Alternatives to Mercury 2
Compare Mercury 2 alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to Mercury 2 in 2026. Compare features, ratings, user reviews, pricing, and more from Mercury 2 competitors and alternatives in order to make an informed decision for your business.
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1
Mercury Coder
Inception Labs
Mercury, the latest innovation from Inception Labs, is the first commercial-scale diffusion large language model (dLLM), offering a 10x speed increase and significantly lower costs compared to traditional autoregressive models. Built for high-performance reasoning, coding, and structured text generation, Mercury processes over 1000 tokens per second on NVIDIA H100 GPUs, making it one of the fastest LLMs available. Unlike conventional models that generate text one token at a time, Mercury refines responses using a coarse-to-fine diffusion approach, improving accuracy and reducing hallucinations. With Mercury Coder, a specialized coding model, developers can experience cutting-edge AI-driven code generation with superior speed and efficiency.Starting Price: Free -
2
Mercury Edit 2
Inception
Mercury Edit 2 is part of Inception Labs’ Mercury family of AI models, designed to perform high-speed reasoning, coding, and editing tasks using a fundamentally different architecture from traditional large language models. It builds on Mercury 2, a diffusion-based reasoning model that generates and refines entire outputs in parallel rather than producing text token by token, enabling significantly faster performance and more responsive editing workflows. Instead of acting like a sequential “typewriter,” the system behaves more like an editor, starting with a rough draft and iteratively improving it across multiple tokens at once, which allows for real-time interaction and rapid iteration in tasks such as code editing, content generation, and agent-based workflows. This architecture delivers throughput of up to around 1,000 tokens per second, making it several times faster than conventional models while maintaining competitive reasoning quality across benchmarks.Starting Price: $0.25 per 1M input tokens -
3
DiffusionGemma
Google
DiffusionGemma is an experimental open model that explores text diffusion, an exceptionally fast approach to text generation. Released under an Apache 2.0 license, this 26B Mixture of Experts (MoE) model moves beyond the sequential token-by-token processing of typical autoregressive Large Language Models (LLMs). Instead, it generates entire blocks of text simultaneously, delivering up to 4x faster text generation on GPUs. Built on the intelligence-per-parameter of the Gemma 4 family and Gemini Diffusion research, DiffusionGemma integrates a novel diffusion head designed to maximize generation speed. It is designed for researchers and developers exploring speed-critical, interactive local workflows such as in-line editing, rapid iteration, and non-linear text structures. By shifting the decode bottleneck from memory bandwidth to compute, it can generate more than 1,000 tokens per second on a single NVIDIA H100 and more than 700 tokens per second on an NVIDIA GeForce RTX 5090.Starting Price: Free -
4
ByteDance Seed
ByteDance
Seed Diffusion Preview is a large-scale, code-focused language model that uses discrete-state diffusion to generate code non-sequentially, achieving dramatically faster inference without sacrificing quality by decoupling generation from the token-by-token bottleneck of autoregressive models. It combines a two-stage curriculum, mask-based corruption followed by edit-based augmentation, to robustly train a standard dense Transformer, striking a balance between speed and accuracy and avoiding shortcuts like carry-over unmasking to preserve principled density estimation. The model delivers an inference speed of 2,146 tokens/sec on H20 GPUs, outperforming contemporary diffusion baselines while matching or exceeding their accuracy on standard code benchmarks, including editing tasks, thereby establishing a new speed-quality Pareto frontier and demonstrating discrete diffusion’s practical viability for real-world code generation.Starting Price: Free -
5
Uni-1
Luma AI
UNI-1 is a multimodal artificial intelligence model developed by Luma AI that unifies visual generation and reasoning capabilities within a single architecture, representing a step toward multimodal general intelligence. It was designed to overcome the limitations of traditional AI pipelines, where language models, image generators, and other systems operate independently without shared reasoning. UNI-1 integrates these capabilities so that language, visual understanding, and image generation work together inside one system, allowing the model to reason about scenes, interpret instructions, and generate visual outputs that follow logical and spatial constraints. At its core, UNI-1 is a decoder-only autoregressive transformer that processes text and images as a single interleaved sequence of tokens, enabling the model to treat language and visual information within the same computational framework rather than through separate encoders. -
6
GLM-Image
Z.ai
GLM-Image is a next-generation, open source image generation model developed by Z.ai, designed to combine deep language understanding with high-fidelity visual synthesis. Unlike traditional diffusion-only models, it uses a hybrid architecture that integrates an autoregressive language model with a diffusion decoder, enabling it to first reason about the structure, meaning, and relationships within a prompt before generating the image itself. This approach allows GLM-Image to excel in scenarios that require precise semantic control, such as generating infographics, presentation slides, posters, and diagrams with accurate embedded text and complex layouts. With a total of around 16 billion parameters, the model achieves strong performance in rendering readable, correctly placed text within images, an area where many image models struggle, while maintaining detailed visual quality and consistency. -
