Available Models

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Understanding Model Selection

Cheetah AI provides access to a curated selection of frontier AI models, each optimized for different aspects of software development. Rather than overwhelming you with hundreds of model options, we’ve selected models that excel at coding tasks, offer strong reasoning capabilities, and provide reliable tool usage for agentic workflows. This careful curation ensures that every model available in the dropdown delivers production-quality results for development work. The model selection dropdown in Cheetah AI lets you switch between models based on your current task requirements. Some models excel at rapid iteration with fast response times, while others provide deeper reasoning for complex architectural decisions. Understanding the strengths of each model helps you choose the right tool for each situation, optimizing both the quality of assistance and your development velocity. All models are accessed through our secure infrastructure, which handles authentication and rate limiting transparently. You don’t need to manage API keys or worry about billing. The infrastructure also enables features like request caching and intelligent routing that improve response times and reliability.

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Model Comparison

The following table provides a quick reference for comparing the available models across key dimensions that matter for development work.

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Claude Sonnet 4.5

Claude Sonnet 4.5 is a flagship coding model released in September 2025. It represents the culmination of research into building AI systems that excel at sustained, complex software engineering tasks. The model features a 200,000-token context window with support for up to 64,000 output tokens, making it capable of processing entire codebases and generating comprehensive implementations in a single response. A beta program offers access to a 1-million-token context window for users who need to work with exceptionally large projects. Claude Sonnet 4.5 introduces a hybrid reasoning architecture that allows switching between fast, low-latency responses for simple queries and extended thinking mode for complex problems requiring multi-step reasoning. This flexibility means you get quick answers for straightforward questions while still having access to deep analysis when tackling architectural decisions or debugging subtle issues. The model excels at understanding complex codebases, maintaining consistency across long conversations, and following detailed instructions precisely. Its tool usage capabilities are particularly refined, making it excellent for agentic workflows where the AI needs to coordinate multiple operations like reading files, making edits, and running commands. Key Specifications:

• Context Window: 200,000 tokens (1M in beta)
• Maximum Output: 64,000 tokens
• Reasoning: Hybrid mode with extended thinking

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GPT-5.2 Codex

GPT-5.2 Codex is a specialized coding variant from the GPT-5.2 family, released in December 2025. It provides massive context handling and output generation capabilities optimized specifically for software development tasks. The model represents a response to the growing demand for AI systems capable of handling entire projects rather than individual files. The standout feature of GPT-5.2 Codex is its 400,000-token context window combined with 128,000-token output capacity. This means you can load substantial portions of a codebase into context and receive comprehensive implementations that span multiple files. For large-scale refactoring, migration projects, or generating entire feature implementations, this capacity is transformative. The model supports reasoning mode with configurable effort levels, allowing you to balance response speed against depth of analysis. For quick iterations, lower effort settings provide fast responses. For complex architectural decisions or debugging challenging issues, higher effort settings engage more thorough reasoning processes. GPT-5.2 Codex demonstrates strong performance on software engineering benchmarks and excels at understanding project structure, maintaining coding conventions, and generating idiomatic code across many programming languages. Its training includes extensive exposure to modern frameworks and libraries, making it particularly effective for contemporary web and application development. Key Specifications:

• Context Window: 400,000 tokens
• Maximum Output: 128,000 tokens
• Reasoning: Toggle with effort levels

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Kimi K2

Kimi K2 is a flagship model featuring a trillion-parameter Mixture-of-Experts architecture with 32 billion parameters active during inference. Released with significant updates in late 2025, it has gained recognition for exceptional performance in agentic tasks and long-context scenarios. The model’s 256,000-token context window maintains coherence without the typical degradation seen in other long-context models. This makes it particularly effective for extended coding sessions where earlier context remains relevant throughout the conversation. The model can handle 200-300 sequential tool calls without performance degradation, far exceeding the 30-50 call limit typical of other models. Kimi K2 supports a reasoning toggle that switches between thinking and non-thinking variants. When reasoning is enabled, the model exposes its thought process through a dedicated reasoning field, allowing you to understand how it approaches problems. This transparency is valuable for learning and for verifying that the model’s approach aligns with your intentions. The model demonstrates particular strength in complex tool orchestration scenarios, making it excellent for agentic workflows that require coordinating multiple operations. Its performance on browsing and research tasks also stands out, useful when you need the AI to gather information from documentation or explore unfamiliar APIs. Key Specifications:

