Alternatives to Qwen2.5-Max
Compare Qwen2.5-Max alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to Qwen2.5-Max in 2026. Compare features, ratings, user reviews, pricing, and more from Qwen2.5-Max competitors and alternatives in order to make an informed decision for your business.
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1
QwQ-Max-Preview
Alibaba
QwQ-Max-Preview is an advanced AI model built on the Qwen2.5-Max architecture, designed to excel in deep reasoning, mathematical problem-solving, coding, and agent-related tasks. This preview version offers a sneak peek at its capabilities, which include improved performance in a wide range of general-domain tasks and the ability to handle complex workflows. QwQ-Max-Preview is slated for an official open-source release under the Apache 2.0 license, offering further advancements and refinements in its full version. It also paves the way for a more accessible AI ecosystem, with the upcoming launch of the Qwen Chat app and smaller variants of the model like QwQ-32B, aimed at developers seeking local deployment options.Starting Price: Free -
2
Tülu 3
Ai2
Tülu 3 is an advanced instruction-following language model developed by the Allen Institute for AI (Ai2), designed to enhance capabilities in areas such as knowledge, reasoning, mathematics, coding, and safety. Built upon the Llama 3 Base, Tülu 3 employs a comprehensive four-stage post-training process: meticulous prompt curation and synthesis, supervised fine-tuning on a diverse set of prompts and completions, preference tuning using both off- and on-policy data, and a novel reinforcement learning approach to bolster specific skills with verifiable rewards. This open-source model distinguishes itself by providing full transparency, including access to training data, code, and evaluation tools, thereby closing the performance gap between open and proprietary fine-tuning methods. Evaluations indicate that Tülu 3 outperforms other open-weight models of similar size, such as Llama 3.1-Instruct and Qwen2.5-Instruct, across various benchmarks.Starting Price: Free -
3
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. -
4
ERNIE 4.5
Baidu
ERNIE 4.5 is a cutting-edge conversational AI platform developed by Baidu, leveraging advanced natural language processing (NLP) models to enable highly sophisticated human-like interactions. The platform is part of Baidu’s ERNIE (Enhanced Representation through Knowledge Integration) series, which integrates multimodal capabilities, including text, image, and voice. ERNIE 4.5 enhances the ability of AI models to understand complex context and deliver more accurate, nuanced responses, making it suitable for various applications, from customer service and virtual assistants to content creation and enterprise-level automation.Starting Price: $0.55 per 1M tokens -
5
ERNIE X1
Baidu
ERNIE X1 is an advanced conversational AI model developed by Baidu as part of their ERNIE (Enhanced Representation through Knowledge Integration) series. Unlike previous versions, ERNIE X1 is designed to be more efficient in understanding and generating human-like responses. It incorporates cutting-edge machine learning techniques to handle complex queries, making it capable of not only processing text but also generating images and engaging in multimodal communication. ERNIE X1 is often used in natural language processing applications such as chatbots, virtual assistants, and enterprise automation, offering significant improvements in accuracy, contextual understanding, and response quality.Starting Price: $0.28 per 1M tokens -
6
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 -
7
GPT-4.5
OpenAI
GPT-4.5 is a powerful AI model that improves upon its predecessor by scaling unsupervised learning, enhancing reasoning abilities, and offering improved collaboration capabilities. Designed to better understand human intent and collaborate in more natural, intuitive ways, GPT-4.5 delivers higher accuracy and lower hallucination rates across a broad range of topics. Its advanced capabilities enable it to generate creative and insightful content, solve complex problems, and assist with tasks in writing, design, and even space exploration. With improved AI-human interactions, GPT-4.5 is optimized for practical applications, making it more accessible and reliable for businesses and developers.Starting Price: $75.00 / 1M tokens -
8
Gemini 2.5 Pro
Google
