List of the Top AI Models for Claude Code in 2026 - Page 3

Laguna XS.2 stands out as Poolside's groundbreaking open-weight coding model, noted for being the lightest and fastest in the Laguna lineup. Equipped with a staggering 33 billion parameters organized in a Mixture of Experts structure, of which 3 billion are active, this model has undergone extensive training in-house utilizing 30 trillion tokens. As the most recent generation model available to the public, it features a second-generation architecture and represents Poolside's first open-weight release, benefiting from lessons learned during the Laguna M.1 training process, which utilized synthetic data and reinforcement learning. Tailored specifically to optimize agentic coding workflows, Laguna XS.2 is exceptional in coding, acting, and rapid iteration, particularly within Poolside's coding agent ecosystem. This model is especially beneficial for developers and teams in need of a lightweight and efficient coding solution, as opposed to more complex frontier systems. Released under the flexible Apache 2.0 license, it enables the community to evaluate, refine, quantize, and build upon its weights, fostering an environment of collaborative development. Ultimately, Laguna XS.2 not only serves as a powerful tool for agentic coding but also promotes creativity and experimentation among its users, allowing for a diverse range of applications and enhancements.

Laguna M.1 is recognized as Poolside's premier model for agentic coding, meticulously designed in-house to optimize software development processes. This sophisticated model incorporates 225 billion parameters and employs a Mixture of Experts architecture with 23 billion parameters activated, all trained on a colossal dataset of 30 trillion tokens using a network of 6,144 NVIDIA H200 GPUs. Poolside committed to developing Laguna M.1 from the ground up, utilizing proprietary data, a specialized training codebase, and an asynchronous on-policy reinforcement learning strategy within its agent framework, all specifically oriented towards agentic coding applications. The model's architecture is crafted to deliver top-tier performance within Poolside's coding agent, empowering it to adeptly reason through programming tasks, engage with an array of tools, modify code, run tests, and support extensive autonomous development sessions. Tailored for developers and teams facing complex coding obstacles, Laguna M.1 boasts enhanced capabilities in reasoning, understanding architecture, managing terminal actions, and executing multi-step processes, far exceeding the abilities of lighter models. Overall, its comprehensive feature set establishes it as an indispensable tool for professionals immersed in high-stakes software projects, making it a vital component in the landscape of agentic coding solutions.

Sakana Fugu is a multi-agent AI system that operates like one model while coordinating many underlying expert models behind a single API. The platform is designed to deliver frontier-level performance without forcing users to depend on one model provider or manually manage several separate AI tools. Fugu dynamically chooses which agents should participate in each task and coordinates them through learned collaboration patterns. This approach allows the system to handle complex work such as coding, reasoning, scientific problem solving, code review, security assessment, literature analysis, patent research, and autonomous research workflows. Sakana Fugu is grounded in research on learned orchestration, including TRINITY and the Conductor, which explore how AI systems can route tasks, assign roles, and coordinate communication among multiple agents. Users can access the system through an OpenAI-compatible API and choose between Fugu and Fugu Ultra depending on their workload. Fugu is built for everyday coding, chatbot, review, and productivity use cases where strong performance and lower latency are both important. Fugu Ultra uses a deeper pool of expert agents to improve quality on harder tasks such as Kaggle competitions, paper reproduction, cybersecurity analysis, and technical investigations. Organizations can control which agents, providers, or models are allowed in the pool to meet privacy, data handling, compliance, and procurement needs. The platform offers pay-as-you-go and subscription pricing options, with Fugu Ultra priced separately for input, output, and cached input tokens. Sakana Fugu gives developers, researchers, and enterprises a way to plug multi-agent intelligence into existing workflows while maintaining flexibility, control, and stronger performance on demanding tasks.

The GLM-5V-Turbo stands as a cutting-edge multimodal coding foundation model, expertly designed for scenarios necessitating visual inputs, proficient in interpreting various formats including images, videos, texts, and files to produce text-based results. This model is particularly optimized for agent workflows, enabling it to grasp environments effectively, devise suitable actions, and execute tasks, while also maintaining compatibility with agent frameworks such as Claude Code and OpenClaw. Notably, it excels in managing long-context interactions, offering an impressive context capacity of 200K tokens alongside an output limit of up to 128K tokens, making it exceptionally suited for complex, long-duration projects. Moreover, it presents an array of thinking modes tailored for different situations, demonstrates strong visual understanding of both images and videos, and streams outputs in real-time to improve user interaction. It also incorporates advanced function-calling capabilities that allow seamless integration of external tools, with its context caching feature significantly enhancing performance during extended dialogues. In real-world applications, the model is capable of skillfully converting design mockups into operational frontend projects, highlighting its adaptability and depth in practical coding environments. Furthermore, this adaptability empowers users to approach a diverse array of intricate tasks with assurance and effectiveness, greatly enhancing their productivity.