7
Gemini Diffusion
Google DeepMind
Gemini Diffusion is our state-of-the-art research model exploring what diffusion means for language and text generation. Large-language models are the foundation of generative AI today. We’re using a technique called diffusion to explore a new kind of language model that gives users greater control, creativity, and speed in text generation. Diffusion models work differently. Instead of predicting text directly, they learn to generate outputs by refining noise, step by step. This means they can iterate on a solution very quickly and error correct during the generation process. This helps them excel at tasks like editing, including in the context of math and code. Generates entire blocks of tokens at once, meaning it responds more coherently to a user’s prompt than autoregressive models. Gemini Diffusion’s external benchmark performance is comparable to much larger models, whilst also being faster. -
8
Nemotron 3 Ultra
NVIDIA
Nemotron 3 Nano is a compact, open large language model in NVIDIA’s Nemotron 3 family, designed for efficient agentic reasoning, conversational AI, and coding tasks. It uses a hybrid Mixture-of-Experts Mamba-Transformer architecture that activates only a small subset of parameters per token, enabling low-latency inference while maintaining strong accuracy and reasoning performance. It has approximately 31.6 billion total parameters with around 3.2 billion active (3.6 billion including embeddings), allowing it to achieve higher accuracy than previous Nemotron 2 Nano while using less computation per forward pass. Nemotron 3 Nano supports long-context processing of up to one million tokens, enabling it to handle large documents, multi-step workflows, and extended reasoning chains in a single pass. It is designed for high-throughput, real-time execution, excelling in multi-turn conversations, tool calling, and agent-based workflows where tasks require planning, reasoning, and more. -
9
Inception Labs
Inception Labs
Inception Labs is pioneering the next generation of AI with diffusion-based large language models (dLLMs), a breakthrough in AI that offers 10x faster performance and 5-10x lower cost than traditional autoregressive models. Inspired by the success of diffusion models in image and video generation, Inception’s dLLMs introduce enhanced reasoning, error correction, and multimodal capabilities, allowing for more structured and accurate text generation. With applications spanning enterprise AI, research, and content generation, Inception’s approach sets a new standard for speed, efficiency, and control in AI-driven workflows. -
10
Nemotron 3 Super
NVIDIA
Nemotron-3 Super is part of NVIDIA’s Nemotron 3 family of open models designed to enable advanced agentic AI systems that can reason, plan, and execute multi-step workflows across complex environments. The model introduces a hybrid Mamba-Transformer Mixture-of-Experts architecture that combines the efficiency of state-space Mamba layers with the contextual understanding of transformer attention, allowing it to process long sequences and complex reasoning tasks with high accuracy and throughput. This architecture activates only a subset of model parameters for each token, improving computational efficiency while maintaining strong reasoning capabilities and enabling scalable inference for large workloads. Nemotron-3 Super contains roughly 120 billion parameters with around 12 billion active during inference, accelerating multi-step reasoning and collaborative agent interactions across large contexts. -
11
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. -
12
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. -
13
NVIDIA Cosmos
NVIDIA
NVIDIA Cosmos is a developer-first platform of state-of-the-art generative World Foundation Models (WFMs), advanced video tokenizers, guardrails, and an accelerated data processing and curation pipeline designed to supercharge physical AI development. It enables developers working on autonomous vehicles, robotics, and video analytics AI agents to generate photorealistic, physics-aware synthetic video data, trained on an immense dataset including 20 million hours of real-world and simulated video, to rapidly simulate future scenarios, train world models, and fine‑tune custom behaviors. It includes three core WFM types; Cosmos Predict, capable of generating up to 30 seconds of continuous video from multimodal inputs; Cosmos Transfer, which adapts simulations across environments and lighting for versatile domain augmentation; and Cosmos Reason, a vision-language model that applies structured reasoning to interpret spatial-temporal data for planning and decision-making.Starting Price: Free -
14
Step 3.5 Flash
StepFun
Step 3.5 Flash is an advanced open source foundation language model engineered for frontier reasoning and agentic capabilities with exceptional efficiency, built on a sparse Mixture of Experts (MoE) architecture that selectively activates only about 11 billion of its ~196 billion parameters per token to deliver high-density intelligence and real-time responsiveness. Its 3-way Multi-Token Prediction (MTP-3) enables generation throughput in the hundreds of tokens per second for complex multi-step reasoning chains and task execution, and it supports efficient long contexts with a hybrid sliding window attention approach that reduces computational overhead across large datasets or codebases. It demonstrates robust performance on benchmarks for reasoning, coding, and agentic tasks, rivaling or exceeding many larger proprietary models, and includes a scalable reinforcement learning framework for consistent self-improvement.Starting Price: Free -