Gemini 2.5 Pro is an advanced AI model designed to handle complex tasks with enhanced reasoning and coding capabilities. Leading common benchmarks, it excels in math, science, and coding, demonstrating strong performance in tasks like web app creation and code transformation. Built on the Gemini 2.5 foundation, it features a 1 million token context window, enabling it to process vast datasets from various sources such as text, images, and code repositories. Available now in Google AI Studio, Gemini 2.5 Pro is optimized for more sophisticated applications and supports advanced users with improved performance for complex problem-solving.Starting Price: $19.99/month -
9
Hunyuan-TurboS
Tencent
Tencent's Hunyuan-TurboS is a next-generation AI model designed to offer rapid responses and outstanding performance in various domains such as knowledge, mathematics, and creative tasks. Unlike previous models that require "slow thinking," Hunyuan-TurboS enhances response speed, doubling word output speed and reducing first-word latency by 44%. Through innovative architecture, it provides superior performance while lowering deployment costs. This model combines fast thinking (intuition-based responses) with slow thinking (logical analysis), ensuring quicker, more accurate solutions across diverse scenarios. Hunyuan-TurboS excels in benchmarks, competing with leading models like GPT-4 and DeepSeek V3, making it a breakthrough in AI-driven performance. -
10
OpenAI deep research
OpenAI
OpenAI's deep research is an AI-powered tool designed to autonomously conduct complex, multi-step research tasks across various domains, such as science, coding, and mathematics. By analyzing user-provided inputs—such as questions, text documents, images, PDFs, or spreadsheets—the system formulates a structured research plan, gathers relevant information, and delivers comprehensive responses within minutes. It also provides process summaries with citations, helping users verify sources. While this tool significantly accelerates research efficiency, it may occasionally produce inaccuracies or struggle to differentiate between authoritative sources and misinformation. Currently available to ChatGPT Pro users, deep research represents a step toward AI-driven knowledge discovery, with ongoing improvements planned for accuracy and response time. -
11
OpenAI o3-mini-high
OpenAI
The o3-mini-high model from OpenAI advances AI reasoning by refining deep problem-solving in coding, mathematics, and complex tasks. It features adaptive thinking time with adjustable reasoning modes (low, medium, high) to optimize performance based on task complexity. Outperforming the o1 series by 200 Elo points on Codeforces, it delivers high efficiency at a lower cost while maintaining speed and accuracy. As part of the o3 family, it pushes AI problem-solving boundaries while remaining accessible, offering a free tier and expanded limits for Plus subscribers. -
12
Qwen2
Alibaba
Qwen2 is the large language model series developed by Qwen team, Alibaba Cloud. Qwen2 is a series of large language models developed by the Qwen team at Alibaba Cloud. It includes both base language models and instruction-tuned models, ranging from 0.5 billion to 72 billion parameters, and features both dense models and a Mixture-of-Experts model. The Qwen2 series is designed to surpass most previous open-weight models, including its predecessor Qwen1.5, and to compete with proprietary models across a broad spectrum of benchmarks in language understanding, generation, multilingual capabilities, coding, mathematics, and reasoning.Starting Price: Free -
13
Qwen3-Coder
Qwen
Qwen3‑Coder is an agentic code model available in multiple sizes, led by the 480B‑parameter Mixture‑of‑Experts variant (35B active) that natively supports 256K‑token contexts (extendable to 1M) and achieves state‑of‑the‑art results comparable to Claude Sonnet 4. Pre‑training on 7.5T tokens (70 % code) and synthetic data cleaned via Qwen2.5‑Coder optimized both coding proficiency and general abilities, while post‑training employs large‑scale, execution‑driven reinforcement learning, scaling test‑case generation for diverse coding challenges, and long‑horizon RL across 20,000 parallel environments to excel on multi‑turn software‑engineering benchmarks like SWE‑Bench Verified without test‑time scaling. Alongside the model, the open source Qwen Code CLI (forked from Gemini Code) unleashes Qwen3‑Coder in agentic workflows with customized prompts, function calling protocols, and seamless integration with Node.js, OpenAI SDKs, and environment variables.Starting Price: Free -
14
Qwen-7B
Alibaba
