Alternatives to Gemma 3n
Compare Gemma 3n alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to Gemma 3n in 2026. Compare features, ratings, user reviews, pricing, and more from Gemma 3n competitors and alternatives in order to make an informed decision for your business.
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1
Gemma
Google
Gemma is a family of lightweight, state-of-the-art open models built from the same research and technology used to create the Gemini models. Developed by Google DeepMind and other teams across Google, Gemma is inspired by Gemini, and the name reflects the Latin gemma, meaning “precious stone.” Accompanying our model weights, we’re also releasing tools to support developer innovation, foster collaboration, and guide the responsible use of Gemma models. Gemma models share technical and infrastructure components with Gemini, our largest and most capable AI model widely available today. This enables Gemma 2B and 7B to achieve best-in-class performance for their sizes compared to other open models. And Gemma models are capable of running directly on a developer laptop or desktop computer. Notably, Gemma surpasses significantly larger models on key benchmarks while adhering to our rigorous standards for safe and responsible outputs. -
2
Gemma 2
Google
A family of state-of-the-art, light-open models created from the same research and technology that were used to create Gemini models. These models incorporate comprehensive security measures and help ensure responsible and reliable AI solutions through selected data sets and rigorous adjustments. Gemma models achieve exceptional comparative results in their 2B, 7B, 9B, and 27B sizes, even outperforming some larger open models. With Keras 3.0, enjoy seamless compatibility with JAX, TensorFlow, and PyTorch, allowing you to effortlessly choose and change frameworks based on task. Redesigned to deliver outstanding performance and unmatched efficiency, Gemma 2 is optimized for incredibly fast inference on various hardware. The Gemma family of models offers different models that are optimized for specific use cases and adapt to your needs. Gemma models are large text-to-text lightweight language models with a decoder, trained in a huge set of text data, code, and mathematical content. -
3
Gemma 3
Google
Gemma 3, introduced by Google, is a new AI model built on the Gemini 2.0 architecture, designed to offer enhanced performance and versatility. This model is capable of running efficiently on a single GPU or TPU, making it accessible for a wide range of developers and researchers. Gemma 3 focuses on improving natural language understanding, generation, and other AI-driven tasks. By offering scalable, powerful AI capabilities, Gemma 3 aims to advance the development of AI systems across various industries and use cases.Starting Price: Free -
4
PaliGemma 2
Google
PaliGemma 2, the next evolution in tunable vision-language models, builds upon the performant Gemma 2 models, adding the power of vision and making it easier than ever to fine-tune for exceptional performance. With PaliGemma 2, these models can see, understand, and interact with visual input, opening up a world of new possibilities. It offers scalable performance with multiple model sizes (3B, 10B, 28B parameters) and resolutions (224px, 448px, 896px). PaliGemma 2 generates detailed, contextually relevant captions for images, going beyond simple object identification to describe actions, emotions, and the overall narrative of the scene. Our research demonstrates leading performance in chemical formula recognition, music score recognition, spatial reasoning, and chest X-ray report generation, as detailed in the technical report. Upgrading to PaliGemma 2 is a breeze for existing PaliGemma users. -
5
MedGemma
Google DeepMind
MedGemma is a collection of Gemma 3 variants that are trained for performance on medical text and image comprehension. Developers can use MedGemma to accelerate building healthcare-based AI applications. MedGemma currently comes in two variants: a 4B multimodal version and a 27B text-only version. MedGemma 4B utilizes a SigLIP image encoder that has been specifically pre-trained on a variety of de-identified medical data, including chest X-rays, dermatology images, ophthalmology images, and histopathology slides. Its LLM component is trained on a diverse set of medical data, including radiology images, histopathology patches, ophthalmology images, and dermatology images. MedGemma 4B is available in both pre-trained (suffix: -pt) and instruction-tuned (suffix -it) versions. The instruction-tuned version is a better starting point for most applications. -
6
Mistral Small 3.1
Mistral
Mistral Small 3.1 is a state-of-the-art, multimodal, and multilingual AI model released under the Apache 2.0 license. Building upon Mistral Small 3, this enhanced version offers improved text performance, and advanced multimodal understanding, and supports an expanded context window of up to 128,000 tokens. It outperforms comparable models like Gemma 3 and GPT-4o Mini, delivering inference speeds of 150 tokens per second. Designed for versatility, Mistral Small 3.1 excels in tasks such as instruction following, conversational assistance, image understanding, and function calling, making it suitable for both enterprise and consumer-grade AI applications. Its lightweight architecture allows it to run efficiently on a single RTX 4090 or a Mac with 32GB RAM, facilitating on-device deployments. It is available for download on Hugging Face, accessible via Mistral AI's developer playground, and integrated into platforms like Google Cloud Vertex AI, with availability on NVIDIA NIM andStarting Price: Free -