15
DeepSeek-V4-Flash
DeepSeek
DeepSeek-V4-Flash is a high-efficiency Mixture-of-Experts (MoE) language model designed for fast, scalable reasoning and text generation. It features 284 billion total parameters with 13 billion activated parameters, delivering strong performance while optimizing computational cost. The model supports an extensive context window of up to one million tokens, enabling it to process large documents and complex workflows with ease. Its hybrid attention architecture enhances long-context efficiency by reducing memory and compute requirements. Trained on over 32 trillion tokens, DeepSeek-V4-Flash demonstrates solid capabilities across knowledge, reasoning, and coding tasks. It is designed for scenarios where speed and efficiency are critical, offering a balance between performance and resource usage. The model also supports multiple reasoning modes, allowing users to adjust between faster outputs and deeper analysis.Starting Price: Free -
16
MiMo-V2-Flash
Xiaomi Technology
MiMo-V2-Flash is an open weight large language model developed by Xiaomi based on a Mixture-of-Experts (MoE) architecture that blends high performance with inference efficiency. It has 309 billion total parameters but activates only 15 billion active parameters per inference, letting it balance reasoning quality and computational efficiency while supporting extremely long context handling, for tasks like long-document understanding, code generation, and multi-step agent workflows. It incorporates a hybrid attention mechanism that interleaves sliding-window and global attention layers to reduce memory usage and maintain long-range comprehension, and it uses a Multi-Token Prediction (MTP) design that accelerates inference by processing batches of tokens in parallel. MiMo-V2-Flash delivers very fast generation speeds (up to ~150 tokens/second) and is optimized for agentic applications requiring sustained reasoning and multi-turn interactions.Starting Price: Free -
17
GPT-4o mini
OpenAI
A small model with superior textual intelligence and multimodal reasoning. GPT-4o mini enables a broad range of tasks with its low cost and latency, such as applications that chain or parallelize multiple model calls (e.g., calling multiple APIs), pass a large volume of context to the model (e.g., full code base or conversation history), or interact with customers through fast, real-time text responses (e.g., customer support chatbots). Today, GPT-4o mini supports text and vision in the API, with support for text, image, video and audio inputs and outputs coming in the future. The model has a context window of 128K tokens, supports up to 16K output tokens per request, and has knowledge up to October 2023. Thanks to the improved tokenizer shared with GPT-4o, handling non-English text is now even more cost effective. -
18
Amazon Nova Micro
Amazon
Amazon Nova Micro is an AI model designed for high-speed, low-cost text processing and generation. It excels in language understanding, translation, code completion, and mathematical problem-solving, providing fast responses with a generation speed of over 200 tokens per second. The model supports fine-tuning for text input and is ideal for applications requiring real-time processing and efficiency. With support for 200+ languages and a maximum of 128k tokens, Nova Micro is perfect for interactive AI applications that prioritize speed and affordability. -
19
Trinity-Large-Thinking
Arcee AI
Trinity Large Thinking is a frontier open source reasoning model developed by Arcee AI, designed specifically for complex, multi-step problem solving and autonomous agent workflows that require long-horizon planning and tool use. Built on a sparse Mixture-of-Experts architecture with roughly 400 billion total parameters but only about 13 billion active per token, the model achieves high efficiency while maintaining strong reasoning performance across tasks such as mathematical problem solving, code generation, and multi-step analysis. It introduces extended chain-of-thought reasoning capabilities, allowing the model to generate intermediate “thinking traces” before producing final answers, which improves accuracy and reliability in complex scenarios. Trinity Large Thinking supports a very large context window of up to 262K tokens, enabling it to process long documents, maintain state across extended interactions, and operate effectively in continuous agent loops.Starting Price: Free -
20
Seed-Music
ByteDance
Seed-Music is a unified framework for high-quality and controlled music generation and editing, capable of producing vocal and instrumental works from multimodal inputs such as lyrics, style descriptions, sheet music, audio references, or voice prompts, and of supporting post-production editing of existing tracks by allowing direct modification of melodies, timbres, lyrics, or instruments. It combines autoregressive language modeling with diffusion approaches and a three-stage pipeline comprising representation learning (which encodes raw audio into intermediate representations, including audio tokens, symbolic music tokens, and vocoder latents), generation (which transforms these multimodal inputs into music representations), and rendering (which converts those representations into high-fidelity audio). The system supports lead-sheet to song conversion, singing synthesis, voice conversion, audio continuation, style transfer, and fine-grained control over music structure. -
21
Qwen3.5
Alibaba
Qwen3.5 is a next-generation open-weight multimodal large language model designed to power native vision-language agents. The flagship release, Qwen3.5-397B-A17B, combines a hybrid linear attention architecture with sparse mixture-of-experts, activating only 17 billion parameters per forward pass out of 397 billion total to maximize efficiency. It delivers strong benchmark performance across reasoning, coding, multilingual understanding, visual reasoning, and agent-based tasks. The model expands language support from 119 to 201 languages and dialects while introducing a 1M-token context window in its hosted version, Qwen3.5-Plus. Built for multimodal tasks, it processes text, images, and video with advanced spatial reasoning and tool integration. Qwen3.5 also incorporates scalable reinforcement learning environments to improve general agent capabilities. Designed for developers and enterprises, it enables efficient, tool-augmented, multimodal AI workflows.Starting Price: Free -
22
Mercury Rugged Edge Servers
Mercury
Rugged subsystems engineered to bring the latest Silicon Valley technology to every inhospitable corner of the globe. No matter the end use, environment or security requirements, when your mission is critical Mercury rugged servers and embedded processing subsystems are the only options. Compute-heavy applications, including AI, signals intelligence and sensor fusion, have driven the need for real-time big data analysis at the edge. Mercury's ruggedized servers and processing solutions make the most sophisticated Silicon Valley technology profoundly more accessible to the A&D industry for actionable insights in the field. Our fully configurable systems are engineered to push computing to the cutting edge of innovation. Mercury’s airborne and mission computers accelerate complex airborne applications like artificial intelligence (AI) and streamline integration, technology refresh, and safety certification. -
23
VideoPoet
Google
VideoPoet is a simple modeling method that can convert any autoregressive language model or large language model (LLM) into a high-quality video generator. It contains a few simple components. An autoregressive language model learns across video, image, audio, and text modalities to autoregressively predict the next video or audio token in the sequence. A mixture of multimodal generative learning objectives are introduced into the LLM training framework, including text-to-video, text-to-image, image-to-video, video frame continuation, video inpainting and outpainting, video stylization, and video-to-audio. Furthermore, such tasks can be composed together for additional zero-shot capabilities. This simple recipe shows that language models can synthesize and edit videos with a high degree of temporal consistency. -
24
LTM-2-mini
Magic AI
LTM-2-mini is a 100M token context model: LTM-2-mini. 100M tokens equals ~10 million lines of code or ~750 novels. For each decoded token, LTM-2-mini’s sequence-dimension algorithm is roughly 1000x cheaper than the attention mechanism in Llama 3.1 405B1 for a 100M token context window. The contrast in memory requirements is even larger – running Llama 3.1 405B with a 100M token context requires 638 H100s per user just to store a single 100M token KV cache.2 In contrast, LTM requires a small fraction of a single H100’s HBM per user for the same context. -
25
NVIDIA Llama Nemotron
NVIDIA
NVIDIA Llama Nemotron is a family of advanced language models optimized for reasoning and a diverse set of agentic AI tasks. These models excel in graduate-level scientific reasoning, advanced mathematics, coding, instruction following, and tool calls. Designed for deployment across various platforms, from data centers to PCs, they offer the flexibility to toggle reasoning capabilities on or off, reducing inference costs when deep reasoning isn't required. The Llama Nemotron family includes models tailored for different deployment needs. Built upon Llama models and enhanced by NVIDIA through post-training, these models demonstrate improved accuracy, up to 20% over base models, and optimized inference speeds, achieving up to five times the performance of other leading open reasoning models. This efficiency enables handling more complex reasoning tasks, enhances decision-making capabilities, and reduces operational costs for enterprises. -
26
CodeQwen
Alibaba
CodeQwen is the code version of Qwen, the large language model series developed by the Qwen team, Alibaba Cloud. It is a transformer-based decoder-only language model pre-trained on a large amount of data of codes. Strong code generation capabilities and competitive performance across a series of benchmarks. Supporting long context understanding and generation with the context length of 64K tokens. CodeQwen supports 92 coding languages and provides excellent performance in text-to-SQL, bug fixes, etc. You can just write several lines of code with transformers to chat with CodeQwen. Essentially, we build the tokenizer and the model from pre-trained methods, and we use the generate method to perform chatting with the help of the chat template provided by the tokenizer. We apply the ChatML template for chat models following our previous practice. The model completes the code snippets according to the given prompts, without any additional formatting.Starting Price: Free -
27
RemObjects Mercury
RemObjects Mercury
