Alternatives to GLM-4.7-FlashX
Compare GLM-4.7-FlashX alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to GLM-4.7-FlashX in 2026. Compare features, ratings, user reviews, pricing, and more from GLM-4.7-FlashX competitors and alternatives in order to make an informed decision for your business.
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GLM-4.7-Flash
Z.ai
GLM-4.7 Flash is a lightweight variant of GLM-4.7, Z.ai’s flagship large language model designed for advanced coding, reasoning, and multi-step task execution with strong agentic performance and a very large context window. It is an MoE-based model optimized for efficient inference that balances performance and resource use, enabling deployment on local machines with moderate memory requirements while maintaining deep reasoning, coding, and agentic task abilities. GLM-4.7 itself advances over earlier generations with enhanced programming capabilities, stable multi-step reasoning, context preservation across turns, and improved tool-calling workflows, and supports very long context lengths (up to ~200 K tokens) for complex tasks that span large inputs or outputs. The Flash variant retains many of these strengths in a smaller footprint, offering competitive benchmark performance in coding and reasoning tasks for models in its size class.Starting Price: Free -
2
GLM-4.5V-Flash
Zhipu AI
GLM-4.5V-Flash is an open source vision-language model, designed to bring strong multimodal capabilities into a lightweight, deployable package. It supports image, video, document, and GUI inputs, enabling tasks such as scene understanding, chart and document parsing, screen reading, and multi-image analysis. Compared to larger models in the series, GLM-4.5V-Flash offers a compact footprint while retaining core VLM capabilities like visual reasoning, video understanding, GUI task handling, and complex document parsing. It can serve in “GUI agent” workflows, meaning it can interpret screenshots or desktop captures, recognize icons or UI elements, and assist with automated desktop or web-based tasks. Although it forgoes some of the largest-model performance gains, GLM-4.5V-Flash remains versatile for real-world multimodal tasks where efficiency, lower resource usage, and broad modality support are prioritized.Starting Price: Free -
3
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 -
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Falcon 3
Technology Innovation Institute (TII)
Falcon 3 is an open-source large language model (LLM) developed by the Technology Innovation Institute (TII) to make advanced AI accessible to a broader audience. Designed for efficiency, it operates seamlessly on lightweight devices, including laptops, without compromising performance. The Falcon 3 ecosystem comprises four scalable models, each tailored to diverse applications, and supports multiple languages while optimizing resource usage. This latest iteration in TII's LLM series achieves state-of-the-art results in reasoning, language understanding, instruction following, code, and mathematics tasks. By combining high performance with resource efficiency, Falcon 3 aims to democratize access to AI, empowering users across various sectors to leverage advanced technology without the need for extensive computational resources.Starting Price: Free -
5
Gemini 2.0 Flash
Google
The Gemini 2.0 Flash AI model represents the next generation of high-speed, intelligent computing, designed to set new benchmarks in real-time language processing and decision-making. Building on the robust foundation of its predecessor, it incorporates enhanced neural architecture and breakthrough advancements in optimization, enabling even faster and more accurate responses. Gemini 2.0 Flash is designed for applications requiring instantaneous processing and adaptability, such as live virtual assistants, automated trading systems, and real-time analytics. Its lightweight, efficient design ensures seamless deployment across cloud, edge, and hybrid environments, while its improved contextual understanding and multitasking capabilities make it a versatile tool for tackling complex, dynamic workflows with precision and speed. -
6
Gemini 1.5 Flash
Google
The Gemini 1.5 Flash AI model is an advanced, high-speed language model engineered for lightning-fast processing and real-time responsiveness. Designed to excel in dynamic and time-sensitive applications, it combines streamlined neural architecture with cutting-edge optimization techniques to deliver exceptional performance without compromising on accuracy. Gemini 1.5 Flash is tailored for scenarios requiring rapid data processing, instant decision-making, and seamless multitasking, making it ideal for chatbots, customer support systems, and interactive applications. Its lightweight yet powerful design ensures it can be deployed efficiently across a range of platforms, from cloud-based environments to edge devices, enabling businesses to scale their operations with unmatched agility. -
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Gemini 3 Flash
Google