Qwen-7B is the 7B-parameter version of the large language model series, Qwen (abbr. Tongyi Qianwen), proposed by Alibaba Cloud. Qwen-7B is a Transformer-based large language model, which is pretrained on a large volume of data, including web texts, books, codes, etc. Additionally, based on the pretrained Qwen-7B, we release Qwen-7B-Chat, a large-model-based AI assistant, which is trained with alignment techniques. The features of the Qwen-7B series include: Trained with high-quality pretraining data. We have pretrained Qwen-7B on a self-constructed large-scale high-quality dataset of over 2.2 trillion tokens. The dataset includes plain texts and codes, and it covers a wide range of domains, including general domain data and professional domain data. Strong performance. In comparison with the models of the similar model size, we outperform the competitors on a series of benchmark datasets, which evaluates natural language understanding, mathematics, coding, etc. And more.Starting Price: Free -
15
Qwen3
Alibaba
Qwen3, the latest iteration of the Qwen family of large language models, introduces groundbreaking features that enhance performance across coding, math, and general capabilities. With models like the Qwen3-235B-A22B and Qwen3-30B-A3B, Qwen3 achieves impressive results compared to top-tier models, thanks to its hybrid thinking modes that allow users to control the balance between deep reasoning and quick responses. The platform supports 119 languages and dialects, making it an ideal choice for global applications. Its pre-training process, which uses 36 trillion tokens, enables robust performance, and advanced reinforcement learning (RL) techniques continue to refine its capabilities. Available on platforms like Hugging Face and ModelScope, Qwen3 offers a powerful tool for developers and researchers working in diverse fields.Starting Price: Free -
16
Qwen3-Max
Alibaba
Qwen3-Max is Alibaba’s latest trillion-parameter large language model, designed to push performance in agentic tasks, coding, reasoning, and long-context processing. It is built atop the Qwen3 family and benefits from the architectural, training, and inference advances introduced there; mixing thinker and non-thinker modes, a “thinking budget” mechanism, and support for dynamic mode switching based on complexity. The model reportedly processes extremely long inputs (hundreds of thousands of tokens), supports tool invocation, and exhibits strong performance on benchmarks in coding, multi-step reasoning, and agent benchmarks (e.g., Tau2-Bench). While its initial variant emphasizes instruction following (non-thinking mode), Alibaba plans to bring reasoning capabilities online to enable autonomous agent behavior. Qwen3-Max inherits multilingual support and extensive pretraining on trillions of tokens, and it is delivered via API interfaces compatible with OpenAI-style functions.Starting Price: Free -
17
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 -
18
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 -
19
Qwen
Alibaba
Qwen is a powerful, free AI assistant built on the advanced Qwen model series, designed to help anyone with creativity, research, problem-solving, and everyday tasks. While Qwen Chat is the main interface for most users, Qwen itself powers a broad range of intelligent capabilities including image generation, deep research, website creation, advanced reasoning, and context-aware search. Its multimodal intelligence enables Qwen to understand and process text, images, audio, and video simultaneously for richer insights. Qwen is available on web, desktop, and mobile, ensuring seamless access across all devices. For developers, the Qwen API provides OpenAI-compatible endpoints, making integration simple and allowing Qwen’s intelligence to power apps, services, and automation. Whether you're chatting through Qwen Chat or building with the Qwen API, Qwen delivers fast, flexible, and highly capable AI support.Starting Price: Free -
20
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 -
21
Qwen Code
Qwen
Qwen3‑Coder is an agentic code model available in multiple sizes, led by the 480B‑parameter Mixture‑of‑Experts variant (35B active) that natively supports 256K‑token contexts (extendable to 1M) and achieves state‑of‑the‑art results on Agentic Coding, Browser‑Use, and Tool‑Use tasks comparable to Claude Sonnet 4. Pre‑training on 7.5T tokens (70 % code) and synthetic data cleaned via Qwen2.5‑Coder optimized both coding proficiency and general abilities, while post‑training employs large‑scale, execution‑driven reinforcement learning and long‑horizon RL across 20,000 parallel environments to excel on multi‑turn software‑engineering benchmarks like SWE‑Bench Verified without test‑time scaling. Alongside the model, the open source Qwen Code CLI (forked from Gemini Code) unleashes Qwen3‑Coder in agentic workflows with customized prompts, function calling protocols, and seamless integration with Node.js, OpenAI SDKs, and more.Starting Price: Free -