Mercury is an implementation of the BASIC programming language that is fully code-compatible with Microsoft Visual Basic.NET™, but takes it to the next level, and to new horizons. With Mercury, you will be able to build your existing VB.NET projects and leverage your Visual Basic™ language experience to write code for any modern target platform. You can mix Mercury code with any of the other five Elements languages in the same project if you like! The Mercury language will be deeply integrated into our development environments. Develop your projects in our smart yet lightweight IDEs, Water on Windows or Fire on Mac, with project templates, code completion, integrated debugging for all platforms, and many other advanced development features. Of course, Mercury will also integrate into Visual Studio™ 2017, 2019 and 2022. With Elements, all languages are created equal. Even within the same project, you can mix Mercury, C#, Swift, Java, Oxygene and Go.Starting Price: $49 per month -
28
MiniMax M2.5
MiniMax
MiniMax M2.5 is a frontier AI model engineered for real-world productivity across coding, agentic workflows, search, and office tasks. Extensively trained with reinforcement learning in hundreds of thousands of real-world environments, it achieves state-of-the-art performance in benchmarks such as SWE-Bench Verified and BrowseComp. The model demonstrates strong architectural thinking, decomposing complex problems before generating code across more than ten programming languages. M2.5 operates at high throughput speeds of up to 100 tokens per second, enabling faster completion of multi-step tasks. It is optimized for efficient reasoning, reducing token usage and execution time compared to previous versions. With dramatically lower pricing than competing frontier models, it delivers powerful performance at minimal cost. Integrated into MiniMax Agent, M2.5 supports professional-grade office workflows, financial modeling, and autonomous task execution.Starting Price: Free -
29
Qwen3.5-Plus
Alibaba
Qwen3.5-Plus is a high-performance native vision-language model designed for efficient text generation, deep reasoning, and multimodal understanding. Built on a hybrid architecture that combines linear attention with a sparse mixture-of-experts design, it delivers strong performance while optimizing inference efficiency. The model supports text, image, and video inputs and produces text outputs, making it suitable for complex multimodal workflows. With a massive 1 million token context window and up to 64K output tokens, Qwen3.5-Plus enables long-form reasoning and large-scale document analysis. It includes advanced capabilities such as structured outputs, function calling, web search, and tool integration via the Responses API. The model supports prefix continuation, caching, batch processing, and fine-tuning for flexible deployment. Designed for developers and enterprises, Qwen3.5-Plus provides scalable, high-throughput AI performance with OpenAI-compatible API access.Starting Price: $0.4 per 1M tokens -
30
Mercurial
Mercurial
Mercurial is a free, distributed source control management tool. It efficiently handles projects of any size and offers an easy and intuitive interface. Mercurial efficiently handles projects of any size and kind. Every clone contains the whole project history, so most actions are local, fast and convenient. Mercurial supports a multitude of workflows and you can easily enhance its functionality with extensions. Mercurial strives to deliver on each of its promises. Most tasks simply work on the first try and without requiring arcane knowledge. The functionality of Mercurial can be increased with extensions, either by activating the official ones which are shipped with Mercurial or downloading some from the wiki or by writing your own. Extensions are written in Python and can change the workings of the basic commands, add new commands and access all the core functions of Mercurial. -
31
GLM-4.5
Z.ai
GLM‑4.5 is Z.ai’s latest flagship model in the GLM family, engineered with 355 billion total parameters (32 billion active) and a companion GLM‑4.5‑Air variant (106 billion total, 12 billion active) to unify advanced reasoning, coding, and agentic capabilities in one architecture. It operates in a “thinking” mode for complex, multi‑step reasoning and tool use, and a “non‑thinking” mode for instant responses, supporting up to 128 K token context length and native function calling. Available via the Z.ai chat platform and API, with open weights on HuggingFace and ModelScope, GLM‑4.5 ingests diverse inputs to solve general problem‑solving, common‑sense reasoning, coding from scratch or within existing projects, and end‑to‑end agent workflows such as web browsing and slide generation. Built on a Mixture‑of‑Experts design with loss‑free balance routing, grouped‑query attention, and an MTP layer for speculative decoding, it delivers enterprise‑grade performance. -
32
DeepSeek-V4
DeepSeek
DeepSeek-V4 is a next-generation open-source language model designed for high-performance reasoning, coding, and long-context intelligence. It introduces a powerful architecture with up to one million token context length, enabling seamless handling of large datasets and complex multi-step workflows. The model comes in two variants: DeepSeek-V4-Pro for maximum performance and DeepSeek-V4-Flash for efficiency and speed. DeepSeek-V4-Pro features 1.6 trillion total parameters with 49 billion activated, delivering near state-of-the-art performance comparable to leading closed-source models. It excels in agentic coding, mathematical reasoning, and world knowledge tasks. The model integrates advanced attention mechanisms, including token-wise compression and sparse attention, significantly reducing compute and memory costs. It is also optimized for AI agents, supporting tool use and multi-step workflows.Starting Price: Free -