• Context Window: 256,000 tokens
• Maximum Output: 64,000 tokens
• Architecture: 1T parameters MoE (32B active)
• Reasoning: Toggle between thinking/non-thinking variants

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DeepSeek V3

DeepSeek V3 is an open-source Mixture-of-Experts model with 671 billion total parameters and 37 billion active per token. Despite being open-source, it delivers performance comparable to leading models while offering significantly lower costs, making it an excellent choice for high-volume development work. The model supports a 128,000-token context window, sufficient for most development tasks including working with multiple related files simultaneously. Its 8,000-token output limit is more constrained than some alternatives, but adequate for typical code generation and explanation tasks. DeepSeek V3 supports Fill-in-Middle (FIM) completion through a dedicated endpoint, making it particularly effective for code completion scenarios where you need the model to generate code that fits between existing sections. This capability integrates well with inline editing workflows. The model’s cost efficiency makes it attractive for iterative development where you might make many requests during a coding session. The lower per-token costs mean you can experiment freely without concern about accumulating significant charges, encouraging the kind of exploratory interaction that often leads to better solutions. Key Specifications:

• Context Window: 128,000 tokens
• Maximum Output: 8,192 tokens
• Architecture: 671B parameters MoE (37B active)
• Reasoning: Not available
• FIM Support: Yes

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MiniMax M2.1

MiniMax M2.1 is a state-of-the-art open-source model released in December 2025, specifically optimized for agentic capabilities. With 230 billion total parameters but only 10 billion active during inference, it achieves frontier-class performance while maintaining computational efficiency. The model is designed from the ground up to function as a “Digital Employee” capable of handling end-to-end workflows in production environments. It excels at coding, tool use, instruction following, and long-horizon planning, the core capabilities required for autonomous development assistance. MiniMax M2.1 features always-on reasoning with thinking exposed through dedicated tags in the output. This means you always see the model’s reasoning process, providing transparency into how it approaches problems. The reasoning is particularly thorough for multi-step tasks and complex debugging scenarios. The model demonstrates exceptional performance in multi-language programming, including strong capabilities in Rust, Java, Go, C++, TypeScript, and JavaScript. It also shows particular strength in web development, mobile app development for both Android and iOS, and office automation scenarios. Key Specifications:

• Context Window: 200,000 tokens
• Maximum Output: 64,000 tokens
• Architecture: 230B parameters MoE (10B active)
• Reasoning: Always on with think tags

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Grok 4

Grok 4 is a flagship model released in July 2025, designed for users who demand both deep reasoning and strong performance. The model features a 256,000-token context window and excels at step-by-step problem solving, mathematics, logic, and precise instruction following. Unlike models with optional reasoning, Grok 4 always engages its reasoning capabilities. This means every response benefits from thorough analysis, though the reasoning process is internal rather than exposed in the output. This design choice prioritizes response quality over transparency into the thinking process. The model supports parallel tool calling and multimodal input including both text and images. These capabilities make it versatile for development workflows that involve analyzing screenshots, diagrams, or visual documentation alongside code. Key Specifications:

• Context Window: 256,000 tokens
• Maximum Output: 100,000 tokens
• Reasoning: Always on (internal)
• Multimodal: Text and images

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Grok 4.1 Fast

Grok 4.1 Fast is a speed-optimized variant offering an extraordinary 2-million-token context window at dramatically lower costs than Grok 4. This massive context capacity opens possibilities for working with entire large codebases in a single conversation. The model supports a reasoning toggle, allowing you to enable deeper analysis when needed while maintaining fast response times for straightforward queries. When reasoning is disabled, responses are particularly quick, making it excellent for rapid iteration cycles. The 2-million-token context window is particularly valuable for understanding large, interconnected codebases where relationships between distant components matter. You can load entire projects into context and ask questions that require understanding the full system architecture. