Alternatives to Ling 3.0 Flash
Compare Ling 3.0 Flash alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to Ling 3.0 Flash in 2026. Compare features, ratings, user reviews, pricing, and more from Ling 3.0 Flash competitors and alternatives in order to make an informed decision for your business.
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1
GLM-5.2
Zhipu AI
GLM-5.2 is an advanced AI foundation model designed to support complex reasoning, coding, and long-range agentic tasks. It helps developers, teams, and organizations build intelligent systems that can understand instructions, solve technical problems, and assist with demanding workflows. The model is especially useful for software engineering, automation, research, and productivity-focused applications. GLM-5.2 is built to handle large amounts of context, making it suitable for projects that require deeper understanding across extended conversations, documents, or codebases. Its mixture-of-experts design helps balance strong performance with more efficient model operation. GLM-5.2 gives businesses and developers a powerful AI tool for creating smarter applications, improving technical workflows, and supporting advanced digital experiences.Starting Price: Free -
2
Kimi K3
Moonshot AI
Kimi K3 is Moonshot AI’s most capable model, built for frontier intelligence scenarios such as software engineering, knowledge work, deep reasoning, and multimodal understanding. The model has 2.8 trillion parameters and uses Kimi Delta Attention, a hybrid linear attention mechanism, along with Attention Residuals for long-context performance. Kimi K3 supports a 1 million token context window, making it useful for analyzing large codebases, long documents, complex knowledge bases, and multi-step workflows. It includes native visual understanding for images and videos, with support for structured message formats, base64 image input, uploaded video files, and multimodal reasoning. Developers can use Kimi K3 through an OpenAI-compatible API with support for streaming, structured JSON output, partial mode, custom tools, dynamic tool loading, and automatic context caching.Starting Price: $3 per 1M tokens (input) -
3
Qwen3.8 Max
Alibaba
Qwen3.8 Max is Alibaba’s next-generation Qwen flagship model, currently referenced publicly as Qwen3.8-Max-Preview. The model is positioned as a large multimodal AI system for advanced reasoning, coding, agentic workflows, data analysis, office productivity, and document understanding. Alibaba Cloud Model Studio lists Qwen3.8-Max-Preview as one of the newest models available through its Token Plan, alongside other text, image, and video models. Public reporting describes Qwen3.8 Max as a 2.4 trillion-parameter model that can handle text, images, video, and documents. It is expected to improve on Qwen3.7 Max in areas such as coding, full-stack development, data analysis, and office workflows. Built for developers and AI teams, Qwen3.8 Max is best understood as a frontier Qwen preview model for testing advanced multimodal and agentic AI workloads.Starting Price: $3 per 1M (input) -
4
Gemini 3.6 Flash
Google
Gemini 3.6 Flash is Google’s newest Flash model built for efficient, reliable, production-scale AI agents. The model improves on Gemini 3.5 Flash with stronger coding, knowledge work, multimodal performance, computer use, and agentic workflow execution. Gemini 3.6 Flash is designed to use fewer output tokens, take fewer reasoning steps, reduce unnecessary tool calls, and lower the cost of complex AI tasks. It supports document parsing, chart analysis, data analysis, report drafting, code migrations, visual understanding, and multi-agent orchestration. The model is available through the Gemini API, Google AI Studio, Android Studio, Google Antigravity, Gemini Enterprise Agent Platform, Gemini Enterprise app, and the Gemini app. Built for developers and enterprises, Gemini 3.6 Flash helps teams build faster, lower-cost, and more capable AI agents across coding, analysis, productivity, and multimodal workloads.Starting Price: $1.50 per 1M tokens (input) -
5
GPT-5.6 Sol
OpenAI
GPT-5.6 Sol is a next-generation OpenAI model designed for advanced reasoning, coding, agentic workflows, biology analysis, cybersecurity support, and complex knowledge work. It is part of the GPT-5.6 model family alongside Terra and Luna, with Sol positioned as the flagship model for the most demanding tasks. The model introduces a new max reasoning effort for deeper thinking and an ultra mode that uses subagents to accelerate complex work beyond a single-agent approach. GPT-5.6 Sol shows strong performance in command-line coding workflows, long-horizon security tasks, genomics analysis, vulnerability research, debugging, patch development, and defensive testing. OpenAI pairs the model’s stronger capabilities with layered safeguards, real-time misuse classifiers, account-level review, automated red-teaming, and enterprise controls for sensitive workflows. GPT-5.6 Sol helps developers, enterprises, researchers, and security teams complete sophisticated technical work.Starting Price: $5 per 1M tokens (input) -
6
MiniMax M3