22
Qwen3-Max-Thinking
Alibaba
Qwen3-Max-Thinking is Alibaba’s latest flagship reasoning-enhanced large language model, built as an extension of the Qwen3-Max family and designed to deliver state-of-the-art analytical performance and multi-step reasoning capabilities. It scales up from one of the largest parameter bases in the Qwen ecosystem and incorporates advanced reinforcement learning and adaptive tool integration so the model can leverage search, memory, and code interpreter functions dynamically during inference to address difficult multi-stage tasks with higher accuracy and contextual depth compared with standard generative responses. Qwen3-Max-Thinking introduces a unique Thinking Mode that exposes deliberate, step-by-step reasoning before final outputs, enabling transparency and traceability of logical chains, and can be tuned with configurable “thinking budgets” to balance performance quality with computational cost. -
23
DeepCoder
Agentica Project
DeepCoder is a fully open source code-reasoning and generation model released by Agentica Project in collaboration with Together AI. It is fine-tuned from DeepSeek-R1-Distilled-Qwen-14B using distributed reinforcement learning, achieving a 60.6% accuracy on LiveCodeBench (representing an 8% improvement over the base), a performance level that matches that of proprietary models such as o3-mini (2025-01-031 Low) and o1 while using only 14 billion parameters. It was trained over 2.5 weeks on 32 H100 GPUs with a curated dataset of roughly 24,000 coding problems drawn from verified sources (including TACO-Verified, PrimeIntellect SYNTHETIC-1, and LiveCodeBench submissions), each problem requiring a verifiable solution and at least five unit tests to ensure reliability for RL training. To handle long-range context, DeepCoder employs techniques such as iterative context lengthening and overlong filtering.Starting Price: Free -
24
Qwen2-VL
Alibaba
Qwen2-VL is the latest version of the vision language models based on Qwen2 in the Qwen model familities. Compared with Qwen-VL, Qwen2-VL has the capabilities of: SoTA understanding of images of various resolution & ratio: Qwen2-VL achieves state-of-the-art performance on visual understanding benchmarks, including MathVista, DocVQA, RealWorldQA, MTVQA, etc. Understanding videos of 20 min+: Qwen2-VL can understand videos over 20 minutes for high-quality video-based question answering, dialog, content creation, etc. Agent that can operate your mobiles, robots, etc.: with the abilities of complex reasoning and decision making, Qwen2-VL can be integrated with devices like mobile phones, robots, etc., for automatic operation based on visual environment and text instructions. Multilingual Support: to serve global users, besides English and Chinese, Qwen2-VL now supports the understanding of texts in different languages inside imagesStarting Price: Free -
25
Qwen3-Coder-Next
Alibaba
Qwen3-Coder-Next is an open-weight language model specifically designed for coding agents and local development that delivers advanced coding reasoning, complex tool usage, and robust performance on long-horizon programming tasks with high efficiency, using a mixture-of-experts architecture that balances powerful capabilities with resource-friendly operation. It provides enhanced agentic coding abilities that help software developers, AI system builders, and automated coding workflows generate, debug, and reason about code with deep contextual understanding while recovering from execution errors, making it well-suited for autonomous coding agents and development-oriented applications. By achieving strong performance comparable to much larger parameter models while requiring fewer active parameters, Qwen3-Coder-Next enables cost-effective deployment for dynamic and complex programming workloads in research and production environments.Starting Price: Free -
26
QwQ-32B
Alibaba
QwQ-32B is an advanced reasoning model developed by Alibaba Cloud's Qwen team, designed to enhance AI's problem-solving capabilities. With 32 billion parameters, it achieves performance comparable to state-of-the-art models like DeepSeek's R1, which has 671 billion parameters. This efficiency is achieved through optimized parameter utilization, allowing QwQ-32B to perform complex tasks such as mathematical reasoning, coding, and general problem-solving with fewer resources. The model supports a context length of up to 32,000 tokens, enabling it to process extensive input data effectively. QwQ-32B is accessible via Alibaba's chatbot service, Qwen Chat, and is open sourced under the Apache 2.0 license, promoting collaboration and further development within the AI community.Starting Price: Free -