33
SubQ
Subquadratic
SubQ is a large language model developed by Subquadratic, designed specifically for long-context reasoning tasks. It can process up to 12 million tokens in a single prompt, allowing it to analyze entire codebases, long histories, and complex datasets at once. The model uses a sub-quadratic sparse-attention architecture that improves efficiency by focusing only on the most relevant relationships in the data. This approach reduces computational overhead while maintaining strong performance on large-scale tasks. SubQ is optimized for use cases such as software engineering, coding agents, and long-context retrieval. It delivers fast processing speeds and operates at a lower cost compared to many traditional models. Developers can access SubQ through APIs or integrate it into coding tools for enhanced workflows. Its architecture enables scalable AI reasoning without the limitations of standard transformer models. -
34
DeepSeek-V4-Pro
DeepSeek
DeepSeek-V4-Pro is a large-scale Mixture-of-Experts (MoE) language model designed for advanced reasoning, coding, and long-context understanding. It features 1.6 trillion total parameters with 49 billion activated parameters, enabling high performance while maintaining efficiency. The model supports an exceptionally large context window of up to one million tokens, allowing it to process extensive documents and workflows. It uses a hybrid attention architecture to optimize long-context performance and reduce computational cost. DeepSeek-V4-Pro is trained on over 32 trillion tokens, improving its knowledge and reasoning capabilities. It also includes advanced optimization techniques for stability and faster convergence during training. The model supports multiple reasoning modes, allowing users to balance speed and accuracy based on their needs. Overall, it provides a powerful open-source solution for complex AI tasks and large-scale applications.Starting Price: Free -
35
Gemini 3.1 Flash-Lite
Google
Gemini 3.1 Flash-Lite is Google’s fastest and most cost-efficient model in the Gemini 3 series, designed for high-volume developer workloads. It delivers strong performance at scale while maintaining affordability, with pricing set at $0.25 per million input tokens and $1.50 per million output tokens. The model significantly improves speed, offering a 2.5x faster time to first answer token and a 45% increase in output speed compared to Gemini 2.5 Flash. Despite its lower cost tier, it achieves high benchmark results, including an Elo score of 1432 and strong performance across reasoning and multimodal evaluations. Gemini 3.1 Flash-Lite supports adaptive “thinking levels,” allowing developers to control how much reasoning power is used for different tasks. It is suitable for large-scale applications such as translation, content moderation, user interface generation, and simulation building. -
36
Command A Reasoning
Cohere AI
Command A Reasoning is Cohere’s most advanced enterprise-ready language model, engineered for high-stakes reasoning tasks and seamless integration into AI agent workflows. The model delivers exceptional reasoning performance, efficiency, and controllability, scaling across multi-GPU setups with support for up to 256,000-token context windows, ideal for handling long documents and multi-step agentic tasks. Organizations can fine-tune output precision and latency through a token budget, allowing a single model to flexibly serve both high-accuracy and high-throughput use cases. It powers Cohere’s North platform with leading benchmark performance and excels in multilingual contexts across 23 languages. Designed with enterprise safety in mind, it balances helpfulness with robust safeguards against harmful outputs. A lightweight deployment option allows running the model securely on a single H100 or A100 GPU, simplifying private, scalable use. -
37
Qwen3-VL
Alibaba
Qwen3-VL is the newest vision-language model in the Qwen family (by Alibaba Cloud), designed to fuse powerful text understanding/generation with advanced visual and video comprehension into one unified multimodal model. It accepts inputs in mixed modalities, text, images, and video, and handles long, interleaved contexts natively (up to 256 K tokens, with extensibility beyond). Qwen3-VL delivers major advances in spatial reasoning, visual perception, and multimodal reasoning; the model architecture incorporates several innovations such as Interleaved-MRoPE (for robust spatio-temporal positional encoding), DeepStack (to leverage multi-level features from its Vision Transformer backbone for refined image-text alignment), and text–timestamp alignment (for precise reasoning over video content and temporal events). These upgrades enable Qwen3-VL to interpret complex scenes, follow dynamic video sequences, read and reason about visual layouts.Starting Price: Free -
38
Olmo 3
Ai2
Olmo 3 is a fully open model family spanning 7 billion and 32 billion parameter variants that delivers not only high-performing base, reasoning, instruction, and reinforcement-learning models, but also exposure of the entire model flow, including raw training data, intermediate checkpoints, training code, long-context support (65,536 token window), and provenance tooling. Starting with the Dolma 3 dataset (≈9 trillion tokens) and its disciplined mix of web text, scientific PDFs, code, and long-form documents, the pre-training, mid-training, and long-context phases shape the base models, which are then post-trained via supervised fine-tuning, direct preference optimisation, and RL with verifiable rewards to yield the Think and Instruct variants. The 32 B Think model is described as the strongest fully open reasoning model to date, competitively close to closed-weight peers in math, code, and complex reasoning.Starting Price: Free -