MiniMax
MiniMax M3 is an open-weight multimodal AI model designed for coding, agentic workflows, long-context reasoning, and complex automation tasks. The model combines frontier-level coding performance, native multimodal understanding, and a context window of up to 1 million tokens. MiniMax M3 uses MiniMax Sparse Attention to improve long-context efficiency while reducing compute requirements for large-scale inputs. It supports text, image, and video understanding, making it useful for workflows that combine code, documents, visual references, and tool-driven tasks. The model is built for repository-scale reasoning, software engineering, autonomous task execution, tool calling, and multi-step agent workflows. MiniMax M3 helps developers, AI teams, and enterprises build capable agents that can reason across large contexts and work with multimodal information.Starting Price: Free -
7
Muse Spark 1.1
Meta
Muse Spark 1.1 is a multimodal reasoning model from Meta Superintelligence Labs built for agentic tasks, coding, computer use, tool use, and multimodal understanding. The model improves on the original Muse Spark with stronger performance in planning, orchestration, long-context work, coding workflows, and external app interactions. Muse Spark 1.1 can manage a 1 million token context window, remember earlier actions, retrieve important information, compact context, and delegate tasks across parallel subagents. It is designed to operate across tools, MCP servers, custom skills, browsers, native apps, scripts, images, video, PDFs, and audio-based workflows. Developers can access Muse Spark 1.1 through the new Meta Model API public preview, while users can try it in Thinking mode in the Meta AI app and on meta.ai.Starting Price: $1.25 per 1M tokens (input) -
8
Kimi K2.7 Code
Moonshot AI
Kimi K2.7 Code is an open-source, coding-focused agentic AI model developed by Moonshot AI for long-horizon software engineering tasks. It is designed to improve coding performance, agent workflows, and real-world development assistance compared with earlier Kimi K2 versions. The model supports a 256K context window, making it useful for working with large codebases, long technical documents, and complex multi-step programming tasks. Kimi K2.7 Code is available through Kimi Code and API access, with OpenAI- and Anthropic-compatible options for easier integration into developer workflows. It is also listed on Hugging Face and supports deployment through inference engines such as vLLM, SGLang, and KTransformers. With improved agentic capabilities, long-context support, and reduced thinking-token usage compared with K2.6, Kimi K2.7 Code gives developers a flexible open-source option for AI-assisted coding.Starting Price: Free -
9
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. -
10
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 -
11
Inkling
Thinking Machines Lab
Inkling is an open-weights multimodal AI model from Thinking Machines designed as a customizable foundation model for developers, researchers, and enterprises. The model is a Mixture-of-Experts transformer with 975 billion total parameters, 41 billion active parameters, and support for context windows up to 1 million tokens. Inkling was trained from scratch on text, images, audio, and video, giving it native capabilities across reasoning, coding, agentic tool use, vision, audio, factuality, and instruction following. It is built with controllable thinking effort so users can balance performance, latency, and token efficiency for different workloads. The model is available for fine-tuning on Tinker, with playground access, API availability through ecosystem partners, and full weights published on Hugging Face. Built for customization, Inkling gives teams an open-weights base model for building domain-specific AI systems, multimodal agents, coding workflows, research tools, and more.Starting Price: Free -
12
Laguna S 2.1
Poolside
Laguna S 2.1 is an open weight agentic coding model designed to pursue longer-horizon work and make effective use of reasoning. It uses a 118-billion-parameter Mixture-of-Experts architecture with 8 billion active parameters per token and supports a context window of up to one million tokens in both thinking and no-thinking modes. Its compact active size makes it suitable for complex work on local machines while remaining competitive with models many times larger on terminal, software-engineering, codebase-question-answering, and tool-use benchmarks. Laguna S 2.1 is built to keep working through difficult tasks with greater persistence, verification, and willingness to backtrack instead of declaring success too early. In demonstrated runs, it built and validated a browser rendering engine from an empty folder, optimized an agent harness for faster execution and substantially lower memory allocation, and completed extended mathematical research using the tools in its environment. -
13
Gemini 3.5 Flash
Google