27
Qwen2.5-1M
Alibaba
Qwen2.5-1M is an open-source language model developed by the Qwen team, designed to handle context lengths of up to one million tokens. This release includes two model variants, Qwen2.5-7B-Instruct-1M and Qwen2.5-14B-Instruct-1M, marking the first time Qwen models have been upgraded to support such extensive context lengths. To facilitate efficient deployment, the team has also open-sourced an inference framework based on vLLM, integrated with sparse attention methods, enabling processing of 1M-token inputs with a 3x to 7x speed improvement. Comprehensive technical details, including design insights and ablation experiments, are available in the accompanying technical report.Starting Price: Free -
28
DeepScaleR
Agentica Project
DeepScaleR is a 1.5-billion-parameter language model fine-tuned from DeepSeek-R1-Distilled-Qwen-1.5B using distributed reinforcement learning and a novel iterative context-lengthening strategy that gradually increases its context window from 8K to 24K tokens during training. It was trained on ~40,000 carefully curated mathematical problems drawn from competition-level datasets like AIME (1984–2023), AMC (pre-2023), Omni-MATH, and STILL. DeepScaleR achieves 43.1% accuracy on AIME 2024, a roughly 14.3 percentage point boost over the base model, and surpasses the performance of the proprietary O1-Preview model despite its much smaller size. It also posts strong results on a suite of math benchmarks (e.g., MATH-500, AMC 2023, Minerva Math, OlympiadBench), demonstrating that small, efficient models tuned with RL can match or exceed larger baselines on reasoning tasks.Starting Price: Free -
29
Sky-T1
NovaSky
Sky-T1-32B-Preview is an open source reasoning model developed by the NovaSky team at UC Berkeley's Sky Computing Lab. It matches the performance of proprietary models like o1-preview on reasoning and coding benchmarks, yet was trained for under $450, showcasing the feasibility of cost-effective, high-level reasoning capabilities. The model was fine-tuned from Qwen2.5-32B-Instruct using a curated dataset of 17,000 examples across diverse domains, including math and coding. The training was completed in 19 hours on eight H100 GPUs with DeepSpeed Zero-3 offloading. All aspects of the project, including data, code, and model weights, are fully open-source, empowering the academic and open-source communities to replicate and enhance the model's performance.Starting Price: Free -
30
GLM-5
Zhipu AI
GLM-5 is Z.ai’s latest large language model built for complex systems engineering and long-horizon agentic tasks. It scales significantly beyond GLM-4.5, increasing total parameters and training data while integrating DeepSeek Sparse Attention to reduce deployment costs without sacrificing long-context capacity. The model combines enhanced pre-training with a new asynchronous reinforcement learning infrastructure called slime, improving training efficiency and post-training refinement. GLM-5 achieves best-in-class performance among open-source models across reasoning, coding, and agent benchmarks, narrowing the gap with leading frontier models. It ranks highly on evaluations such as Vending Bench 2, demonstrating strong long-term planning and operational capabilities. The model is open-sourced under the MIT License.Starting Price: Free -
31
Smaug-72B
Abacus
Smaug-72B is a powerful open-source large language model (LLM) known for several key features: High Performance: It currently holds the top spot on the Hugging Face Open LLM leaderboard, surpassing models like GPT-3.5 in various benchmarks. This means it excels at tasks like understanding, responding to, and generating human-like text. Open Source: Unlike many other advanced LLMs, Smaug-72B is freely available for anyone to use and modify, fostering collaboration and innovation in the AI community. Focus on Reasoning and Math: It specifically shines in handling reasoning and mathematical tasks, attributing this strength to unique fine-tuning techniques developed by Abacus AI, the creators of Smaug-72B. Based on Qwen-72B: It's technically a fine-tuned version of another powerful LLM called Qwen-72B, released by Alibaba, further improving upon its capabilities. Overall, Smaug-72B represents a significant step forward in open-source AI.Starting Price: Free -
32
Claude Opus 3
Anthropic