Gemini 3 Flash is Google’s latest AI model built to deliver frontier intelligence with exceptional speed and efficiency. It combines Pro-level reasoning with Flash-level latency, making advanced AI more accessible and affordable. The model excels in complex reasoning, multimodal understanding, and agentic workflows while using fewer tokens for everyday tasks. Gemini 3 Flash is designed to scale across consumer apps, developer tools, and enterprise platforms. It supports rapid coding, data analysis, video understanding, and interactive application development. By balancing performance, cost, and speed, Gemini 3 Flash redefines what fast AI can achieve. -
8
Gemini Flash
Google
Gemini Flash is an advanced large language model (LLM) from Google, specifically designed for high-speed, low-latency language processing tasks. Part of Google DeepMind’s Gemini series, Gemini Flash is tailored to provide real-time responses and handle large-scale applications, making it ideal for interactive AI-driven experiences such as customer support, virtual assistants, and live chat solutions. Despite its speed, Gemini Flash doesn’t compromise on quality; it’s built on sophisticated neural architectures that ensure responses remain contextually relevant, coherent, and precise. Google has incorporated rigorous ethical frameworks and responsible AI practices into Gemini Flash, equipping it with guardrails to manage and mitigate biased outputs, ensuring it aligns with Google’s standards for safe and inclusive AI. With Gemini Flash, Google empowers businesses and developers to deploy responsive, intelligent language tools that can meet the demands of fast-paced environments. -
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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 -
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MiniMax M2
MiniMax
MiniMax M2 is an open source foundation model built specifically for agentic applications and coding workflows, striking a new balance of performance, speed, and cost. It excels in end-to-end development scenarios, handling programming, tool-calling, and complex, long-chain workflows with capabilities such as Python integration, while delivering inference speeds of around 100 tokens per second and offering API pricing at just ~8% of the cost of comparable proprietary models. The model supports “Lightning Mode” for high-speed, lightweight agent tasks, and “Pro Mode” for in-depth full-stack development, report generation, and web-based tool orchestration; its weights are fully open source and available for local deployment with vLLM or SGLang. MiniMax M2 positions itself as a production-ready model that enables agents to complete independent tasks, such as data analysis, programming, tool orchestration, and large-scale multi-step logic at real organizational scale.Starting Price: $0.30 per million input tokens -
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Xiaomi MiMo
Xiaomi Technology
The Xiaomi MiMo API open platform is a developer-oriented interface for accessing and integrating Xiaomi’s MiMo family of AI models, including reasoning and language models such as MiMo-V2-Flash, into applications and services through standardized APIs and cloud endpoints, enabling developers to build AI-enabled features like conversational agents, reasoning workflows, code assistance, and search-augmented tasks without managing model infrastructure themselves. It offers REST-style API access with authentication, request signing, and structured responses so software can send prompts and receive generated text or processed outputs programmatically, and it supports common operations like text generation, prompt handling, and inference over MiMo models. By providing documentation and onboarding tools, the open platform lets teams integrate Xiaomi’s latest open source large language models, which leverage Mixture-of-Experts (MoE) architectures.Starting Price: Free -
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Hunyuan T1
Tencent
Hunyuan T1 is Tencent's deep-thinking AI model, now fully open to all users through the Tencent Yuanbao platform. This model excels in understanding multiple dimensions and potential logical relationships, making it suitable for handling complex tasks. Users can experience various AI models on the platform, including DeepSeek-R1 and Tencent Hunyuan Turbo. The official version of the Tencent Hunyuan T1 model will also be launched soon, providing external API access and other services. Built upon Tencent's Hunyuan large language model, Yuanbao excels in Chinese language understanding, logical reasoning, and task execution. It offers AI-based search, summaries, and writing capabilities, enabling users to analyze documents and engage in prompt-based interactions. -
13
Kimi K2.5
Moonshot AI
Kimi K2.5 is a next-generation multimodal AI model designed for advanced reasoning, coding, and visual understanding tasks. It features a native multimodal architecture that supports both text and visual inputs, enabling image and video comprehension alongside natural language processing. Kimi K2.5 delivers open-source state-of-the-art performance in agent workflows, software development, and general intelligence tasks. The model offers ultra-long context support with a 256K token window, making it suitable for large documents and complex conversations. It includes long-thinking capabilities that allow multi-step reasoning and tool invocation for solving challenging problems. Kimi K2.5 is fully compatible with the OpenAI API format, allowing developers to switch seamlessly with minimal changes. With strong performance, flexibility, and developer-focused tooling, Kimi K2.5 is built for production-grade AI applications.Starting Price: Free -