Gemini 3.5 Flash is Google’s latest frontier AI model designed to combine advanced intelligence, high-speed performance, and agentic workflow execution for developers, enterprises, and everyday users. Built as part of the Gemini 3.5 family, the model excels at coding, long-horizon reasoning, multimodal understanding, and complex multi-step automation tasks while delivering significantly faster output speeds than many competing frontier models. Gemini 3.5 Flash powers AI agents capable of planning, executing, and managing workflows such as application development, codebase maintenance, data analysis, and financial document preparation through the Antigravity harness. The model also supports rich multimodal experiences by generating interactive graphics, dynamic web interfaces, animations, and advanced visual content. Gemini 3.5 Flash is integrated across Google products including the Gemini app, Google Search AI Mode, Google Antigravity, Google AI Studio, Android Studio, and more.Starting Price: $1.50 per 1M tokens (input) -
14
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 -
15
Hy3
Tencent
Hy3 preview is Tencent Hy’s most intelligent model in the Hy series to date, built as a 295B-parameter Mixture-of-Experts model with 21B activated parameters, 3.8B MTP layer parameters, and support for up to a 256K token context window. As the first model trained on Tencent Hy’s rebuilt infrastructure, Hy3 preview is designed to improve real-world usability across complex reasoning, instruction following, context learning, coding, agent capabilities, and overall inference performance. It integrates both fast and slow thinking capabilities, allowing direct responses for simpler tasks and deeper reasoning for complex math, coding, and reasoning work. The model is built around well-rounded capabilities across long-context understanding, instruction following, tool use, and agent workflows, with evaluation focused not only on standard benchmarks but also on authentic business and development scenarios.Starting Price: Free -
16
Ling 2.6
Ant Group
Ling 2.6 is a general-purpose large language model series independently developed and open-sourced by Ant Group, built on a Mixture of Experts architecture and designed for inference efficiency, long context modeling, training technology, and AI Agent collaborative reasoning. Ling’s MoE architecture routes each token to activate only the most relevant expert subnetworks, compressing actual computation to a minimal fraction while maintaining large-scale model capacity. The Ling 2.6 series further advances long-sequence modeling, with Ling-2.6-1T supporting up to a 1M native context window and the official API exposing a 256K context window, while Ling-2.6-flash provides a native 256K context window capable of processing approximately 200,000 characters of long-form input. The models are designed for reliable long-range information retrieval, with no noticeable degradation whether information appears at the beginning, middle, or end of the context.Starting Price: $0.0028 per 1M tokens -
17
Ling 2.6 Flash
Ant Group
Ling 2.6 Flash is the latest cost-effective model in the Ling series, built on a Mixture of Experts architecture with 104B total parameters and 7.4B activated parameters. It is designed to achieve an optimal balance between inference performance and compute cost, making it suitable for general-purpose scenarios where strong reasoning capability, high throughput, and efficient deployment matter. Ling’s MoE architecture routes each token to activate only the most relevant expert subnetworks, compressing actual computation to a minimal fraction while maintaining large-scale model capacity. Ling 2.6 Flash provides a native 256K context window and can process approximately 200,000 characters of long-form input, with reliable long-range information retrieval whether key information appears at the beginning, middle, or end of the context. Its aggregate benchmark performance is comparable to or exceeds 40B-class Dense models.Starting Price: $0.00037 per 1M tokens -
18
Gemma 4
Google
Gemma 4 is an AI model introduced by Google and built on the Gemini architecture to deliver improved performance and flexibility. The model is designed to run efficiently on a single GPU or TPU, making it more accessible to developers and researchers. Gemma 4 enhances capabilities in natural language understanding and text generation, supporting a wide range of AI-driven applications. Its architecture allows it to handle complex tasks while maintaining efficient resource usage. Developers can use the model to build applications that rely on advanced language processing and automation. The design emphasizes scalability so that it can support both smaller projects and larger AI systems. By combining efficiency with powerful language capabilities, Gemma 4 helps advance the development of modern AI solutions.Starting Price: Free -
19
Ring 2.6
Ant Group