39
AudioCraft
Meta AI
AudioCraft is a single-stop code base for all your generative audio needs: music, sound effects, and compression after training on raw audio signals. With AudioCraft, we simplify the overall design of generative models for audio compared to prior work. Both MusicGen and AudioGen consist of a single autoregressive Language Model (LM) that operates over streams of compressed discrete music representation, i.e., tokens. We introduce a simple approach to leverage the internal structure of the parallel streams of tokens and show that, with a single model and elegant token interleaving pattern, our approach efficiently models audio sequences, simultaneously capturing the long-term dependencies in the audio and allowing us to generate high-quality audio. Our models leverage the EnCodec neural audio codec to learn the discrete audio tokens from the raw waveform. EnCodec maps the audio signal to one or several parallel streams of discrete tokens. -
40
ModelScope
Alibaba Cloud
This model is based on a multi-stage text-to-video generation diffusion model, which inputs a description text and returns a video that matches the text description. Only English input is supported. This model is based on a multi-stage text-to-video generation diffusion model, which inputs a description text and returns a video that matches the text description. Only English input is supported. The text-to-video generation diffusion model consists of three sub-networks: text feature extraction, text feature-to-video latent space diffusion model, and video latent space to video visual space. The overall model parameters are about 1.7 billion. Support English input. The diffusion model adopts the Unet3D structure, and realizes the function of video generation through the iterative denoising process from the pure Gaussian noise video.Starting Price: Free -
41
Mistral Large
Mistral AI
Mistral Large is Mistral AI's flagship language model, designed for advanced text generation and complex multilingual reasoning tasks, including text comprehension, transformation, and code generation. It supports English, French, Spanish, German, and Italian, offering a nuanced understanding of grammar and cultural contexts. With a 32,000-token context window, it can accurately recall information from extensive documents. The model's precise instruction-following and native function-calling capabilities facilitate application development and tech stack modernization. Mistral Large is accessible through Mistral's platform, Azure AI Studio, and Azure Machine Learning, and can be self-deployed for sensitive use cases. Benchmark evaluations indicate that Mistral Large achieves strong results, making it the world's second-ranked model generally available through an API, next to GPT-4.Starting Price: Free -
42
Yi-Large
01.AI
Yi-Large is a proprietary large language model developed by 01.AI, offering a 32k context length with both input and output costs at $2 per million tokens. It stands out with its advanced capabilities in natural language processing, common-sense reasoning, and multilingual support, performing on par with leading models like GPT-4 and Claude3 in various benchmarks. Yi-Large is designed for tasks requiring complex inference, prediction, and language understanding, making it suitable for applications like knowledge search, data classification, and creating human-like chatbots. Its architecture is based on a decoder-only transformer with enhancements such as pre-normalization and Group Query Attention, and it has been trained on a vast, high-quality multilingual dataset. This model's versatility and cost-efficiency make it a strong contender in the AI market, particularly for enterprises aiming to deploy AI solutions globally.Starting Price: $0.19 per 1M input token -
43
ModelArk
ByteDance
ModelArk is ByteDance’s one-stop large model service platform, providing access to cutting-edge AI models for video, image, and text generation. With powerful options like Seedance 1.0 for video, Seedream 3.0 for image creation, and DeepSeek-V3.1 for reasoning, it enables businesses and developers to build scalable, AI-driven applications. Each model is backed by enterprise-grade security, including end-to-end encryption, data isolation, and auditability, ensuring privacy and compliance. The platform’s token-based pricing keeps costs transparent, starting with 500,000 free inference tokens per LLM and 2 million tokens per vision model. Developers can quickly integrate APIs for inference, fine-tuning, evaluation, and plugins to extend model capabilities. Designed for scalability, ModelArk offers fast deployment, high GPU availability, and seamless enterprise integration. -
44
Nemotron 3 Nano
NVIDIA
Nemotron 3 Nano is the smallest model in the NVIDIA Nemotron 3 family, built for agentic AI applications with strong reasoning, conversational ability, and cost-efficient inference. It is a hybrid Mamba-Transformer Mixture-of-Experts model with 3.2 billion active parameters, 3.6 billion including embeddings, and 31.6 billion total parameters. NVIDIA describes it as more accurate than the previous Nemotron 2 Nano while activating less than half of the parameters per forward pass, improving efficiency without sacrificing performance. The model is positioned as more accurate than GPT-OSS-20B and Qwen3-30B-A3B-Thinking-2507 on popular benchmarks across different categories. On an 8K input and 16K output setting using a single H200, it delivers inference throughput 3.3 times higher than Qwen3-30B-A3B and 2.2 times higher than GPT-OSS-20B. Nemotron 3 Nano supports context lengths up to 1 million tokens and is reported to outperform GPT-OSS-20B and Qwen3-30B-A3B-Instruct-2507. -