14
CodeGemma
Google
CodeGemma is a collection of powerful, lightweight models that can perform a variety of coding tasks like fill-in-the-middle code completion, code generation, natural language understanding, mathematical reasoning, and instruction following. CodeGemma has 3 model variants, a 7B pre-trained variant that specializes in code completion and generation from code prefixes and/or suffixes, a 7B instruction-tuned variant for natural language-to-code chat and instruction following; and a state-of-the-art 2B pre-trained variant that provides up to 2x faster code completion. Complete lines, and functions, and even generate entire blocks of code, whether you're working locally or using Google Cloud resources. Trained on 500 billion tokens of primarily English language data from web documents, mathematics, and code, CodeGemma models generate code that's not only more syntactically correct but also semantically meaningful, reducing errors and debugging time. -
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Megatron-Turing
NVIDIA
Megatron-Turing Natural Language Generation model (MT-NLG), is the largest and the most powerful monolithic transformer English language model with 530 billion parameters. This 105-layer, transformer-based MT-NLG improves upon the prior state-of-the-art models in zero-, one-, and few-shot settings. It demonstrates unmatched accuracy in a broad set of natural language tasks such as, Completion prediction, Reading comprehension, Commonsense reasoning, Natural language inferences, Word sense disambiguation, etc. With the intent of accelerating research on the largest English language model till date and enabling customers to experiment, employ and apply such a large language model on downstream language tasks - NVIDIA is pleased to announce an Early Access program for its managed API service to MT-NLG mode. -
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GLM-4.6V
Zhipu AI
GLM-4.6V is a state-of-the-art open source multimodal vision-language model from the Z.ai (GLM-V) family designed for reasoning, perception, and action. It ships in two variants: a full-scale version (106B parameters) for cloud or high-performance clusters, and a lightweight “Flash” variant (9B) optimized for local deployment or low-latency use. GLM-4.6V supports a native context window of up to 128K tokens during training, enabling it to process very long documents or multimodal inputs. Crucially, it integrates native Function Calling, meaning the model can take images, screenshots, documents, or other visual media as input directly (without manual text conversion), reason about them, and trigger tool calls, bridging “visual perception” with “executable action.” This enables a wide spectrum of capabilities; interleaved image-and-text content generation (for example, combining document understanding with text summarization or generation of image-annotated responses).Starting Price: Free -
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OpenAI o3-mini
OpenAI
OpenAI o3-mini is a lightweight version of the advanced o3 AI model, offering powerful reasoning capabilities in a more efficient and accessible package. Designed to break down complex instructions into smaller, manageable steps, o3-mini excels in coding tasks, competitive programming, and problem-solving in mathematics and science. This compact model provides the same high-level precision and logic as its larger counterpart but with reduced computational requirements, making it ideal for use in resource-constrained environments. With built-in deliberative alignment, o3-mini ensures safe, ethical, and context-aware decision-making, making it a versatile tool for developers, researchers, and businesses seeking a balance between performance and efficiency. -
18
Gemini Nano
Google
Gemini Nano from Google is a lightweight, energy-efficient AI model designed for high performance in compact, resource-constrained environments. Tailored for edge computing and mobile applications, Gemini Nano combines Google's advanced AI architecture with cutting-edge optimization techniques to deliver seamless performance without compromising speed or accuracy. Despite its compact size, it excels in tasks like voice recognition, natural language processing, real-time translation, and personalized recommendations. With a focus on privacy and efficiency, Gemini Nano processes data locally, minimizing reliance on cloud infrastructure while maintaining robust security. Its adaptability and low power consumption make it an ideal choice for smart devices, IoT ecosystems, and on-the-go AI solutions. -
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Amazon Nova 2 Lite
Amazon