7
EmbeddingGemma
Google
EmbeddingGemma is a 308-million-parameter multilingual text embedding model, lightweight yet powerful, optimized to run entirely on everyday devices such as phones, laptops, and tablets, enabling fast, offline embedding generation that protects user privacy. Built on the Gemma 3 architecture, it supports over 100 languages, processes up to 2,000 input tokens, and leverages Matryoshka Representation Learning (MRL) to offer flexible embedding dimensions (768, 512, 256, or 128) for tailored speed, storage, and precision. Its GPU-and EdgeTPU-accelerated inference delivers embeddings in milliseconds, under 15 ms for 256 tokens on EdgeTPU, while quantization-aware training keeps memory usage under 200 MB without compromising quality. This makes it ideal for real-time, on-device tasks such as semantic search, retrieval-augmented generation (RAG), classification, clustering, and similarity detection, whether for personal file search, mobile chatbots, or custom domain use. -
8
DataGemma
Google
DataGemma represents a pioneering effort by Google to enhance the accuracy and reliability of large language models (LLMs) when dealing with statistical and numerical data. Launched as a set of open models, DataGemma leverages Google's Data Commons, a vast repository of public statistical data—to ground its responses in real-world facts. This initiative employs two innovative approaches: Retrieval Interleaved Generation (RIG) and Retrieval Augmented Generation (RAG). The RIG method integrates real-time data checks during the generation process to ensure factual accuracy, while RAG retrieves relevant information before generating responses, thereby reducing the likelihood of AI hallucinations. By doing so, DataGemma aims to provide users with more trustworthy and factually grounded answers, marking a significant step towards mitigating the issue of misinformation in AI-generated content. -
9
Falcon 2
Technology Innovation Institute (TII)
Falcon 2 11B is an open-source, multilingual, and multimodal AI model, uniquely equipped with vision-to-language capabilities. It surpasses Meta’s Llama 3 8B and delivers performance on par with Google’s Gemma 7B, as independently confirmed by the Hugging Face Leaderboard. Looking ahead, the next phase of development will integrate a 'Mixture of Experts' approach to further enhance Falcon 2’s capabilities, pushing the boundaries of AI innovation.Starting Price: Free -
10
CodeGemma
Google
CodeGemma is a collection of powerful, lightweight models that can perform a variety of coding tasks like fill-in-the-middle code completion, code generation, natural language understanding, mathematical reasoning, and instruction following. CodeGemma has 3 model variants, a 7B pre-trained variant that specializes in code completion and generation from code prefixes and/or suffixes, a 7B instruction-tuned variant for natural language-to-code chat and instruction following; and a state-of-the-art 2B pre-trained variant that provides up to 2x faster code completion. Complete lines, and functions, and even generate entire blocks of code, whether you're working locally or using Google Cloud resources. Trained on 500 billion tokens of primarily English language data from web documents, mathematics, and code, CodeGemma models generate code that's not only more syntactically correct but also semantically meaningful, reducing errors and debugging time. -
11
Gemma
Ceros
Meet Gemma, your new creative AI sidekick. Generate new ideas, optimize existing designs, and automate tedious tasks so you can focus on your creative vision. Ask Gemma for help writing just about anything, from headlines and body text to brand names. Gemma is capable of creating ultra realistic imagery, which can be upscaled and edited. Gemma is online 24/7. An intuitive interface unlocks countless AI models and connects to many creative tools you’re already familiar with. Gemma is programmed to learn from your ideas and preferences and to provide suggestions and insights that you might not have considered before. Easy to install onto your desktop allowing you to take Gemma with you to any file or application. That daunting blank canvas? Conquered. With advanced algorithms, Gemma can power your creative vision. -
12
kluster.ai
kluster.ai
Kluster.ai is a developer-centric AI cloud platform designed to deploy, scale, and fine-tune large language models (LLMs) with speed and efficiency. Built for developers by developers, it offers Adaptive Inference, a flexible and scalable service that adjusts seamlessly to workload demands, ensuring high-performance processing and consistent turnaround times. Adaptive Inference provides three distinct processing options: real-time inference for ultra-low latency needs, asynchronous inference for cost-effective handling of flexible timing tasks, and batch inference for efficient processing of high-volume, bulk tasks. It supports a range of open-weight, cutting-edge multimodal models for chat, vision, code, and more, including Meta's Llama 4 Maverick and Scout, Qwen3-235B-A22B, DeepSeek-R1, and Gemma 3 . Kluster.ai's OpenAI-compatible API allows developers to integrate these models into their applications seamlessly.Starting Price: $0.15per input -
13
NativeMind
NativeMind
