhao et al. · 3+ 243 smaller vendors (288 models)
§ 01 · Microsoft models
49 models from Microsoft · page 1 of 1.
| # | Model | Vendor | Parameters | Architecture | Benchmarks | Results |
|---|---|---|---|---|---|---|
| 001 | Faster R-CNN | Microsoft | Unknown | Unknown | 4 | 19 |
| 002 | microsoft/Phi-4-mini-instruct | Microsoft | 3.84B | — | 3 | 17 |
| 003 | Phi-4 | Microsoft | 14B | transformer | 3 | 17 |
| 004 | microsoft/Phi-3.5-mini-instruct | Microsoft | 3.82B | — | 2 | 16 |
| 005 | microsoft/phi-4 | Microsoft | 14.7B | — | 3 | 16 |
| 006 | Phi-3-medium-4k-instruct | Microsoft | — | — | 2 | 12 |
| 007 | Phi-3-mini-4k-instruct | Microsoft | — | — | 2 | 12 |
| 008 | Phi-3.5-MoE-instruct | Microsoft | — | — | 1 | 11 |
| 009 | Phi-3-small-8k-instruct | Microsoft | — | — | 1 | 11 |
| 010 | Phi-4 Multimodal Instruct | Microsoft | 6B | Phi-4 multimodal | 8 | 9 |
| 011 | 🚧WizardLM-2-7B | Microsoft | — | — | 1 | 9 |
| 012 | WizardLM-2-8x22b | Microsoft | — | — | 1 | 7 |
| 013 | DeBERTa-v3-large | Microsoft | 304M | DeBERTa-v3-large | 5 | 6 |
| 014 | E5-Mistral-7B-instruct | Microsoft | 7B | Mistral-7B (LLM-based embedding) | 3 | 4 |
| 015 | ResNet-152 | Microsoft | 60M | CNN | 2 | 3 |
| 016 | ResNet-50 | Microsoft | 25M | CNN | 3 | 3 |
| 017 | UniXcoder | Microsoft | Unknown | Transformer encoder-decoder | 3 | 3 |
| 018 | CodeBERT | Microsoft | Unknown | BERT | 2 | 2 |
| 019 | Faster R-CNN (VGG-16) | Microsoft | ~137M | Two-stage detector: RPN + Fast R-CNN with VGG-16 backbone | 1 | 2 |
| 020 | Florence-2-Large | Microsoft | — | — | 1 | 2 |
| 021 | KOSMOS-2.5 | Microsoft | — | — | 1 | 2 |
| 022 | LightGBM | Microsoft | — | Gradient Boosted Trees (leaf-wise) | 2 | 2 |
| 023 | RAD-DINO | Microsoft | — | Self-supervised ViT | 2 | 2 |
| 024 | VALL-E 2 | Microsoft | Unknown | Neural codec language model (EnCodec tokens) | 2 | 2 |
| 025 | Azure Document Intelligence | Microsoft | Unknown | Managed layout + OCR extraction service | 1 | 1 |
| 026 | Azure OCR | Microsoft | — | Cloud OCR Service | 1 | 1 |
| 027 | BEiT-3 (ViT-L) | Microsoft | Unknown | Multiway Transformer (ViT-L/14) | 1 | 1 |
| 028 | BioViL | Microsoft | — | Vision-Language Transformer | 1 | 1 |
| 029 | CodeBERT | Microsoft | — | BERT pretrained on code + NL | 1 | 1 |
| 030 | DeBERTa (ensemble) | Microsoft | — | — | 1 | 1 |
| 031 | DiT-Base | Microsoft | — | Vision Transformer (self-supervised) | 1 | 1 |
| 032 | DiT-Large | Microsoft | Unknown | Document Image Transformer Large | 1 | 1 |
| 033 | DiT-L (Cascade R-CNN) | Microsoft | Unknown | Document Image Transformer (BEiT-based) + Cascade R-CNN detection head | 1 | 1 |
| 034 | GraphCodeBERT | Microsoft | 125M | transformer | 1 | 1 |
| 035 | LayoutLMv3 | Microsoft | Unknown | Multimodal Transformer (text + layout + image) | 1 | 1 |
| 036 | LayoutLMv3-Large | Microsoft | Unknown | Multimodal Transformer (text + layout + image unified pre-training) | 1 | 1 |
| 037 | mDeBERTa-v3-base | Microsoft | 86M | DeBERTa-v3 (multilingual) | 1 | 1 |
| 038 | NaturalSpeech 3 | Microsoft | ~500M | Factorized codec + non-AR diffusion | 1 | 1 |
| 039 | NaturalSpeech 3 | Microsoft | Unknown | Discrete diffusion + codec LM (factored) | 1 | 1 |
| 040 | Pengi | Microsoft | ~300M | CLAP audio encoder + GPT-2 decoder | 1 | 1 |
| 041 | Phi-4 14B | Microsoft | 14B | — | 1 | 1 |
| 042 | swin_large.ms_in22k_ft_in1k | Microsoft | — | Swin-L, IN22K pre-train, IN1K fine-tune | 1 | 1 |
| 043 | Swin-L (Cascade R-CNN) | Microsoft | — | — | 1 | 1 |
| 044 | Swin-L + UperNet | Microsoft | Unknown | Swin Transformer Large backbone + UperNet head | 1 | 1 |
| 045 | Swin Transformer Large | Microsoft | 197M | Hierarchical Vision Transformer | 1 | 1 |
| 046 | Swin Transformer V2 Large | Microsoft | 197M | Hierarchical Vision Transformer | 1 | 1 |
| 047 | UFO (GPT-4V) | Microsoft | Unknown | UI-Focused agent with dual-agent architecture on GPT-4V | 1 | 1 |
| 048 | VALL-E | Microsoft | ~400M | Neural codec LM (EnCodec tokens) | 1 | 1 |
| 049 | WavLM Large (SV) | Microsoft | 316M | WavLM Large + ECAPA-TDNN head | 1 | 1 |