Nova 2 Lite is a lightweight, high-speed reasoning model designed to handle everyday AI workloads across text, images, and video. It can generate clear, context-aware responses and lets users fine-tune how much internal reasoning the model performs before producing an answer. This adjustable “thinking depth” gives teams the flexibility to choose faster replies or more detailed problem-solving depending on the task. It stands out for customer service bots, automated document handling, and general business workflow support. Nova 2 Lite delivers strong performance across standard evaluation tests. It performs on par with or better than comparable compact models in most benchmark categories, demonstrating reliable comprehension and response quality. Its strengths include interpreting complex documents, pulling accurate insights from video content, generating usable code, and delivering grounded answers based on provided information. -
20
Gemini 2.0 Flash Thinking
Google
Gemini 2.0 Flash Thinking is an advanced AI model developed by Google DeepMind, designed to enhance reasoning capabilities by explicitly displaying its thought processes. This transparency allows the model to tackle complex problems more effectively and provides users with clear explanations of its decision-making steps. By showcasing its internal reasoning, Gemini 2.0 Flash Thinking not only improves performance but also offers greater explainability, making it a valuable tool for applications requiring deep understanding and trust in AI-driven solutions. -
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Reka Flash 3
Reka
Reka Flash 3 is a 21-billion-parameter multimodal AI model developed by Reka AI, designed to excel in general chat, coding, instruction following, and function calling. It processes and reasons with text, images, video, and audio inputs, offering a compact, general-purpose solution for various applications. Trained from scratch on diverse datasets, including publicly accessible and synthetic data, Reka Flash 3 underwent instruction tuning on curated, high-quality data to optimize performance. The final training stage involved reinforcement learning using REINFORCE Leave One-Out (RLOO) with both model-based and rule-based rewards, enhancing its reasoning capabilities. With a context length of 32,000 tokens, Reka Flash 3 performs competitively with proprietary models like OpenAI's o1-mini, making it suitable for low-latency or on-device deployments. The model's full precision requires 39GB (fp16), but it can be compressed to as small as 11GB using 4-bit quantization. -
22
Mistral Large 2
Mistral AI
Mistral AI has launched the Mistral Large 2, an advanced AI model designed to excel in code generation, multilingual capabilities, and complex reasoning tasks. The model features a 128k context window, supporting dozens of languages including English, French, Spanish, and Arabic, as well as over 80 programming languages. Mistral Large 2 is tailored for high-throughput single-node inference, making it ideal for large-context applications. Its improved performance on benchmarks like MMLU and its enhanced code generation and reasoning abilities ensure accuracy and efficiency. The model also incorporates better function calling and retrieval, supporting complex business applications.Starting Price: Free -
23
GigaChat 3 Ultra
Sberbank
GigaChat 3 Ultra is a 702-billion-parameter Mixture-of-Experts model built from scratch to deliver frontier-level reasoning, multilingual capability, and deep Russian-language fluency. It activates just 36 billion parameters per token, enabling massive scale with practical inference speeds. The model was trained on a 14-trillion-token corpus combining natural, multilingual, and high-quality synthetic data to strengthen reasoning, math, coding, and linguistic performance. Unlike modified foreign checkpoints, GigaChat 3 Ultra is entirely original—giving developers full control, modern alignment, and a dataset free of inherited limitations. Its architecture leverages MoE, MTP, and MLA to match open-source ecosystems and integrate easily with popular inference and fine-tuning tools. With leading results on Russian benchmarks and competitive performance on global tasks, GigaChat 3 Ultra represents one of the largest and most capable open-source LLMs in the world.Starting Price: Free -
24
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 -
25
GPT-5.1 Thinking
OpenAI
GPT-5.1 Thinking is the advanced reasoning model variant in the GPT-5.1 series, designed to more precisely allocate “thinking time” based on prompt complexity, responding faster to simpler requests and spending more effort on difficult problems. On a representative task distribution, it is roughly twice as fast on the fastest tasks and twice as slow on the slowest compared with its predecessor. Its responses are crafted to be clearer, with less jargon and fewer undefined terms, making deep analytical work more accessible and understandable. The model dynamically adjusts its reasoning depth, achieving a better balance between speed and thoroughness, particularly when dealing with technical concepts or multi-step questions. By combining high reasoning capacity with improved clarity, GPT-5.1 Thinking offers a powerful tool for tackling complex tasks, such as detailed analysis, coding, research, or technical explanations, while reducing unnecessary latency for routine queries. -
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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. -
27
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 -
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Kimi K2 Thinking