NativeMind is an open source, on-device AI assistant that runs entirely in your browser via Ollama integration, ensuring absolute privacy by never sending data to the cloud. Everything, from model inference to prompt processing, occurs locally, so there’s no syncing, logging, or data leakage. Users can load and switch between powerful open models such as DeepSeek, Qwen, Llama, Gemma, and Mistral instantly, without additional setup, and leverage native browser features for streamlined workflows. NativeMind offers clean, concise webpage summarization; persistent, context-aware chat across multiple tabs; local web search that retrieves and answers queries directly within the page; and immersive, format-preserving translation of entire pages. Built for speed and security, the extension is fully auditable and community-backed, delivering enterprise-grade performance for real-world use cases without vendor lock-in or hidden telemetry.Starting Price: Free -
14
Google AI Edge Gallery
Google
Google AI Edge Gallery is an experimental, open source Android app that demonstrates on-device machine learning and generative AI use cases, letting users download and run models locally (so they work offline once installed). It offers several features including AI Chat (multi-turn conversation), Ask Image (upload or use images to ask questions, identify objects, get descriptions), Audio Scribe (transcribe or translate recorded/uploaded audio), Prompt Lab (for single-turn tasks such as summarization, rewriting, code generation), and performance insights (metrics like latency, decode speed, etc.). Users can switch between different compatible models (including Gemma 3n and models from Hugging Face), bring their own LiteRT models, and explore model cards and source code for transparency. The app aims to protect privacy by doing all processing on the device, no internet connection needed for core operations after models are loaded, reducing latency, and enhancing data security.Starting Price: Free -
15
LFM2
Liquid AI
LFM2 is a next-generation series of on-device foundation models built to deliver the fastest generative-AI experience across a wide range of endpoints. It employs a new hybrid architecture that achieves up to 2x faster decode and prefill performance than comparable models, and up to 3x improvements in training efficiency compared to the previous generation. These models strike an optimal balance of quality, latency, and memory for deployment on embedded systems, allowing real-time, on-device AI across smartphones, laptops, vehicles, wearables, and other endpoints, enabling millisecond inference, device resilience, and full data sovereignty. Available in three dense checkpoints (0.35 B, 0.7 B, and 1.2 B parameters), LFM2 demonstrates benchmark performance that outperforms similarly sized models in tasks such as knowledge recall, mathematics, multilingual instruction-following, and conversational dialogue evaluations. -
16
TinyLlama
TinyLlama
The TinyLlama project aims to pretrain a 1.1B Llama model on 3 trillion tokens. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs. We adopted exactly the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint.Starting Price: Free -
17
Mu
Microsoft
Mu is a 330-million-parameter encoder–decoder language model designed to power the agent in Windows settings by mapping natural-language queries to Settings function calls, running fully on-device via NPUs at over 100 tokens per second while maintaining high accuracy. Drawing on Phi Silica optimizations, Mu’s encoder–decoder architecture reuses a fixed-length latent representation to cut computation and memory overhead, yielding 47 percent lower first-token latency and 4.7× higher decoding speed on Qualcomm Hexagon NPUs compared to similar decoder-only models. Hardware-aware tuning, including a 2/3–1/3 encoder–decoder parameter split, weight sharing between input and output embeddings, Dual LayerNorm, rotary positional embeddings, and grouped-query attention, enables fast inference at over 200 tokens per second on devices like Surface Laptop 7 and sub-500 ms response times for settings queries. -
18
KServe
KServe
Highly scalable and standards-based model inference platform on Kubernetes for trusted AI. KServe is a standard model inference platform on Kubernetes, built for highly scalable use cases. Provides performant, standardized inference protocol across ML frameworks. Support modern serverless inference workload with autoscaling including a scale to zero on GPU. Provides high scalability, density packing, and intelligent routing using ModelMesh. Simple and pluggable production serving for production ML serving including prediction, pre/post-processing, monitoring, and explainability. Advanced deployments with the canary rollout, experiments, ensembles, and transformers. ModelMesh is designed for high-scale, high-density, and frequently-changing model use cases. ModelMesh intelligently loads and unloads AI models to and from memory to strike an intelligent trade-off between responsiveness to users and computational footprint.Starting Price: Free -
19
Private LLM
Private LLM
Private LLM is a local AI chatbot for iOS and macOS that works offline, keeping your information completely on-device, safe, and private. It doesn't need the internet to work, so your data never leaves your device. It stays just with you. With no subscription fees, you pay once and use it on all your Apple devices. It's designed for everyone, with easy-to-use features for generating text, helping with language, and a whole lot more. Private LLM uses the latest AI models quantized with state-of-the-art quantization techniques to provide a high-quality on-device AI experience without compromising your privacy. It's a smart, secure way to get creative and productive, anytime and anywhere. Private LLM opens the door to the vast possibilities of AI with support for an extensive selection of open-source LLM models, including the Llama 3, Google Gemma, Microsoft Phi-2, Mixtral 8x7B family and many more on both your iPhones, iPads and Macs. -