Ring is a trillion-parameter thinking model from Ant Group, designed for real-world Agent workflows. It uses the same Mixture of Experts architecture as Ling, activating about 63B parameters per inference, and focuses on coding agents, tool use, multi-tool collaboration, engineering development, research analysis, and long-horizon task execution. Rather than only pursuing “smarter” results, Ring is built to consistently complete complex tasks at reasonable cost, balancing quality, speed, and execution efficiency in production environments. Ring-2.6-1T introduces an adjustable Reasoning Effort mechanism with high and xhigh reasoning intensity levels, using adaptive reasoning budget allocation based on task complexity. High mode is designed for high-frequency Agent workflows, lower token cost, faster multi-step execution, multi-turn interaction, tool collaboration, and task decomposition.Starting Price: $0.0028 per 1M tokens -
20
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 -
21
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 -
22
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 -
23
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. -
24
MiMo-V2.5-Pro
Xiaomi Technology
Xiaomi MiMo-V2.5-Pro is an advanced open-source AI model designed to handle complex, long-horizon tasks with strong agentic capabilities. It features a Mixture-of-Experts architecture with over one trillion parameters and a large context window of up to one million tokens. The model is built to perform sophisticated reasoning, coding, and problem-solving across extended workflows. It demonstrates high performance on benchmark tests related to software engineering, reasoning, and general intelligence. MiMo-V2.5-Pro can autonomously complete complex projects, such as building full software systems or optimizing engineering designs. It uses hybrid attention mechanisms to balance efficiency and performance across long contexts. The model is also optimized for token efficiency, reducing computational cost while maintaining strong results. By combining scalability, efficiency, and advanced reasoning, MiMo-V2.5-Pro represents a major step forward in open-source AI models. -
25
LongCat-2.0
LongCat
LongCat-2.0 is a 1.6 trillion total-parameter Mixture-of-Experts language model built on AI ASIC superpods, with about 48 billion parameters activated per token and strong performance across coding and agentic tasks. It is a substantial step up from previous LongCat models, combining large-scale sparse architecture with dedicated post-training for real-world software engineering, tool use, long-context reasoning, and multi-step agent workflows. LongCat-2.0 is trained and deployed entirely on AI ASIC superpods, with pretraining spanning more than 35 trillion tokens and millions of accelerator-hours, demonstrating frontier-scale training on alternative hardware platforms. To strengthen long-horizon tasks, the model introduces LongCat Sparse Attention and is trained on hundreds of billions of tokens of 1M-context data, giving it native support for ultra-long context tasks and reliable long-document understanding. -
26
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 -
27
GLM-5.1
Zhipu AI
GLM-5.1 is the latest iteration of Z.ai’s GLM series, designed as a frontier-level, agent-oriented AI model optimized for coding, reasoning, and long-horizon workflows. It builds on the GLM-5 architecture, which uses a Mixture-of-Experts (MoE) design to deliver high performance while keeping inference costs efficient, and is part of a broader push toward open-weight, developer-accessible models. A core focus of GLM-5.1 is enabling agentic behavior, meaning it can plan, execute, and iterate across multi-step tasks rather than simply responding to single prompts. It is specifically designed to handle complex workflows such as debugging code, navigating repositories, and executing chained operations with sustained context. Compared to earlier models, GLM-5.1 improves reliability in long interactions, maintaining coherence across extended sessions and reducing breakdowns in multi-step reasoning.Starting Price: Free -
28
MiMo-V2.5
Xiaomi Technology
Xiaomi MiMo-V2.5 is an advanced open-source AI model designed to combine strong agentic capabilities with native multimodal understanding. It can process and reason across text, images, and audio within a single unified system. The model uses a sparse Mixture-of-Experts architecture with hundreds of billions of parameters for efficient performance. It supports an extended context window of up to one million tokens, enabling long and complex workflows. MiMo-V2.5 is built to handle tasks such as coding, reasoning, and multimodal analysis with high accuracy. It incorporates dedicated visual and audio encoders to enhance perception and cross-modal reasoning. The model demonstrates strong benchmark performance across coding, reasoning, and multimodal tasks. By combining multimodality, efficiency, and agentic intelligence, MiMo-V2.5 advances the capabilities of open-source AI systems. -
29
Laguna XS 2.1
Poolside