Moonshot AI
Kimi K2 Thinking is an advanced open source reasoning model developed by Moonshot AI, designed specifically for long-horizon, multi-step workflows where the system interleaves chain-of-thought processes with tool invocation across hundreds of sequential tasks. The model uses a mixture-of-experts architecture with a total of 1 trillion parameters, yet only about 32 billion parameters are activated per inference pass, optimizing efficiency while maintaining vast capacity. It supports a context window of up to 256,000 tokens, enabling the handling of extremely long inputs and reasoning chains without losing coherence. Native INT4 quantization is built in, which reduces inference latency and memory usage without performance degradation. Kimi K2 Thinking is explicitly built for agentic workflows; it can autonomously call external tools, manage sequential logic steps (up to and typically between 200-300 tool calls in a single chain), and maintain consistent reasoning.Starting Price: Free -
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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. -
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Gemini 2.0 Flash-Lite
Google
Gemini 2.0 Flash-Lite is Google DeepMind's lighter AI model, designed to offer a cost-effective solution without compromising performance. As the most economical model in the Gemini 2.0 lineup, Flash-Lite is tailored for developers and businesses seeking efficient AI capabilities at a lower cost. It supports multimodal inputs and features a context window of one million tokens, making it suitable for a variety of applications. Flash-Lite is currently available in public preview, allowing users to explore its potential in enhancing their AI-driven projects. -
31
ChatGLM
Zhipu AI
ChatGLM-6B is an open-source, Chinese-English bilingual dialogue language model based on the General Language Model (GLM) architecture with 6.2 billion parameters. Combined with model quantization technology, users can deploy locally on consumer-grade graphics cards (only 6GB of video memory is required at the INT4 quantization level). ChatGLM-6B uses technology similar to ChatGPT, optimized for Chinese Q&A and dialogue. After about 1T identifiers of Chinese and English bilingual training, supplemented by supervision and fine-tuning, feedback self-help, human feedback reinforcement learning and other technologies, ChatGLM-6B with 6.2 billion parameters has been able to generate answers that are quite in line with human preferences.Starting Price: Free -
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Ministral 3B
Mistral AI
Mistral AI introduced two state-of-the-art models for on-device computing and edge use cases, named "les Ministraux": Ministral 3B and Ministral 8B. These models set a new frontier in knowledge, commonsense reasoning, function-calling, and efficiency in the sub-10B category. They can be used or tuned for various applications, from orchestrating agentic workflows to creating specialist task workers. Both models support up to 128k context length (currently 32k on vLLM), and Ministral 8B features a special interleaved sliding-window attention pattern for faster and memory-efficient inference. These models were built to provide a compute-efficient and low-latency solution for scenarios such as on-device translation, internet-less smart assistants, local analytics, and autonomous robotics. Used in conjunction with larger language models like Mistral Large, les Ministraux also serve as efficient intermediaries for function-calling in multi-step agentic workflows.Starting Price: Free -
33
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 -
34
Mistral Small 3.1
Mistral
Mistral Small 3.1 is a state-of-the-art, multimodal, and multilingual AI model released under the Apache 2.0 license. Building upon Mistral Small 3, this enhanced version offers improved text performance, and advanced multimodal understanding, and supports an expanded context window of up to 128,000 tokens. It outperforms comparable models like Gemma 3 and GPT-4o Mini, delivering inference speeds of 150 tokens per second. Designed for versatility, Mistral Small 3.1 excels in tasks such as instruction following, conversational assistance, image understanding, and function calling, making it suitable for both enterprise and consumer-grade AI applications. Its lightweight architecture allows it to run efficiently on a single RTX 4090 or a Mac with 32GB RAM, facilitating on-device deployments. It is available for download on Hugging Face, accessible via Mistral AI's developer playground, and integrated into platforms like Google Cloud Vertex AI, with availability on NVIDIA NIM andStarting Price: Free -
35
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 -
36
DeepSeek-V3.1-Terminus
DeepSeek
DeepSeek has released DeepSeek-V3.1-Terminus, which enhances the V3.1 architecture by incorporating user feedback to improve output stability, consistency, and agent performance. It notably reduces instances of mixed Chinese/English character output and unintended garbled characters, resulting in cleaner, more consistent language generation. The update upgrades both the code agent and search agent subsystems to yield stronger, more reliable performance across benchmarks. DeepSeek-V3.1-Terminus is also available as an open source model, and its weights are published on Hugging Face. The model structure remains the same as DeepSeek-V3, ensuring compatibility with existing deployment methods, with updated inference demos provided for community use. While trained at a scale of 685B parameters, the model includes FP8, BF16, and F32 tensor formats, offering flexibility across environments.Starting Price: Free -
