Model Library
Browse and deploy state-of-the-art AI models through the DEVUP Gateway.
Browse and deploy state-of-the-art AI models through the DEVUP Gateway.
Browse and deploy state-of-the-art AI models through the DEVUP Gateway.
Muse Glimmer 30B is Meta Superintelligence Lab's first open-weight release, and it was designed around a single constraint: an agent that runs on the machine in front of you. Roughly 29.6 billion dense parameters including a dedicated vision encoder, distilled from a larger model, and quantized to fit under 20 GB — small enough to run on one consumer GPU while still holding a 131K context and reading screenshots, charts, and documents. What sets it apart is what it was trained for rather than what it knows: sustained tool use across long workflows, and diagnosing a failed tool call and retrying rather than stopping. It ships with published prompt-injection and privacy evaluations, which few models of any size do. Apache 2.0.
Kimi K3 is Moonshot AI's most capable model and the first open model in the 3-trillion-parameter class. It carries 2.8T total parameters but activates only 104B per token, routing through 16 of 896 experts, and it is built on a new attention design that mixes Kimi Delta Attention with gated latent attention across 93 layers. Vision is native rather than bolted on, with a dedicated encoder inside the model, and the context window runs to a full million tokens. It was built for work that takes a long time to finish: sustained engineering sessions across large repositories, terminal orchestration, deep research, and document-heavy knowledge work. Thinking is always on, with three effort levels, and the model returns its reasoning as a first-class field.
Inkling-Small is a Mixture-of-Experts transformer with 276B total parameters, 12B active, trained on NVIDIA GB300 NVL72 systems. Like Inkling, it features native reasoning over audio and images, variable thinking effort
The google/gemma-4-31B-it-Ultra is a highly capable, multimodal, dense open-weights model developed by Google DeepMind. Featuring 30.7 billion parameters and a massive 256,000-token context window, it excels in advanced reasoning, complex coding, and multimodal tasks (including text, high-resolution images, and video temporal reasoning). Released under the permissive Apache 2.0 license, this "Ultra" designation highlights its optimization for autonomous agent workflows, precise function calling, and structured data generation. When quantized (e.g., 4-bit), it can be comfortably run locally on consumer-grade hardware like a 24GB RTX 4090.
Claude Opus 5 is Anthropic's most advanced Opus model, powering long-running agents while delivering improvements in coding and professional work.
Inkling is a general-purpose multimodal model that accepts text, image and audio inputs and generates text outputs.
Claude Fable 5 is Anthropic's next generation of intelligence for the hardest knowledge work and coding problems. It works independently for longer than any prior generally available Claude model: run it in an agent harness and it can work for days at a time, planning across stages, delegating to sub-agents, and checking its own work.
Claude Sonnet 5 is Anthropic's most capable Sonnet model yet, built for coding, agents, and professional work at scale. It brings near-Opus intelligence to the model teams run at scale every day, with the same balance of capability, cost, and speed teams already rely on Sonnet for.
MiniMax-M3 is a native multimodal model with 1M context. It has ~428B parameters and ~23B activated parameters.
Nemotron Content Safety 3.5 is a multimodal safety classifier developed by NVIDIA. A compact safety model that handles text, images, and custom policies. It outputs a safe/unsafe classification plus a reasoning trace, and can be used as an inference-time guardrail, as a judge for LLM safety testing and evaluation, or with the accompanying training dataset to post-train models for safer behavior.
Built for the Agent era, it delivers stable performance in complex reasoning and long-horizon tasks, including multi-step planning, visual-text reasoning, video understanding, and advanced analysis.
Built for low-latency, high-concurrency, cost-sensitive use cases, with flexible deployment, four-tier thinking, and multimodal