Laguna XS 2.1 is an upgraded open weight agentic coding model designed for long-horizon work on a local machine. It uses a 33-billion-parameter Mixture-of-Experts architecture with 3 billion activated parameters per token, retaining the same efficient architecture as Laguna XS.2 while improving multilingual software engineering and terminal-style task performance. The model is built to support coding agents that inspect repositories, reason through complex changes, use tools, execute commands, and continue working across extended tasks. It is served with a 256K context window, giving agents room to work with large codebases, lengthy histories, and multi-step workflows. Laguna XS 2.1 is supported by vLLM, SGLang, NVIDIA TensorRT-LLM, Hugging Face Transformers, and Ollama, with native llama.cpp support planned. It is available in BF16, FP8, INT4, and NVFP4 checkpoints, allowing developers to choose between maximum fidelity and configurations suited to tighter VRAM or compute budgets. -
30
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 -
31
Qwen3.6-35B-A3B
Alibaba
Qwen3.5-35B-A3B is part of the Qwen3.5 “Medium” model series, designed as a highly efficient, multimodal foundation model that balances strong reasoning ability with practical deployment requirements. It uses a Mixture-of-Experts (MoE) architecture with 35 billion total parameters but activates only about 3 billion per token, allowing it to deliver performance comparable to much larger models while significantly reducing computational cost. The model integrates a hybrid attention mechanism that combines linear attention with standard attention layers, enabling efficient long-context processing and improved scalability for complex tasks. As a native vision-language model, it can process both text and visual inputs, supporting use cases such as multimodal reasoning, coding, and agent-based workflows. It is designed to function as a general-purpose “AI agent,” capable of planning, tool use, and structured problem solving rather than just conversational responses.Starting Price: Free -
32
Ling Studio
Ant Group
Ling Studio is Ant Ling’s online environment for exploring the infinite possibilities of AI and testing the core capabilities of the Ling model family. It gives users a direct place to try Ant Ling models before building with them through API access, making it easier to experience multi-turn reasoning, long-context processing, multimodal generation, and model behavior in a practical chat workspace. It connects to Ant Ling’s high-performance model family for text, coding, reasoning, and multimodal tasks. Ling models are general-purpose LLMs built on Mixture of Experts architecture, balancing high parameter scale with low activation cost and supporting conversation, text generation, and content creation. Ring models specialize in deep reasoning and cognitive capabilities, with strong performance in math, programming, and comprehensive reasoning benchmarks. -
33
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 -
34
Grok 4.1 Fast
SpaceXAI
Grok 4.1 Fast is an xAI model designed to deliver advanced tool-calling capabilities with a massive 2-million-token context window. It excels at complex real-world tasks such as customer support, finance, troubleshooting, and dynamic agent workflows. The model pairs seamlessly with the new Agent Tools API, which enables real-time web search, X search, file retrieval, and secure code execution. This combination gives developers the power to build fully autonomous, production-grade agents that plan, reason, and use tools effectively. Grok 4.1 Fast is trained with long-horizon reinforcement learning, ensuring stable multi-turn accuracy even across extremely long prompts. With its speed, cost-efficiency, and high benchmark scores, it sets a new standard for scalable enterprise-grade AI agents. -
35
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 -
36
GLM-4.5V
Zhipu AI
GLM-4.5V builds on the GLM-4.5-Air foundation, using a Mixture-of-Experts (MoE) architecture with 106 billion total parameters and 12 billion activation parameters. It achieves state-of-the-art performance among open-source VLMs of similar scale across 42 public benchmarks, excelling in image, video, document, and GUI-based tasks. It supports a broad range of multimodal capabilities, including image reasoning (scene understanding, spatial recognition, multi-image analysis), video understanding (segmentation, event recognition), complex chart and long-document parsing, GUI-agent workflows (screen reading, icon recognition, desktop automation), and precise visual grounding (e.g., locating objects and returning bounding boxes). GLM-4.5V also introduces a “Thinking Mode” switch, allowing users to choose between fast responses or deeper reasoning when needed.Starting Price: Free -
37
Sarvam 105B
Sarvam
Sarvam-105B is the flagship large language model in Sarvam’s open source model family, designed to deliver high-performance reasoning, multilingual understanding, and agent-based execution within a single scalable system. Built as a Mixture-of-Experts (MoE) model with approximately 105 billion total parameters, of which only a fraction are activated per token, it achieves strong computational efficiency while maintaining high capability across complex tasks. The model is optimized for advanced reasoning, coding, mathematics, and agentic workflows, making it suitable for tasks that require multi-step problem solving and structured outputs rather than simple conversational responses. Sarvam-105B supports long-context processing of up to around 128K tokens, enabling it to handle large documents, extended conversations, and deep analytical queries without losing coherence.Starting Price: Free -