37
Llama 4 Maverick
Meta
Llama 4 Maverick is one of the most advanced multimodal AI models from Meta, featuring 17 billion active parameters and 128 experts. It surpasses its competitors like GPT-4o and Gemini 2.0 Flash in a broad range of benchmarks, especially in tasks related to coding, reasoning, and multilingual capabilities. Llama 4 Maverick combines image and text understanding, enabling it to deliver industry-leading results in image-grounding tasks and precise, high-quality output. With its efficient performance at a reduced parameter size, Maverick offers exceptional value, especially in general assistant and chat applications.Starting Price: Free -
38
Grok 4.1 Thinking is xAI’s advanced reasoning-focused AI model designed for deeper analysis, reflection, and structured problem-solving. It uses explicit thinking tokens to reason through complex prompts before delivering a response, resulting in more accurate and context-aware outputs. The model excels in tasks that require multi-step logic, nuanced understanding, and thoughtful explanations. Grok 4.1 Thinking demonstrates a strong, coherent personality while maintaining analytical rigor and reliability. It has achieved the top overall ranking on the LMArena Text Leaderboard, reflecting strong human preference in blind evaluations. The model also shows leading performance in emotional intelligence and creative reasoning benchmarks. Grok 4.1 Thinking is built for users who value clarity, depth, and defensible reasoning in AI interactions.
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39
GPT-3.5
OpenAI
GPT-3.5 is the next evolution of GPT 3 large language model from OpenAI. GPT-3.5 models can understand and generate natural language. We offer four main models with different levels of power suitable for different tasks. The main GPT-3.5 models are meant to be used with the text completion endpoint. We also offer models that are specifically meant to be used with other endpoints. Davinci is the most capable model family and can perform any task the other models can perform and often with less instruction. For applications requiring a lot of understanding of the content, like summarization for a specific audience and creative content generation, Davinci is going to produce the best results. These increased capabilities require more compute resources, so Davinci costs more per API call and is not as fast as the other models.Starting Price: $0.0200 per 1000 tokens -
40
Gemma 2
Google
A family of state-of-the-art, light-open models created from the same research and technology that were used to create Gemini models. These models incorporate comprehensive security measures and help ensure responsible and reliable AI solutions through selected data sets and rigorous adjustments. Gemma models achieve exceptional comparative results in their 2B, 7B, 9B, and 27B sizes, even outperforming some larger open models. With Keras 3.0, enjoy seamless compatibility with JAX, TensorFlow, and PyTorch, allowing you to effortlessly choose and change frameworks based on task. Redesigned to deliver outstanding performance and unmatched efficiency, Gemma 2 is optimized for incredibly fast inference on various hardware. The Gemma family of models offers different models that are optimized for specific use cases and adapt to your needs. Gemma models are large text-to-text lightweight language models with a decoder, trained in a huge set of text data, code, and mathematical content. -
41
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 -
42
GPT-3
OpenAI
Our GPT-3 models can understand and generate natural language. We offer four main models with different levels of power suitable for different tasks. Davinci is the most capable model, and Ada is the fastest. The main GPT-3 models are meant to be used with the text completion endpoint. We also offer models that are specifically meant to be used with other endpoints. Davinci is the most capable model family and can perform any task the other models can perform and often with less instruction. For applications requiring a lot of understanding of the content, like summarization for a specific audience and creative content generation, Davinci is going to produce the best results. These increased capabilities require more compute resources, so Davinci costs more per API call and is not as fast as the other models.Starting Price: $0.0200 per 1000 tokens -
43
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 -
44
Ministral 8B
Mistral AI