Ling 2.6 signifies a series of large language models that have been independently developed and made open-source by Ant Group, leveraging a Mixture of Experts (MoE) architecture to optimize inference efficiency, manage long context modeling, improve training methodologies, and facilitate collaborative reasoning among AI agents. Through the implementation of this MoE architecture, Ling adeptly channels each token to interact solely with the most relevant expert subnetworks, which markedly decreases computational demands while maintaining the model's extensive functional capabilities. Notably, this series achieves significant advancements in long-sequence modeling, as demonstrated by Ling-2.6-1T, which supports a native context window of up to 1 million tokens and provides a 256K context window via its official API; further, Ling-2.6-flash is designed with a native 256K context window, allowing it to process approximately 200,000 characters in large inputs. These models are designed with great precision to ensure the reliable retrieval of information over long distances without any noticeable degradation in quality, regardless of the position of the data within the context. This cutting-edge methodology in long-context processing establishes a new standard for both efficiency and reliability in the performance of language models. The implications of such advancements could revolutionize how AI systems interact with extensive data sets, enabling more sophisticated applications in various fields.

The Ling 2.6 Flash is the latest and most cost-effective member of the Ling series, featuring a Mixture of Experts architecture that boasts 104 billion parameters, with 7.4 billion of these actively utilized. Designed to achieve an optimal balance between inference speed and resource costs, this model excels in various applications that require robust reasoning, high throughput, and efficient deployment. Its MoE framework allows the model to engage only the most relevant expert subnetworks for each token, thereby significantly lowering the computational burden while still leveraging the model's extensive capacity. With a native context window of 256K, Ling 2.6 Flash can process approximately 200,000 characters of lengthy input, effectively retrieving essential long-range information no matter where it appears in the context. Additionally, its benchmark performance competes with or even surpasses that of dense models with 40 billion parameters, showcasing its strong position within the AI landscape. This combination of efficiency and high performance positions the Ling 2.6 Flash as a compelling choice for developers who desire sophisticated capabilities without placing undue strain on their resources. As technology continues to evolve, the Ling 2.6 Flash stands out as a prime candidate for future innovations in artificial intelligence.

Ring represents an advanced trillion-parameter model developed by Ant Group, designed to optimize real-world Agent workflows. Utilizing a Mixture of Experts architecture akin to that of Ling, it activates around 63 billion parameters for each inference and is adept at performing tasks such as coding agents, using tools, collaborating with diverse instruments, software engineering, conducting research, and managing long-term projects. Rather than simply aiming for more intelligent outcomes, Ring focuses on ensuring the dependable execution of complex tasks while keeping costs manageable, thereby achieving a harmonious balance of quality, speed, and efficiency in production environments. The most recent version, Ring-2.6-1T, features a customizable Reasoning Effort mechanism with high and xhigh reasoning intensity levels that adjust the reasoning budget based on task complexity. The high mode is specifically designed for frequent Agent workflows, leading to reduced token costs and expedited multi-step processes, while also promoting multi-turn conversations, tool collaboration, and task breakdown. This evolution significantly boosts the operational capabilities of agents, making them more effective across various domains and enhancing their overall performance in dynamic environments. Consequently, Ring stands as a pivotal advancement in the realm of intelligent agents, showcasing its versatility and reliability.

Ling 3.0 Flash is an evolved language model specifically designed for long-term agent tasks, featuring rapid response capabilities, low activation levels, and reliable tool utilization. With a Mixture-of-Experts architecture, it encompasses an impressive total of 124 billion parameters, activating 5.1 billion parameters for each token, which optimizes its performance while ensuring efficient inference. The model showcases a remarkable native context window of 256K tokens, expandable to a maximum of 1 million tokens, facilitating effective information retrieval from extensive contexts. In comparison to its earlier version, the original Flash model, Ling 3.0 Flash offers superior stability for extended operations, enhances tool-calling accuracy, adheres more closely to instructions, and shows improved compatibility with agent harnesses and coding tasks. Furthermore, its advanced spatial awareness capabilities allow it to construct grids of physical scenes and assess relative positions with precision, while its hybrid reasoning abilities increase success rates across various task complexities. This model not only represents a substantial advancement in language modeling technology but also ensures users can attain exceptional performance across a wide array of applications, thus broadening its potential use cases. Overall, Ling 3.0 Flash stands out as a groundbreaking development in the field, likely to influence future applications significantly.