Opus, our most intelligent model, outperforms its peers on most of the common evaluation benchmarks for AI systems, including undergraduate level expert knowledge (MMLU), graduate level expert reasoning (GPQA), basic mathematics (GSM8K), and more. It exhibits near-human levels of comprehension and fluency on complex tasks, leading the frontier of general intelligence. All Claude 3 models show increased capabilities in analysis and forecasting, nuanced content creation, code generation, and conversing in non-English languages like Spanish, Japanese, and French.Starting Price: Free -
33
Qwen2.5-VL
Alibaba
Qwen2.5-VL is the latest vision-language model from the Qwen series, representing a significant advancement over its predecessor, Qwen2-VL. This model excels in visual understanding, capable of recognizing a wide array of objects, including text, charts, icons, graphics, and layouts within images. It functions as a visual agent, capable of reasoning and dynamically directing tools, enabling applications such as computer and phone usage. Qwen2.5-VL can comprehend videos exceeding one hour in length and can pinpoint relevant segments within them. Additionally, it accurately localizes objects in images by generating bounding boxes or points and provides stable JSON outputs for coordinates and attributes. The model also supports structured outputs for data like scanned invoices, forms, and tables, benefiting sectors such as finance and commerce. Available in base and instruct versions across 3B, 7B, and 72B sizes, Qwen2.5-VL is accessible through platforms like Hugging Face and ModelScope.Starting Price: Free -
34
Kimi K2
Moonshot AI
Kimi K2 is a state-of-the-art open source large language model series built on a mixture-of-experts (MoE) architecture, featuring 1 trillion total parameters and 32 billion activated parameters for task-specific efficiency. Trained with the Muon optimizer on over 15.5 trillion tokens and stabilized by MuonClip’s attention-logit clamping, it delivers exceptional performance in frontier knowledge, reasoning, mathematics, coding, and general agentic workflows. Moonshot AI provides two variants, Kimi-K2-Base for research-level fine-tuning and Kimi-K2-Instruct pre-trained for immediate chat and tool-driven interactions, enabling both custom development and drop-in agentic capabilities. Benchmarks show it outperforms leading open source peers and rivals top proprietary models in coding tasks and complex task breakdowns, while its 128 K-token context length, tool-calling API compatibility, and support for industry-standard inference engines.Starting Price: Free -
35
DeepSeek-V2
DeepSeek
DeepSeek-V2 is a state-of-the-art Mixture-of-Experts (MoE) language model introduced by DeepSeek-AI, characterized by its economical training and efficient inference capabilities. With a total of 236 billion parameters, of which only 21 billion are active per token, it supports a context length of up to 128K tokens. DeepSeek-V2 employs innovative architectures like Multi-head Latent Attention (MLA) for efficient inference by compressing the Key-Value (KV) cache and DeepSeekMoE for cost-effective training through sparse computation. This model significantly outperforms its predecessor, DeepSeek 67B, by saving 42.5% in training costs, reducing the KV cache by 93.3%, and enhancing generation throughput by 5.76 times. Pretrained on an 8.1 trillion token corpus, DeepSeek-V2 excels in language understanding, coding, and reasoning tasks, making it a top-tier performer among open-source models.Starting Price: Free -
36
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. -
37
Qwen2.5-VL-32B
Alibaba
Qwen2.5-VL-32B is a state-of-the-art AI model designed for multimodal tasks, offering advanced capabilities in both text and image reasoning. It builds upon the earlier Qwen2.5-VL series, improving response quality with more human-like, formatted answers. The model excels in mathematical reasoning, fine-grained image understanding, and complex, multi-step reasoning tasks, such as those found in MathVista and MMMU benchmarks. Its superior performance has been demonstrated in comparison to other models, outperforming the larger Qwen2-VL-72B in certain areas. With improved image parsing and visual logic deduction, Qwen2.5-VL-32B provides a detailed, accurate analysis of images and can generate responses based on complex visual inputs. It has been optimized for both text and image tasks, making it ideal for applications requiring sophisticated reasoning and understanding across different media. -
38
DeepSWE
Agentica Project