38
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 -
39
Command A+
Cohere AI
Command A+ is Cohere’s fastest and most powerful language model yet, an open-source enterprise workhorse built for complex reasoning, multimodal and multilingual agentic tasks, and efficient private deployment. It is a sparse mixture-of-experts model with 218B total parameters and 25B active parameters, designed for high-performance agentic workflows with minimal compute overhead. Command A+ unifies capabilities from across the Command family into one scalable model, supporting text, image, reasoning, and tool use with a 128K input context, 64K max generation, and support for 48 languages. It is optimized for reasoning, agentic workflows, RAG, multilingual work, and multimodal document processing, with support for vLLM and Transformers. Compared with earlier Command A models, it improves enterprise workload performance across multimodal understanding, retrieval, long-horizon tasks, complex reasoning, coding, translation, and document understanding. -
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Reka Flash 3
Reka
Reka Flash 3 is a 21-billion-parameter multimodal AI model developed by Reka AI, designed to excel in general chat, coding, instruction following, and function calling. It processes and reasons with text, images, video, and audio inputs, offering a compact, general-purpose solution for various applications. Trained from scratch on diverse datasets, including publicly accessible and synthetic data, Reka Flash 3 underwent instruction tuning on curated, high-quality data to optimize performance. The final training stage involved reinforcement learning using REINFORCE Leave One-Out (RLOO) with both model-based and rule-based rewards, enhancing its reasoning capabilities. With a context length of 32,000 tokens, Reka Flash 3 performs competitively with proprietary models like OpenAI's o1-mini, making it suitable for low-latency or on-device deployments. The model's full precision requires 39GB (fp16), but it can be compressed to as small as 11GB using 4-bit quantization. -
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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 -
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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 -
43
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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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 -
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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. -
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Gemini 3.5 Flash-Lite
Google
Gemini 3.5 Flash-Lite is Google’s fastest model in the Gemini 3.5 series, designed for low-latency tasks and high-throughput developer workflows such as agentic search, document processing, coding, and large-scale data analysis. It delivers 350 output tokens per second and significantly improves on previous Flash-Lite generations in both quality and agentic performance. Developers can configure its thinking level to match the workload: minimal or low thinking supports fast execution for high-volume tasks, while higher thinking levels enable more complex, multi-step subagent workflows. Built-in computer-use capabilities allow the model to interact reliably with digital environments across supported surfaces. Gemini 3.5 Flash-Lite also advances coding, long-context understanding, and real-world task execution, outperforming Gemini 3.1 Flash-Lite across key evaluations and even surpassing Gemini 3 Flash on several agentic and software-engineering benchmarks.Starting Price: $0.30 per 1M input tokens -
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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 -
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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 -
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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 -
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ERNIE 5.1
Baidu
ERNIE 5.1 is Baidu’s latest large language model designed to deliver advanced reasoning, agentic AI capabilities, creative writing, and world knowledge performance while operating with significantly improved efficiency. The model builds on the foundation of ERNIE 5.0 while reducing total parameters and training costs, allowing it to achieve flagship-level intelligence at a fraction of the computational expense of comparable models. ERNIE 5.1 performs strongly across international benchmarks for reasoning, search, knowledge, and agentic tasks, ranking among the top global AI models and leading among Chinese-developed models on multiple leaderboards. The platform introduces a new fully asynchronous reinforcement learning infrastructure that improves training efficiency, scalability, and stability for complex long-horizon AI tasks. ERNIE 5.1 also features advanced creative writing capabilities.