Claude Opus 4.1 marks a significant iterative improvement over its earlier version, Claude Opus 4, with a focus on enhancing capabilities in coding, agentic reasoning, and data analysis while keeping deployment straightforward. This latest iteration achieves a remarkable coding accuracy of 74.5 percent on the SWE-bench Verified, alongside improved research depth and detailed tracking for agentic search operations. Additionally, GitHub has noted substantial progress in multi-file code refactoring, while Rakuten Group highlights its proficiency in pinpointing precise corrections in large codebases without introducing errors. Independent evaluations show that the performance of junior developers has seen an increase of about one standard deviation relative to Opus 4, indicating meaningful advancements that align with the trajectory of past Claude releases.

Claude Opus 4.5 represents a major leap in Anthropic’s model development, delivering breakthrough performance across coding, research, mathematics, reasoning, and agentic tasks. The model consistently surpasses competitors on SWE-bench Verified, SWE-bench Multilingual, Aider Polyglot, BrowseComp-Plus, and other cutting-edge evaluations, demonstrating mastery across multiple programming languages and multi-turn, real-world workflows. Early users were struck by its ability to handle subtle trade-offs, interpret ambiguous instructions, and produce creative solutions—such as navigating airline booking rules by reasoning through policy loopholes. Alongside capability gains, Opus 4.5 is Anthropic’s safest and most robustly aligned model, showing industry-leading resistance to strong prompt-injection attacks and lower rates of concerning behavior. Developers benefit from major upgrades to the Claude API, including effort controls that balance speed versus capability, improved context efficiency, and longer-running agentic processes with richer memory. The platform also strengthens multi-agent coordination, enabling Opus 4.5 to manage subagents for complex, multi-step research and engineering tasks. Claude Code receives new enhancements like Plan Mode improvements, parallel local and remote sessions, and better GitHub research automation. Consumer apps gain better context handling, expanded Chrome integration, and broader access to Claude for Excel. Enterprise and premium users see increased usage limits and more flexible access to Opus-level performance. Altogether, Claude Opus 4.5 showcases what the next generation of AI can accomplish—faster work, deeper reasoning, safer operation, and richer support for modern development and productivity workflows.

SubQ is a next-generation large language model developed by Subquadratic, designed to handle extremely long-context reasoning tasks with high efficiency. It supports up to 12 million tokens in a single prompt, allowing it to process entire codebases, months of development history, and large datasets in one step. The model uses a fully sub-quadratic sparse-attention architecture, which reduces unnecessary computations by focusing only on meaningful relationships between data points. This approach significantly lowers computational costs while maintaining strong performance across complex tasks. SubQ is optimized for use cases such as software engineering, code analysis, long-context retrieval, and AI agent workflows. It enables developers to analyze large amounts of information without breaking it into smaller segments. The model offers fast processing speeds and lower operational costs compared to traditional transformer-based models. SubQ is accessible through APIs, making it easy for developers and enterprises to integrate it into their systems. It can also be used within coding agents to improve code mapping, exploration, and understanding. The platform supports streaming and tool usage for more dynamic workflows. Its architecture allows it to scale efficiently as data size increases, overcoming common limitations of standard models. SubQ also delivers competitive performance on benchmarks related to coding and long-context tasks. By combining efficiency, scalability, and large context capabilities, it provides a powerful solution for advanced AI applications.