DeepSWE is a fully open source, state-of-the-art coding agent built on top of the Qwen3-32B foundation model and trained exclusively via reinforcement learning (RL), without supervised finetuning or distillation from proprietary models. It is developed using rLLM, Agentica’s open source RL framework for language agents. DeepSWE operates as an agent; it interacts with a simulated development environment (via the R2E-Gym environment) using a suite of tools (file editor, search, shell-execution, submit/finish), enabling it to navigate codebases, edit multiple files, compile/run tests, and iteratively produce patches or complete engineering tasks. DeepSWE exhibits emergent behaviors beyond simple code generation; when presented with bugs or feature requests, the agent reasons about edge cases, seeks existing tests in the repository, proposes patches, writes extra tests for regressions, and dynamically adjusts its “thinking” effort.Starting Price: Free -
39
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 -
40
Llama 2
Meta
The next generation of our open source large language model. This release includes model weights and starting code for pretrained and fine-tuned Llama language models — ranging from 7B to 70B parameters. Llama 2 pretrained models are trained on 2 trillion tokens, and have double the context length than Llama 1. Its fine-tuned models have been trained on over 1 million human annotations. Llama 2 outperforms other open source language models on many external benchmarks, including reasoning, coding, proficiency, and knowledge tests. Llama 2 was pretrained on publicly available online data sources. The fine-tuned model, Llama-2-chat, leverages publicly available instruction datasets and over 1 million human annotations. We have a broad range of supporters around the world who believe in our open approach to today’s AI — companies that have given early feedback and are excited to build with Llama 2.Starting Price: Free -
41
Athene-V2
Nexusflow
Athene-V2 is Nexusflow's latest 72-billion-parameter model suite, fine-tuned from Qwen 2.5 72B, designed to compete with GPT-4o across key capabilities. This suite includes Athene-V2-Chat-72B, a state-of-the-art chat model that matches GPT-4o in multiple benchmarks, excelling in chat helpfulness (Arena-Hard), code completion (ranking #2 on bigcode-bench-hard), mathematics (MATH), and precise long log extraction. Additionally, Athene-V2-Agent-72B balances chat and agent functionalities, offering concise, directive responses and surpassing GPT-4o in Nexus-V2 function calling benchmarks focused on complex enterprise-level use cases. These advancements underscore the industry's shift from merely scaling model sizes to specialized customization, illustrating how targeted post-training processes can finely optimize models for distinct skills and applications. -
42
Qwen2.5
Alibaba
Qwen2.5 is an advanced multimodal AI model designed to provide highly accurate and context-aware responses across a wide range of applications. It builds on the capabilities of its predecessors, integrating cutting-edge natural language understanding with enhanced reasoning, creativity, and multimodal processing. Qwen2.5 can seamlessly analyze and generate text, interpret images, and interact with complex data to deliver precise solutions in real time. Optimized for adaptability, it excels in personalized assistance, data analysis, creative content generation, and academic research, making it a versatile tool for professionals and everyday users alike. Its user-centric design emphasizes transparency, efficiency, and alignment with ethical AI practices.Starting Price: Free -
43
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. -
44
DeepSeek R1
DeepSeek
DeepSeek-R1 is an advanced open-source reasoning model developed by DeepSeek, designed to rival OpenAI's Model o1. Accessible via web, app, and API, it excels in complex tasks such as mathematics and coding, demonstrating superior performance on benchmarks like the American Invitational Mathematics Examination (AIME) and MATH. DeepSeek-R1 employs a mixture of experts (MoE) architecture with 671 billion total parameters, activating 37 billion parameters per token, enabling efficient and accurate reasoning capabilities. This model is part of DeepSeek's commitment to advancing artificial general intelligence (AGI) through open-source innovation.Starting Price: Free -
45
Qwen3-TTS
Alibaba