20
Falcon Mamba 7B
Technology Innovation Institute (TII)
Falcon Mamba 7B is the first open-source State Space Language Model (SSLM), introducing a groundbreaking architecture for Falcon models. Recognized as the top-performing open-source SSLM worldwide by Hugging Face, it sets a new benchmark in AI efficiency. Unlike traditional transformers, SSLMs operate with minimal memory requirements and can generate extended text sequences without additional overhead. Falcon Mamba 7B surpasses leading transformer-based models, including Meta’s Llama 3.1 8B and Mistral’s 7B, showcasing superior performance. This innovation underscores Abu Dhabi’s commitment to advancing AI research and development on a global scale.Starting Price: Free -
21
Unsloth
Unsloth
Unsloth is an open source platform designed to accelerate and optimize the fine-tuning and training of Large Language Models (LLMs). It enables users to train custom models, such as ChatGPT, in just 24 hours instead of the typical 30 days, achieving speeds up to 30 times faster than Flash Attention 2 (FA2) while using 90% less memory. Unsloth supports both LoRA and QLoRA fine-tuning techniques, allowing for efficient customization of models like Mistral, Gemma, and Llama versions 1, 2, and 3. Unsloth's efficiency stems from manually deriving computationally intensive mathematical steps and handwriting GPU kernels, resulting in significant performance gains without requiring hardware modifications. Unsloth delivers a 10x speed increase on a single GPU and up to 32x on multi-GPU systems compared to FA2, with compatibility across NVIDIA GPUs from Tesla T4 to H100, and portability to AMD and Intel GPUs.Starting Price: Free -
22
Phi-4-mini-flash-reasoning
Microsoft
Phi-4-mini-flash-reasoning is a 3.8 billion‑parameter open model in Microsoft’s Phi family, purpose‑built for edge, mobile, and other resource‑constrained environments where compute, memory, and latency are tightly limited. It introduces the SambaY decoder‑hybrid‑decoder architecture with Gated Memory Units (GMUs) interleaved alongside Mamba state‑space and sliding‑window attention layers, delivering up to 10× higher throughput and a 2–3× reduction in latency compared to its predecessor without sacrificing advanced math and logic reasoning performance. Supporting a 64 K‑token context length and fine‑tuned on high‑quality synthetic data, it excels at long‑context retrieval, reasoning tasks, and real‑time inference, all deployable on a single GPU. Phi-4-mini-flash-reasoning is available today via Azure AI Foundry, NVIDIA API Catalog, and Hugging Face, enabling developers to build fast, scalable, logic‑intensive applications. -
23
DeepSeek R2
DeepSeek
DeepSeek R2 is the anticipated successor to DeepSeek R1, a groundbreaking AI reasoning model launched in January 2025 by the Chinese AI startup DeepSeek. Building on R1’s success, which disrupted the AI industry with its cost-effective performance rivaling top-tier models like OpenAI’s o1, R2 promises a quantum leap in capabilities. It is expected to deliver exceptional speed and human-like reasoning, excelling in complex tasks such as advanced coding and high-level mathematical problem-solving. Leveraging DeepSeek’s innovative Mixture-of-Experts architecture and efficient training methods, R2 aims to outperform its predecessor while maintaining a low computational footprint, potentially expanding its reasoning abilities to languages beyond English.Starting Price: Free -
24
Ministral 3B
Mistral AI
Mistral AI introduced two state-of-the-art models for on-device computing and edge use cases, named "les Ministraux": Ministral 3B and Ministral 8B. These models set a new frontier in knowledge, commonsense reasoning, function-calling, and efficiency in the sub-10B category. They can be used or tuned for various applications, from orchestrating agentic workflows to creating specialist task workers. Both models support up to 128k context length (currently 32k on vLLM), and Ministral 8B features a special interleaved sliding-window attention pattern for faster and memory-efficient inference. These models were built to provide a compute-efficient and low-latency solution for scenarios such as on-device translation, internet-less smart assistants, local analytics, and autonomous robotics. Used in conjunction with larger language models like Mistral Large, les Ministraux also serve as efficient intermediaries for function-calling in multi-step agentic workflows.Starting Price: Free -
25
WebLLM
WebLLM
WebLLM is a high-performance, in-browser language model inference engine that leverages WebGPU for hardware acceleration, enabling powerful LLM operations directly within web browsers without server-side processing. It offers full OpenAI API compatibility, allowing seamless integration with functionalities such as JSON mode, function-calling, and streaming. WebLLM natively supports a range of models, including Llama, Phi, Gemma, RedPajama, Mistral, and Qwen, making it versatile for various AI tasks. Users can easily integrate and deploy custom models in MLC format, adapting WebLLM to specific needs and scenarios. The platform facilitates plug-and-play integration through package managers like NPM and Yarn, or directly via CDN, complemented by comprehensive examples and a modular design for connecting with UI components. It supports streaming chat completions for real-time output generation, enhancing interactive applications like chatbots and virtual assistants.Starting Price: Free -
26
Ministral 8B
Mistral AI