SubQ 1.1 Small is a long-context enterprise AI model developed by Subquadratic to address the limitations of traditional models that struggle with large artifacts. It is built for tasks where the full context matters, including analyzing entire codebases, reviewing lengthy contracts, comparing financial filings, and reasoning across document collections. The model uses Subquadratic Sparse Attention, which replaces dense attention with a learned sparse approach that scales more efficiently as context length grows. This allows SubQ 1.1 Small to process extremely large context windows while sharply reducing attention compute requirements. In benchmark testing, the model achieved near-perfect needle-in-a-haystack retrieval at 1M, 2M, 6M, and 12M tokens. It also scored 99.12% on the RULER 128K benchmark, demonstrating strength on tasks involving multi-hop reasoning, variable tracing, aggregation, and long-context understanding. Beyond retrieval, SubQ 1.1 Small maintains competitive performance in general knowledge, coding, and enterprise agent benchmarks such as GPQA Diamond, LiveCodeBench, and AutomationBench Finance. Its efficiency is a major advantage, requiring 64.5x less compute than dense attention and running 56x faster than FlashAttention-2 at 1M tokens on a single attention layer. The model was trained through staged context extension and continued pretraining on long-form artifacts such as books, documents, and repository-scale code. SubQ 1.1 Small is suited for financial analysis, legal work, software engineering, due diligence, long-horizon coding tasks, and enterprise workflows that depend on relationships spread across large bodies of information. It gives organizations a way to reason over complete artifacts more directly instead of relying only on retrieval pipelines, chunking strategies, and agentic scaffolding.

The Ming-Flash Omni 2.0, created by Ant Group, embodies a cutting-edge large language model that functions within a unified multimodal framework, prioritizing the concept of “modal unity + task unity.” As the latest addition to the Ming series, this model is designed to foster a seamless understanding and generation of content across diverse modalities, such as text, images, audio, and video, thereby removing the necessity for various specialized models to carry out specific tasks like visual recognition, audio processing, verbal communication, and artistic creation. Building on advancements made by its earlier versions, Ming-Light Omni and Ming-Flash Omni Preview, this release not only confirms the viability of a consolidated architecture but also scales up to hundreds of billions of parameters while employing a Data Scaling strategy that achieves top-tier performance in open-source settings across a wide array of benchmarks. Significantly, the model features four critical capability modules: image-text comprehension, video interpretation, speech generation, and image creation or manipulation. To further improve image-text understanding, Ming utilizes structured knowledge graphs that enhance its ability to perceive visuals with greater depth. This pioneering methodology not only expands the model's range of applications but also establishes a new benchmark in the realm of artificial intelligence, pushing the boundaries of what is possible in multimodal learning. In doing so, it also opens up new avenues for research and development within the field.

LongCat-2.0 signifies a remarkable leap forward in the field of language models, boasting an impressive 1.6 trillion parameters through a Mixture-of-Experts architecture that utilizes AI ASIC superpods, with around 48 billion parameters activated per token, demonstrating outstanding proficiency in coding and agentic functions. This model notably surpasses its predecessors by incorporating a large-scale sparse architecture along with specialized post-training techniques designed specifically for applications in real-world software development, tool usage, long-context reasoning, and intricate agent operations. Entirely built and executed on AI ASIC superpods, LongCat-2.0's pretraining involved processing over 35 trillion tokens and countless accelerator hours, highlighting the forefront of training techniques on state-of-the-art hardware. To further enhance its capabilities on tasks that require long-term contextual awareness, the model integrates LongCat Sparse Attention and is trained with hundreds of billions of tokens derived from 1M-context datasets, which empowers it to adeptly handle ultra-long context challenges and maintain a comprehensive understanding of extensive documents. This unique blend of features not only establishes LongCat-2.0 as an innovative leader in advanced language models but also sets a new benchmark for future developments in the domain. Its capabilities are likely to inspire a new wave of research and applications in the field.

The Laguna XS 2.1 represents a sophisticated advancement in coding models, functioning as an open weight agentic system that excels in executing long-duration tasks on local machines. It boasts a robust 33-billion-parameter Mixture-of-Experts architecture, activating 3 billion parameters per token, while preserving the efficient design of its predecessor, Laguna XS.2, and significantly enhancing its capabilities in multilingual software engineering and terminal-related tasks. This model is meticulously crafted to support coding agents in reviewing code repositories, navigating complex changes, leveraging diverse tools, executing commands, and ensuring seamless progress throughout extensive projects. With an impressive context window of 256K, it empowers agents to adeptly handle large codebases, maintain extensive histories, and navigate intricate multi-step workflows. The Laguna XS 2.1 also enjoys compatibility with various platforms like vLLM, SGLang, NVIDIA TensorRT-LLM, Hugging Face Transformers, and Ollama, with aspirations for future native support from llama.cpp. Offered in multiple checkpoint formats such as BF16, FP8, INT4, and NVFP4, it allows developers to choose between high fidelity and configurations designed for environments with restricted VRAM or processing capacity. This versatility not only enhances its usability across different development frameworks but also positions it as a prime choice for diverse programming needs and settings. Furthermore, its ability to adapt to varying project demands makes it a valuable asset for developers seeking efficiency and performance in their workflows.

