Best Large Language Models for Claude Code - Page 2

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.

MiniMax M2.5 is a frontier AI model engineered for real-world productivity across coding, agentic workflows, search, and office tasks. Extensively trained with reinforcement learning in hundreds of thousands of real-world environments, it achieves state-of-the-art performance in benchmarks such as SWE-Bench Verified and BrowseComp. The model demonstrates strong architectural thinking, decomposing complex problems before generating code across more than ten programming languages. M2.5 operates at high throughput speeds of up to 100 tokens per second, enabling faster completion of multi-step tasks. It is optimized for efficient reasoning, reducing token usage and execution time compared to previous versions. With dramatically lower pricing than competing frontier models, it delivers powerful performance at minimal cost. Integrated into MiniMax Agent, M2.5 supports professional-grade office workflows, financial modeling, and autonomous task execution.

MiniMax M2.7 is an advanced AI model designed to enhance real-world productivity across coding, search, and office workflows. It is trained with reinforcement learning across numerous real-world environments, enabling it to handle complex, multi-step tasks effectively. The model excels in problem-solving by breaking down challenges before generating solutions across multiple programming languages. It delivers high-speed performance with rapid token generation, allowing tasks to be completed efficiently. With optimized reasoning and cost-effective pricing, it provides powerful capabilities while minimizing resource usage. It also achieves strong performance in software engineering benchmarks, reducing incident response time and improving development efficiency. Additionally, it supports advanced agentic workflows and professional-grade office tasks, making it highly versatile for modern work environments.

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.

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.

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.

Claude Opus 4.1 is an incremental upgrade to Claude Opus 4 that boosts coding, agentic reasoning, and data-analysis performance without changing deployment complexity. It raises coding accuracy to 74.5 percent on SWE-bench Verified and sharpens in-depth research and detailed tracking for agentic search tasks. GitHub reports notable gains in multi-file code refactoring, while Rakuten Group highlights its precision in pinpointing exact corrections within large codebases without introducing bugs. Independent benchmarks show about a one-standard-deviation improvement on junior developer tests compared to Opus 4, mirroring major leaps seen in prior Claude releases.

Claude Opus 4.5 is Anthropic’s newest flagship model, delivering major improvements in reasoning, coding, agentic workflows, and real-world problem solving. It outperforms previous models and leading competitors on benchmarks such as SWE-bench, multilingual coding tests, and advanced agent evaluations. Opus 4.5 also introduces stronger safety features, including significantly higher resistance to prompt injection and improved alignment across sensitive tasks. Developers gain new controls through the Claude API—like effort parameters, context compaction, and advanced tool use—allowing for more efficient, longer-running agentic workflows. Product updates across Claude, Claude Code, the Chrome extension, and Excel integrations expand how users interact with the model for software engineering, research, and everyday productivity. Overall, Claude Opus 4.5 marks a substantial step forward in capability, reliability, and usability for developers, enterprises, and end users.

SubQ is a large language model developed by Subquadratic, designed specifically for long-context reasoning tasks. It can process up to 12 million tokens in a single prompt, allowing it to analyze entire codebases, long histories, and complex datasets at once. The model uses a sub-quadratic sparse-attention architecture that improves efficiency by focusing only on the most relevant relationships in the data. This approach reduces computational overhead while maintaining strong performance on large-scale tasks. SubQ is optimized for use cases such as software engineering, coding agents, and long-context retrieval. It delivers fast processing speeds and operates at a lower cost compared to many traditional models. Developers can access SubQ through APIs or integrate it into coding tools for enhanced workflows. Its architecture enables scalable AI reasoning without the limitations of standard transformer models.

SubQ 1.1 Small is a long-context AI model from Subquadratic designed to reason over complete enterprise artifacts such as codebases, document collections, contracts, and financial filings. It uses Subquadratic Sparse Attention, or SSA, to reduce the high compute costs normally associated with processing very large context windows. The model delivers near-perfect long-context retrieval across 1M, 2M, 6M, and 12M token tests while using far less attention compute than dense attention. SubQ 1.1 Small also maintains strong general reasoning, coding, knowledge, and agentic task performance across multiple benchmarks. Its capabilities make it useful for financial analysis, legal review, contract work, software engineering, due diligence, and other workflows where information is spread across large artifacts. SubQ is built for organizations that want to move beyond fragmented retrieval pipelines and enable direct reasoning over massive bodies of information.

Ming-Flash Omni 2.0 is a full-modal large language model from Ant Group, built on a unified multimodal architecture with “modal unity + task unity” as its core design philosophy. As part of the Ming series, it is designed to achieve cross-modal understanding and generation across text, images, audio, and video, allowing one model to see, hear, speak, and draw instead of relying on multiple specialized models. Ming-Flash Omni 2.0 follows the evolution of Ming-Light Omni and Ming-Flash Omni Preview, moving from unified architecture validation and hundred-billion-parameter scaling to a Data Scaling strategy that achieves open-source SOTA performance on multiple benchmarks. The model integrates four core capability modules: image-text understanding, video analysis, speech synthesis, and image generation or editing. For image-text understanding, Ming introduces structured knowledge graphs for fine-grained visual perception.

Claude Mythos Preview is a highly advanced AI model developed with strong capabilities in cybersecurity, particularly in identifying and exploiting software vulnerabilities. It demonstrates the ability to autonomously discover zero-day vulnerabilities across major operating systems, browsers, and critical software systems. The model can also generate complex exploit chains, including privilege escalation and remote code execution attacks. Its capabilities extend beyond vulnerability detection to reverse engineering and exploit development in both open-source and closed-source environments. Mythos Preview operates through agentic workflows, enabling it to analyze codebases, test hypotheses, and validate exploits independently. These abilities represent a significant leap compared to previous models, which struggled with exploit generation. Overall, Claude Mythos Preview highlights a new era where AI can both strengthen and challenge global cybersecurity practices.