Mistral AI has introduced two advanced models for on-device computing and edge applications, named "les Ministraux": Ministral 3B and Ministral 8B. These models excel in knowledge, commonsense reasoning, function-calling, and efficiency within the sub-10B parameter range. They support up to 128k context length and are designed for various applications, including on-device translation, offline smart assistants, local analytics, and autonomous robotics. Ministral 8B features an interleaved sliding-window attention pattern for faster and more memory-efficient inference. Both models can function as intermediaries in multi-step agentic workflows, handling tasks like input parsing, task routing, and API calls based on user intent with low latency and cost. Benchmark evaluations indicate that les Ministraux consistently outperforms comparable models across multiple tasks. As of October 16, 2024, both models are available, with Ministral 8B priced at $0.1 per million tokens.Starting Price: Free -
27
Dr7.ai
Dr7.ai
Dr7.ai is the world’s first unified medical AI hub, providing one API to access leading healthcare-focused language and vision models. The platform supports 15+ specialized models such as MedGemma, BioGPT, and Med-PaLM 2 for use cases ranging from EHR analysis to medical imaging and drug discovery. Its unified API standard allows developers and healthcare teams to integrate once and switch between models without multiple onboarding processes. With HIPAA/GDPR compliance, advanced encryption, and role-based access, Dr7.ai ensures security and privacy. Real-time updates, benchmarking, and multilingual support make it suitable for global healthcare applications. By streamlining access to cutting-edge AI, Dr7.ai empowers professionals to accelerate innovation in healthcare research, diagnostics, and treatment planning.Starting Price: $0 -
28
Kimi K2 Thinking
Moonshot AI
Kimi K2 Thinking is an advanced open source reasoning model developed by Moonshot AI, designed specifically for long-horizon, multi-step workflows where the system interleaves chain-of-thought processes with tool invocation across hundreds of sequential tasks. The model uses a mixture-of-experts architecture with a total of 1 trillion parameters, yet only about 32 billion parameters are activated per inference pass, optimizing efficiency while maintaining vast capacity. It supports a context window of up to 256,000 tokens, enabling the handling of extremely long inputs and reasoning chains without losing coherence. Native INT4 quantization is built in, which reduces inference latency and memory usage without performance degradation. Kimi K2 Thinking is explicitly built for agentic workflows; it can autonomously call external tools, manage sequential logic steps (up to and typically between 200-300 tool calls in a single chain), and maintain consistent reasoning.Starting Price: Free -
29
Qwen3-Omni
Alibaba
Qwen3-Omni is a natively end-to-end multilingual omni-modal foundation model that processes text, images, audio, and video and delivers real-time streaming responses in text and natural speech. It uses a Thinker-Talker architecture with a Mixture-of-Experts (MoE) design, early text-first pretraining, and mixed multimodal training to support strong performance across all modalities without sacrificing text or image quality. The model supports 119 text languages, 19 speech input languages, and 10 speech output languages. It achieves state-of-the-art results: across 36 audio and audio-visual benchmarks, it hits open-source SOTA on 32 and overall SOTA on 22, outperforming or matching strong closed-source models such as Gemini-2.5 Pro and GPT-4o. To reduce latency, especially in audio/video streaming, Talker predicts discrete speech codecs via a multi-codebook scheme and replaces heavier diffusion approaches. -
30
GLM-4.5V-Flash
Zhipu AI
GLM-4.5V-Flash is an open source vision-language model, designed to bring strong multimodal capabilities into a lightweight, deployable package. It supports image, video, document, and GUI inputs, enabling tasks such as scene understanding, chart and document parsing, screen reading, and multi-image analysis. Compared to larger models in the series, GLM-4.5V-Flash offers a compact footprint while retaining core VLM capabilities like visual reasoning, video understanding, GUI task handling, and complex document parsing. It can serve in “GUI agent” workflows, meaning it can interpret screenshots or desktop captures, recognize icons or UI elements, and assist with automated desktop or web-based tasks. Although it forgoes some of the largest-model performance gains, GLM-4.5V-Flash remains versatile for real-world multimodal tasks where efficiency, lower resource usage, and broad modality support are prioritized.Starting Price: Free -
31
OpenVINO
Intel
The Intel® Distribution of OpenVINO™ toolkit is an open-source AI development toolkit that accelerates inference across Intel hardware platforms. Designed to streamline AI workflows, it allows developers to deploy optimized deep learning models for computer vision, generative AI, and large language models (LLMs). With built-in tools for model optimization, the platform ensures high throughput and lower latency, reducing model footprint without compromising accuracy. OpenVINO™ is perfect for developers looking to deploy AI across a range of environments, from edge devices to cloud servers, ensuring scalability and performance across Intel architectures.Starting Price: Free -
32
VMware tc Server
Broadcom