Mistral AI has introduced two advanced models for on-device computing and edge applications, named "les Ministraux": Ministral 3B and Ministral 8B. These models excel in knowledge, commonsense reasoning, function-calling, and efficiency within the sub-10B parameter range. They support up to 128k context length and are designed for various applications, including on-device translation, offline smart assistants, local analytics, and autonomous robotics. Ministral 8B features an interleaved sliding-window attention pattern for faster and more memory-efficient inference. Both models can function as intermediaries in multi-step agentic workflows, handling tasks like input parsing, task routing, and API calls based on user intent with low latency and cost. Benchmark evaluations indicate that les Ministraux consistently outperforms comparable models across multiple tasks. As of October 16, 2024, both models are available, with Ministral 8B priced at $0.1 per million tokens.Starting Price: Free -
45
MiniMax-M2.1
MiniMax
MiniMax-M2.1 is an open-source, agentic large language model designed for advanced coding, tool use, and long-horizon planning. It was released to the community to make high-performance AI agents more transparent, controllable, and accessible. The model is optimized for robustness in software engineering, instruction following, and complex multi-step workflows. MiniMax-M2.1 supports multilingual development and performs strongly across real-world coding scenarios. It is suitable for building autonomous applications that require reasoning, planning, and execution. The model weights are fully open, enabling local deployment and customization. MiniMax-M2.1 represents a major step toward democratizing top-tier agent capabilities.Starting Price: Free -
46
Gemini 2.5 Flash
Google
Gemini 2.5 Flash is a powerful, low-latency AI model introduced by Google on Vertex AI, designed for high-volume applications where speed and cost-efficiency are key. It delivers optimized performance for use cases like customer service, virtual assistants, and real-time data processing. With its dynamic reasoning capabilities, Gemini 2.5 Flash automatically adjusts processing time based on query complexity, offering granular control over the balance between speed, accuracy, and cost. It is ideal for businesses needing scalable AI solutions that maintain quality and efficiency. -
47
Orpheus TTS
Canopy Labs
Canopy Labs has introduced Orpheus, a family of state-of-the-art speech large language models (LLMs) designed for human-level speech generation. These models are built on the Llama-3 architecture and are trained on over 100,000 hours of English speech data, enabling them to produce natural intonation, emotion, and rhythm that surpasses current state-of-the-art closed source models. Orpheus supports zero-shot voice cloning, allowing users to replicate voices without prior fine-tuning, and offers guided emotion and intonation control through simple tags. The models achieve low latency, with approximately 200ms streaming latency for real-time applications, reducible to around 100ms with input streaming. Canopy Labs has released both pre-trained and fine-tuned 3B-parameter models under the permissive Apache 2.0 license, with plans to release smaller models of 1B, 400M, and 150M parameters for use on resource-constrained devices. -
48
Amazon Nova Pro
Amazon
Amazon Nova Pro is a versatile, multimodal AI model designed for a wide range of complex tasks, offering an optimal combination of accuracy, speed, and cost efficiency. It excels in video summarization, Q&A, software development, and AI agent workflows that require executing multi-step processes. With advanced capabilities in text, image, and video understanding, Nova Pro supports tasks like mathematical reasoning and content generation, making it ideal for businesses looking to implement cutting-edge AI in their operations. -
49
PanGu-Σ
Huawei
Significant advancements in the field of natural language processing, understanding, and generation have been achieved through the expansion of large language models. This study introduces a system which utilizes Ascend 910 AI processors and the MindSpore framework to train a language model with over a trillion parameters, specifically 1.085T, named PanGu-{\Sigma}. This model, which builds upon the foundation laid by PanGu-{\alpha}, takes the traditionally dense Transformer model and transforms it into a sparse one using a concept known as Random Routed Experts (RRE). The model was efficiently trained on a dataset of 329 billion tokens using a technique called Expert Computation and Storage Separation (ECSS), leading to a 6.3-fold increase in training throughput via heterogeneous computing. Experimentation indicates that PanGu-{\Sigma} sets a new standard in zero-shot learning for various downstream Chinese NLP tasks. -
50
GPT-5.1 Pro
OpenAI
GPT-5.1 Pro is the highest-performance version of the GPT-5.1 model family, designed for research-grade reasoning and advanced analytical workloads. It delivers deeper, more structured thinking, making it ideal for complex problem-solving across coding, science, finance, law, and technical research. Unlike the Instant and Thinking versions, GPT-5.1 Pro is built to maintain accuracy under heavy cognitive load, producing clearer logic and more reliable multi-step reasoning. Pro users also gain access to extended context windows, allowing significantly longer inputs and deeper information processing. While it supports the full range of ChatGPT features, GPT-5.1 Pro is optimized for precision, rigor, and high-stakes tasks. It is available exclusively to ChatGPT Pro and Business customers.