Ling 3.0 Tiny is an advanced reasoning model with open weights, consisting of 7.9 billion parameters in total and 1.3 billion that are active, while boasting a remarkable context window of 262,000 tokens. Utilizing a mixture-of-experts architecture, it expands the open-weights Pareto frontier in intelligence relative to its active parameters, all while maintaining a compact size suitable for deployment in various settings. With a score of 25 on the Artificial Analysis Intelligence Index, it rivals gpt-oss-120b, which has a score of 24, even though it uses 15 times fewer total parameters and 4 times fewer active parameters. This exceptional efficiency in parameters comes with a cost, as it demands a hefty 213 million output tokens to finalize the Intelligence Index evaluation. Moreover, Ling 3.0 Tiny shows significant progress in mitigating hallucination rates when compared to Ling-mini-2.0; it boosts its AA-Omniscience score by an impressive 59 points while maintaining consistent accuracy. Rather than resorting to random guesses in uncertain scenarios, the model opted to attempt only 37% of the posed questions during assessment, which resulted in a drastically lowered hallucination rate of 30%, a substantial improvement from the previous generation's staggering 96%. This strategic decision not only underscores the model's enhanced reasoning abilities but also emphasizes its potential for practical applications in the real world. Overall, Ling 3.0 Tiny exemplifies a significant step forward in the development of efficient and reliable AI models.

Claude Mythos Preview is a cutting-edge AI model that represents a significant breakthrough in cybersecurity capabilities and autonomous reasoning. It has shown the ability to independently discover and exploit zero-day vulnerabilities in a wide range of systems, including operating systems, browsers, and critical infrastructure software. The model can generate sophisticated exploit chains, combining multiple vulnerabilities to achieve outcomes such as remote code execution or full system control. It operates using agentic workflows, where it analyzes source code, tests hypotheses, and iteratively refines its findings without human guidance. Mythos Preview is also highly capable in reverse engineering, allowing it to analyze closed-source binaries and uncover hidden vulnerabilities. Compared to previous models, it demonstrates a substantial increase in both accuracy and success rate when developing real-world exploits. It can identify subtle and long-standing bugs that have gone unnoticed for years. The model is also effective at converting known vulnerabilities into working exploits rapidly, reducing the time between disclosure and potential attack. These capabilities highlight both the opportunities and risks associated with advanced AI in cybersecurity. As a result, efforts like Project Glasswing aim to use the model to strengthen global defenses. The model’s emergence signals a shift toward automated, large-scale vulnerability research. Overall, Claude Mythos Preview marks a transformative step in how AI can impact both offensive and defensive cybersecurity.

Claude Fable 5.5 is a possible future addition to Anthropic's premium Claude Fable model line, but Anthropic has not officially announced a model under that name as of September 30, 2026. The company's current model catalog identifies Claude Fable 5.1 as the latest Fable model, alongside the newer Claude Opus 5.5 and Claude Sonnet 5.5. Anthropic positions Fable 5.1 for demanding reasoning and long-horizon agentic work requiring its highest-end model capabilities. The model provides a 1-million-token context window and supports outputs of up to 128,000 tokens. Fable 5.1 uses adaptive thinking that is always enabled and defaults to a high effort setting for more intensive reasoning. It accepts text and images as input and generates text output, with Anthropic listing June 2026 as both its reliable knowledge cutoff and training-data cutoff. Fable 5.1 costs $10 per million input tokens and $50 per million output tokens, with cache reads priced at $0.25 per million tokens. It is officially supported through the Claude API, Amazon Bedrock, Google Cloud, Microsoft Foundry, and Claude Platform on AWS. Anthropic's September releases have expanded the Claude 5.5 generation with Opus 5.5 and Sonnet 5.5, but its official model catalog has not similarly replaced Fable 5.1 with Fable 5.5. Anthropic's Trust Center likewise lists documentation for Claude Mythos 5.1 and Fable 5.1 as well as Opus 5.5 and Sonnet 5.5, without documentation for a Fable 5.5 model. Consequently, claims about Claude Fable 5.5's performance, benchmarks, context limits, pricing, capabilities, or release date should be considered unconfirmed until Anthropic publishes official information.