tc Server is a Web application server based on open-source Apache Tomcat. It preserves the best of Tomcat and adds many mission-critical operational capabilities that are unavailable in the open-source product. tc Server harnesses the power of traditional JEE architectures and eliminates their complexity and performance drawbacks, making it easier, faster, and more cost-effective to build and run cloud-ready applications. With its lean architecture and small memory footprint, tc Server requires significantly fewer resources than conventional servers, which allows for greater server density in virtual and cloud environments. -
33
ZenCtrl
Fotographer AI
ZenCtrl is an open source AI image generation toolkit developed by Fotographer AI, designed to produce high-quality, multi-view, and diverse-scene outputs from a single image without any training. It enables precise regeneration of objects and subjects from any angle and background, offering real-time element regeneration that provides both stability and flexibility in creative workflows. ZenCtrl allows users to regenerate subjects from any angle, swap backgrounds or clothing with just a click, and start generating results immediately without the need for additional training. By leveraging advanced image processing techniques, it ensures high accuracy without the need for extensive training data. The model's architecture is composed of lightweight sub-models, each fine-tuned on task-specific data to excel at a single job, resulting in a lean system that delivers sharper, more controllable results.Starting Price: Free -
34
MaiaOS
Zyphra Technologies
Zyphra is an artificial intelligence company based in Palo Alto with a growing presence in Montreal and London. We’re building MaiaOS, a multimodal agent system combining advanced research in next-gen neural network architectures (SSM hybrids), long-term memory & reinforcement learning. We believe the future of AGI will involve a combination of cloud and on-device deployment strategies with an increasing shift toward local inference. MaiaOS is built around a deployment framework that maximizes inference efficiency for real-time intelligence. Our AI & product teams come from leading organizations and institutions including Google DeepMind, Anthropic, StabilityAI, Qualcomm, Neuralink, Nvidia, and Apple. We have deep expertise across AI models, learning algorithms, and systems/infrastructure with a focus on inference efficiency and AI silicon performance. Zyphra's team is committed to democratizing advanced AI systems. -
35
Sightify AI Agents
Sightify
Sightify | AI Agents is an LLM AI SaaS intended to automate SME workflows while ensuring data sovereignty. Some features include: Data-Sovereign Agents: Fine-tuned w/ RAG on open-source LLMs for specific business process optimization No AI Hallucinations: Source, page, and section citations for database-enforced tokens Multimodal: PDF, Excel, Word, TXT, PNG/JPEG, etc. CRM/ERP System Integration: API documentation, MCP compliant, R&D integration/support Updatable LLMs: Constant New Version Implementations (Qwen 70B, Gemma 27B) Our current AI Agents are: Knowledge Assistant: For client relationship management, HR/company regulations search, etc Contract Finalizer: Finalize legal contracts that are sent to or received from clients/partners Report Generator: Instant monthly/annual sales/marketing/budget reports Market Researcher: Research and analyze enterprise competitors, products, pricing, etc Meeting Notetaker: Employ LLM AI on audio-generated meeting notesStarting Price: $300/year/agent -
36
Mixtral 8x7B
Mistral AI
Mixtral 8x7B is a high-quality sparse mixture of experts model (SMoE) with open weights. Licensed under Apache 2.0. Mixtral outperforms Llama 2 70B on most benchmarks with 6x faster inference. It is the strongest open-weight model with a permissive license and the best model overall regarding cost/performance trade-offs. In particular, it matches or outperforms GPT-3.5 on most standard benchmarks.Starting Price: Free -
37
Hunyuan-Vision-1.5
Tencent
HunyuanVision is a cutting-edge vision-language model developed by Tencent’s Hunyuan team. It uses a mamba-transformer hybrid architecture to deliver strong performance and efficient inference in multimodal reasoning tasks. The version Hunyuan-Vision-1.5 is designed for “thinking on images,” meaning it not only understands vision+language content, but can perform deeper reasoning that involves manipulating or reflecting on image inputs, such as cropping, zooming, pointing, box drawing, or drawing on the image to acquire additional knowledge. It supports a variety of vision tasks (image + video recognition, OCR, diagram understanding), visual reasoning, and even 3D spatial comprehension, all in a unified multilingual framework. The model is built to work seamlessly across languages and tasks and is intended to be open sourced (including checkpoints, technical report, inference support) to encourage the community to experiment and adopt.Starting Price: Free -
38
MAI-1-preview
Microsoft
MAI-1 Preview is Microsoft AI’s first end-to-end trained foundation model, built entirely in-house as a mixture-of-experts architecture. Pre-trained and post-trained on approximately 15,000 NVIDIA H100 GPUs, it is designed to follow instructions and generate helpful, responsive text for everyday user queries, representing a prototype of future Copilot capabilities. Now available for public testing on LMArena, MAI-1 Preview delivers an early glimpse into the platform’s trajectory, with plans to roll out select text-based applications within Copilot over the coming weeks to gather user feedback and refine performance. Microsoft reinforces that it will continue combining its own models, partner models, and developments from the open-source community to flexibly power experiences across millions of unique interactions each day. -