Qwen3-TTS is an open source series of advanced text-to-speech models developed by the Qwen team at Alibaba Cloud under the Apache-2.0 license, offering stable, expressive, and real-time speech generation with features such as voice cloning, voice design, and fine-grained control of prosody and acoustic attributes. The models support 10 major languages, including Chinese, English, Japanese, Korean, German, French, Russian, Portuguese, Spanish, and Italian, and multiple dialectal voice profiles with adaptive control over tone, speaking rate, and emotional expression based on text semantics and instructions. Qwen3-TTS uses efficient tokenization and a dual-track architecture that enables ultra-low-latency streaming synthesis (first audio packet in ~97 ms), making it suitable for interactive and real-time use cases, and includes a range of models with different capabilities (e.g., rapid 3-second voice cloning, custom voice timbres, and instruction-based voice design).Starting Price: Free -
46
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 -
47
Claude Sonnet 3.5
Anthropic
Claude Sonnet 3.5 sets new industry benchmarks for graduate-level reasoning (GPQA), undergraduate-level knowledge (MMLU), and coding proficiency (HumanEval). It shows marked improvement in grasping nuance, humor, and complex instructions, and is exceptional at writing high-quality content with a natural, relatable tone. Claude Sonnet 3.5 operates at twice the speed of Claude Opus 3. This performance boost, combined with cost-effective pricing, makes Claude Sonnet 3.5 ideal for complex tasks such as context-sensitive customer support and orchestrating multi-step workflows. Claude Sonnet 3.5 is now available for free on Claude.ai and the Claude iOS app, while Claude Pro and Team plan subscribers can access it with significantly higher rate limits. It is also available via the Anthropic API, Amazon Bedrock, and Google Cloud’s Vertex AI. The model costs $3 per million input tokens and $15 per million output tokens, with a 200K token context window.Starting Price: Free -
48
Alibaba Cloud Model Studio
Alibaba
Model Studio is Alibaba Cloud’s one-stop generative AI platform that lets developers build intelligent, business-aware applications using industry-leading foundation models like Qwen-Max, Qwen-Plus, Qwen-Turbo, the Qwen-2/3 series, visual-language models (Qwen-VL/Omni), and the video-focused Wan series. Users can access these powerful GenAI models through familiar OpenAI-compatible APIs or purpose-built SDKs, no infrastructure setup required. It supports a full development workflow, experiment with models in the playground, perform real-time and batch inferences, fine-tune with tools like SFT or LoRA, then evaluate, compress, accelerate deployment, and monitor performance, all within an isolated Virtual Private Cloud (VPC) for enterprise-grade security. Customization is simplified via one-click Retrieval-Augmented Generation (RAG), enabling integration of business data into model outputs. Visual, template-driven interfaces facilitate prompt engineering and application design. -
49
Alibaba AI Coding Plan
Alibaba Cloud
Alibaba Cloud’s AI Scene Coding campaign introduces a cloud-based development environment designed to help developers write, test, and deploy software faster using advanced AI coding models. It provides access to powerful models such as Qwen3-Coder-Plus and integrates with popular developer tools, including Cline, Claude Code, Qwen Code, and OpenClaw, allowing engineers to use their preferred coding interfaces while leveraging Alibaba Cloud’s AI infrastructure. It is built to streamline software development by combining large language models with cloud computing resources so developers can generate code, analyze projects, and automate development workflows from a unified environment. These AI models are capable of understanding prompts, writing code, debugging programs, and assisting with complex development tasks, allowing applications to be built in minutes rather than through traditional manual coding cycles.Starting Price: $3 per month -
50
GLM-4.6
Zhipu AI
GLM-4.6 advances upon its predecessor with stronger reasoning, coding, and agentic capabilities: it demonstrates clear improvements in inferential performance, supports tool use during inference, and more effectively integrates into agent frameworks. In benchmark tests spanning reasoning, coding, and agents, GLM-4.6 outperforms GLM-4.5 and shows competitive strength against models such as DeepSeek-V3.2-Exp and Claude Sonnet 4, though it still trails Claude Sonnet 4.5 in pure coding performance. In real-world tests using an extended “CC-Bench” suite across front-end development, tool building, data analysis, and algorithmic tasks, GLM-4.6 beats GLM-4.5 and approaches parity with Claude Sonnet 4, winning ~48.6% of head-to-head comparisons, while also achieving ~15% better token efficiency. GLM-4.6 is available via the Z.ai API, and developers can integrate it as an LLM backend or agent core using the platform’s API.Starting Price: Free