45
Laguna M.1
Poolside
Laguna M.1 is Poolside’s most capable model for agentic coding, built and trained in-house for software development workflows. It is a 225B total-parameter Mixture of Experts model with 23B activated parameters, trained completely in-house on 30T tokens using 6,144 interconnected NVIDIA H200 GPUs. Poolside trained Laguna M.1 from scratch with its own data work, training codebase, and async on-policy reinforcement learning in its agent harness, all with agentic coding in mind. The model is designed to perform at its best inside Poolside’s coding agent, where it can reason through software tasks, interact with tools, edit code, run tests, and support longer autonomous development sessions. Laguna M.1 is built for developers and teams working on complex coding tasks that require stronger reasoning, architectural understanding, terminal use, and multi-step execution than lightweight models can provide.Starting Price: Free -
46
Mistral NeMo
Mistral AI
Mistral NeMo, our new best small model. A state-of-the-art 12B model with 128k context length, and released under the Apache 2.0 license. Mistral NeMo is a 12B model built in collaboration with NVIDIA. Mistral NeMo offers a large context window of up to 128k tokens. Its reasoning, world knowledge, and coding accuracy are state-of-the-art in its size category. As it relies on standard architecture, Mistral NeMo is easy to use and a drop-in replacement in any system using Mistral 7B. We have released pre-trained base and instruction-tuned checkpoints under the Apache 2.0 license to promote adoption for researchers and enterprises. Mistral NeMo was trained with quantization awareness, enabling FP8 inference without any performance loss. The model is designed for global, multilingual applications. It is trained on function calling and has a large context window. Compared to Mistral 7B, it is much better at following precise instructions, reasoning, and handling multi-turn conversations.Starting Price: Free -
47
GPT-NeoX
EleutherAI
An implementation of model parallel autoregressive transformers on GPUs, based on the DeepSpeed library. This repository records EleutherAI's library for training large-scale language models on GPUs. Our current framework is based on NVIDIA's Megatron Language Model and has been augmented with techniques from DeepSpeed as well as some novel optimizations. We aim to make this repo a centralized and accessible place to gather techniques for training large-scale autoregressive language models, and accelerate research into large-scale training.Starting Price: Free -
48
GLM-OCR
Z.ai
GLM-OCR is a multimodal optical character recognition model and open source repository that provides accurate, efficient, and comprehensive document understanding by combining text and visual modalities into a unified encoder–decoder architecture derived from the GLM-V family. Built with a visual encoder pre-trained on large-scale image–text data and a lightweight cross-modal connector feeding into a GLM-0.5B language decoder, the model supports layout detection, parallel region recognition, and structured output for text, tables, formulas, and complicated real-world document formats. It introduces Multi-Token Prediction (MTP) loss and stable full-task reinforcement learning to improve training efficiency, recognition accuracy, and generalization, achieving state-of-the-art benchmarks on major document understanding tasks.Starting Price: Free -
49
NVIDIA Alpamayo
NVIDIA
NVIDIA Alpamayo is an open ecosystem of AI models, simulation tools, and datasets designed to accelerate the development of autonomous vehicles with human-like reasoning capabilities. It is built around a family of Vision-Language-Action (VLA) models that combine visual perception, language-based reasoning, and action planning, enabling vehicles to interpret complex driving environments and make decisions step by step. Unlike traditional systems that rely mainly on pattern recognition, Alpamayo introduces chain-of-thought reasoning, allowing autonomous systems to understand rare or unpredictable “long-tail” scenarios and explain their decisions for improved safety and transparency. It integrates seamlessly with NVIDIA’s full autonomous driving stack, covering training, simulation, and deployment, so developers can build advanced systems without creating core infrastructure from scratch. -
50
MercuryDPM
MercuryDPM
MercuryDPM is an open source code for discrete particle simulations, designed to simulate the motion of particles or atoms by applying forces and torques from external body forces, such as gravity or magnetic fields, and from particle interaction laws. For granular particles, these forces are typically contact forces, including elastic, plastic, viscous, and frictional interactions, while molecular simulations can use interaction potentials such as Lennard-Jones. MercuryDPM is written as a versatile, object-oriented C++ code and is built to be understandable, flexible, and extensible for researchers and engineers who need to create new simulation models. It is developed extensively for granular applications, while remaining adaptable to other particle-based systems and long-range interactions. Its documentation guides users through installation, running simulations, visualization, analysis, and creating new MercuryDPM codes to model systems of their choice.Starting Price: Free