39
Martian
Martian
By using the best-performing model for each request, we can achieve higher performance than any single model. Martian outperforms GPT-4 across OpenAI's evals (open/evals). We turn opaque black boxes into interpretable representations. Our router is the first tool built on top of our model mapping method. We are developing many other applications of model mapping including turning transformers from indecipherable matrices into human-readable programs. If a company experiences an outage or high latency period, automatically reroute to other providers so your customers never experience any issues. Determine how much you could save by using the Martian Model Router with our interactive cost calculator. Input your number of users, tokens per session, and sessions per month, and specify your cost/quality tradeoff. -
40
gpt-4o-mini Realtime
OpenAI
The gpt-4o-mini-realtime-preview model is a compact, lower-cost, realtime variant of GPT-4o designed to power speech and text interactions with low latency. It supports both text and audio inputs and outputs, enabling “speech in, speech out” conversational experiences via a persistent WebSocket or WebRTC connection. Unlike larger GPT-4o models, it currently does not support image or structured output modalities, focusing strictly on real-time voice/text use cases. Developers can open a real-time session via the /realtime/sessions endpoint to obtain an ephemeral key, then stream user audio (or text) and receive responses in real time over the same connection. The model is part of the early preview family (version 2024-12-17), intended primarily for testing and feedback rather than full production loads. Usage is subject to rate limits and may evolve during the preview period. Because it is multimodal in audio/text only, it enables use cases such as conversational voice agents.Starting Price: $0.60 per input -
41
HunyuanOCR
Tencent
Tencent Hunyuan is a large-scale, multimodal AI model family developed by Tencent that spans text, image, video, and 3D modalities, designed for general-purpose AI tasks like content generation, visual reasoning, and business automation. Its model lineup includes variants optimized for natural language understanding, multimodal vision-language comprehension (e.g., image & video understanding), text-to-image creation, video generation, and 3D content generation. Hunyuan models leverage a mixture-of-experts architecture and other innovations (like hybrid “mamba-transformer” designs) to deliver strong performance on reasoning, long-context understanding, cross-modal tasks, and efficient inference. For example, the vision-language model Hunyuan-Vision-1.5 supports “thinking-on-image”, enabling deep multimodal understanding and reasoning on images, video frames, diagrams, or spatial data. -
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Reka Flash 3
Reka
Reka Flash 3 is a 21-billion-parameter multimodal AI model developed by Reka AI, designed to excel in general chat, coding, instruction following, and function calling. It processes and reasons with text, images, video, and audio inputs, offering a compact, general-purpose solution for various applications. Trained from scratch on diverse datasets, including publicly accessible and synthetic data, Reka Flash 3 underwent instruction tuning on curated, high-quality data to optimize performance. The final training stage involved reinforcement learning using REINFORCE Leave One-Out (RLOO) with both model-based and rule-based rewards, enhancing its reasoning capabilities. With a context length of 32,000 tokens, Reka Flash 3 performs competitively with proprietary models like OpenAI's o1-mini, making it suitable for low-latency or on-device deployments. The model's full precision requires 39GB (fp16), but it can be compressed to as small as 11GB using 4-bit quantization. -
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SWE-1.5
Cognition
SWE-1.5 is the latest agent-model release by Cognition, purpose-built for software engineering and characterized by a “frontier-size” architecture comprising hundreds of billions of parameters and optimized end-to-end (model, inference engine, and agent harness) for both speed and intelligence. It achieves near-state-of-the-art coding performance and sets a new benchmark in latency, delivering inference speeds up to 950 tokens/second, roughly six times faster than its predecessor Haiku 4.5 and thirteen times faster than Sonnet 4.5. The model was trained using extensive reinforcement learning in realistic coding-agent environments with multi-turn workflows, unit tests, quality rubrics, and browser-based agentic execution; it also benefits from tightly integrated software tooling and high-throughput hardware (including thousands of GB200 NVL72 chips and a custom hypervisor infrastructure). -
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Ai2 OLMoE
The Allen Institute for Artificial Intelligence
Ai2 OLMoE is a fully open source mixture-of-experts language model that is capable of running completely on-device, allowing you to try our model privately and securely. Our app is intended to help researchers better explore how to make on-device intelligence better and to enable developers to quickly prototype new AI experiences, all with no cloud connectivity required. OLMoE is a highly efficient mixture-of-experts version of the Ai2 OLMo family of models. Experience which real-world tasks state-of-the-art local models are capable of. Research how to improve small AI models. Test your own models locally using our open-source codebase. Integrate OLMoE into other iOS applications. The Ai2 OLMoE app provides privacy and security by operating completely on-device. Easily share the output of your conversations with friends or colleagues. The OLMoE model and the application code are fully open source.Starting Price: Free -
