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.
Qwen3.5-122B-A10B is a large Mixture-of-Experts model from Alibaba's Qwen3.5 series with 122B total parameters and 10B activated per token. It features a 262K token context window (extensible to 1M with YaRN), thinking/reasoning mode, tool calling, and support for 201 languages. Excels at complex reasoning, coding, multimodal understanding, and agentic tasks with the efficiency of sparse activation.
The MiniMax-M2.7 model is a next-generation, high-speed text model built on a sparse Mixture-of-Experts (MoE) architecture. It stands out as a pioneering Self-Evolving AI, utilizing continuous recursive self-improvement pipelines during development. Engineered explicitly for advanced software engineering and complex agentic workflows, it delivers exceptional generation speeds and high-fidelity output for enterprise multi-agent networks, debugging, and productivity automation.
Qwen3.8-27B is the compact member of Alibaba's most capable open model generation, and it makes an unusual argument: that a dense 27-billion-parameter model can compete with systems a hundred times its size on the work most applications actually do. It is a native vision-language model reading images and hour-scale video, with a hybrid attention stack that mixes gated linear attention with full attention across 64 layers, and a 262K native context extensible far beyond it. Thinking is controllable along three separate axes — on or off, three depth levels, and whether reasoning carries forward across turns. It leads its class on document intelligence and structured extraction, and on DEVUP AI it is the choice when the work is high-volume and the documents are real.
A 0.6B-parameter multilingual text-to-speech model with zero-shot voice cloning, supporting 11 languages.
Qwen3.8-2.4T-A95B is the first Qwen-Max-class model released as open weights: a sparse Mixture-of-Experts design with 2.4 trillion total parameters, of which 95 billion activate per token, routing through 10 of 512 experts plus a shared one. Its 92-layer backbone alternates gated linear attention with full attention every fourth block, which is what keeps inference cost near that of a far smaller model while sustaining a 262K context. It is built for the hardest end of the workload spectrum — long-horizon agentic execution, professional research, and repository-scale engineering — and it always thinks before it answers, with three tunable depth levels. Text in, text out, with no mode that skips deliberation.
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.
Qwen3.8-Max is Alibaba's flagship model and the complete form of the Qwen3.8 generation: a Mixture-of-Experts design with 2.4 trillion total parameters, 95 billion of which activate per token, running against a one-million-token context window with native text, image, and video input in a single endpoint. It reads PDFs of two hundred pages and more, and it uses vision throughout execution rather than only at the start — inspecting its own intermediate output, catching a misaligned interface or a wrong-facing object, and revising the plan. Thinking is hybrid: on by default, switchable off per request, and tunable across three depth levels, with reasoning traces that carry across turns so long agent loops keep their chain of thought.
The model prioritizes token efficiency and agentic inference at production scale, stretching what developers can achieve within limited token, latency, and serving-cost budgets.
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.
The openai/gpt-oss-120b is a highly efficient, open-weights Mixture-of-Experts (MoE) language model released by OpenAI under the permissive Apache 2.0 license. While it features 117 billion total parameters, it activates only 5.1 billion parameters per token, allowing it to run entirely on a single 80GB GPU. Designed specifically for advanced AI agents, it features configurable reasoning efforts (low, medium, high), transparent Chain-of-Thought (CoT), and native optimization for tool use and structured data generation.
