OPT

OPT

Meta
+
+

Related Products

  • Google AI Studio
    30 Ratings
    Visit Website
  • Gemini Enterprise Agent Platform
    999 Ratings
    Visit Website
  • LTX
    182 Ratings
    Visit Website
  • LM-Kit.NET
    29 Ratings
    Visit Website
  • RaimaDB
    12 Ratings
    Visit Website
  • ClickLearn
    67 Ratings
    Visit Website
  • Dragonfly
    16 Ratings
    Visit Website
  • Runpod
    230 Ratings
    Visit Website
  • Qloo
    23 Ratings
    Visit Website
  • PackageX OCR Scanning
    48 Ratings
    Visit Website

About

Large language models, which are often trained for hundreds of thousands of compute days, have shown remarkable capabilities for zero- and few-shot learning. Given their computational cost, these models are difficult to replicate without significant capital. For the few that are available through APIs, no access is granted to the full model weights, making them difficult to study. We present Open Pre-trained Transformers (OPT), a suite of decoder-only pre-trained transformers ranging from 125M to 175B parameters, which we aim to fully and responsibly share with interested researchers. We show that OPT-175B is comparable to GPT-3, while requiring only 1/7th the carbon footprint to develop. We are also releasing our logbook detailing the infrastructure challenges we faced, along with code for experimenting with all of the released models.

About

Qwen3.8-Flash-Next is an open-weight multimodal Mixture-of-Experts model and an early preview of the architecture planned for Qwen4. It systematically upgrades attention, residual connections, embeddings, and optimization to improve capability, computational efficiency, model capacity, and training stability. Its hybrid architecture combines Gated DeltaNet, which efficiently compresses historical information, with Qwen Sparse Attention, which selects important context at the micro-block level to reduce attention and indexing costs on long sequences. Gated Residual widens the residual stream into four branches and dynamically controls information flow across layers, while N-gram Embedding adds large-scale local-pattern memory with very little extra per-token computation and can be offloaded to host memory. The model uses a 125B-parameter main network plus 51B N-gram embedding parameters, while activating only 6B parameters per token.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

AI developers interested in a large language model

Audience

Developers, researchers, and AI teams seeking to run or study an efficient multimodal open-weight model with long-context reasoning, coding, multilingual, and agentic capabilities

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

No images available

Screenshots and Videos

Pricing

No information available.
Free Version
Free Trial

Pricing

$2 per 1M (input)
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Meta
Founded: 2004
United States
www.meta.com

Company Information

Alibaba
Founded: 1999
China
qwen.ai/blog

Alternatives

Alternatives

Grok 4.6

Grok 4.6

SpaceXAI
T5

T5

Google
Claude Opus 5

Claude Opus 5

Anthropic
CodeQwen

CodeQwen

Alibaba
Claude Fable 5

Claude Fable 5

Anthropic
PanGu-α

PanGu-α

Huawei
Qwen3.5

Qwen3.5

Alibaba
Llama 2

Llama 2

Meta

Categories

Categories

Integrations

Alibaba Cloud
Alibaba Cloud Model Studio
Cherry Studio
Cline
ClinePass
Hermes Agent
Hugging Face
Model Context Protocol (MCP)
ModelScope
Novita AI
Odysseus
OfoxAI
Ollama
OpenClaw
Python
Qwen
Qwen Code
Qwen Studio
QwenCloud
QwenWork

Integrations

Alibaba Cloud
Alibaba Cloud Model Studio
Cherry Studio
Cline
ClinePass
Hermes Agent
Hugging Face
Model Context Protocol (MCP)
ModelScope
Novita AI
Odysseus
OfoxAI
Ollama
OpenClaw
Python
Qwen
Qwen Code
Qwen Studio
QwenCloud
QwenWork
Claim OPT and update features and information
Claim OPT and update features and information
Claim Qwen3.8-Flash-Next and update features and information
Claim Qwen3.8-Flash-Next and update features and information