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Yi-Lightning
Yi-Lightning
Yi-Lightning, developed by 01.AI under the leadership of Kai-Fu Lee, represents the latest advancement in large language models with a focus on high performance and cost-efficiency. It boasts a maximum context length of 16K tokens and is priced at $0.14 per million tokens for both input and output, making it remarkably competitive. Yi-Lightning leverages an enhanced Mixture-of-Experts (MoE) architecture, incorporating fine-grained expert segmentation and advanced routing strategies, which contribute to its efficiency in training and inference. This model has excelled in various domains, achieving top rankings in categories like Chinese, math, coding, and hard prompts on the chatbot arena, where it secured the 6th position overall and 9th in style control. Its development included comprehensive pre-training, supervised fine-tuning, and reinforcement learning from human feedback, ensuring both performance and safety, with optimizations in memory usage and inference speed. -
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Wan2.5
Alibaba
Wan2.5-Preview introduces a next-generation multimodal architecture designed to redefine visual generation across text, images, audio, and video. Its unified framework enables seamless multimodal inputs and outputs, powering deeper alignment through joint training across all media types. With advanced RLHF tuning, the model delivers superior video realism, expressive motion dynamics, and improved adherence to human preferences. Wan2.5 also excels in synchronized audio-video generation, supporting multi-voice output, sound effects, and cinematic-grade visuals. On the image side, it offers exceptional instruction following, creative design capabilities, and pixel-accurate editing for complex transformations. Together, these features make Wan2.5-Preview a breakthrough platform for high-fidelity content creation and multimodal storytelling.Starting Price: Free -
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Marble
World Labs
Marble is an experimental AI model internally tested by World Labs, a variant and extension of their Large World Model technology. It is a web service that turns a single 2D image into a navigable spatial environment. Marble offers two generation modes: a smaller, fast model for rough previews that’s quick to iterate on, and a larger, high-fidelity model that takes longer (around ten minutes in the example) but produces a significantly more convincing result. The value proposition is instant, photogrammetry-like image-to-world creation without a full capture rig, turning a single shot into an explorable space for memory capture, mood boards, archviz previews, or creative experiments. -
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WedoLow
WedoLow
WedoLow is a deep-tech optimization platform for embedded and hosted C/C++ applications, designed to push software performance and efficiency even on constrained hardware. WedoLow analyses source code, identifies performance bottlenecks (CPU load, memory footprint, energy use), and automatically applies algorithmic and architectural optimizations tailored to the target hardware. The core of the offering, the beLow suite, integrates seamlessly with common build systems, so it fits into existing workflows. Once analysis is complete, beLow provides a detailed diagnostic report, classifies optimization opportunities (bit-exact, permissive, or lossy depending on output-value tolerance), and helps teams choose appropriate strategies based on constraints and trade-offs. WedoLow also supports AI-assisted code generation and optimization via its WedoLow MCP Server. -
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GPT-4.1 mini
OpenAI
GPT-4.1 mini is a compact version of OpenAI’s powerful GPT-4.1 model, designed to provide high performance while significantly reducing latency and cost. With a smaller size and optimized architecture, GPT-4.1 mini still delivers impressive results in tasks such as coding, instruction following, and long-context processing. It supports up to 1 million tokens of context, making it an efficient solution for applications that require fast responses without sacrificing accuracy or depth.Starting Price: $0.40 per 1M tokens (input) -
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NeuroSplit
Skymel
NeuroSplit is a patent-pending adaptive-inferencing technology that dynamically “slices” a model’s neural network connections in real time to create two synchronized sub-models, executing initial layers on the end user’s device and offloading the remainder to cloud GPUs, thereby harnessing idle local compute and reducing server costs by up to 60% without sacrificing performance or accuracy. Integrated into Skymel’s Orchestrator Agent platform, NeuroSplit routes each inference request across devices and clouds based on specified latency, cost, or resource constraints, automatically applying fallback logic and intent-driven model selection to maintain reliability under varying network conditions. Its decentralized architecture ensures end-to-end encryption, role-based access controls, and isolated execution contexts, while real-time analytics dashboards provide insights into cost, throughput, and latency metrics.
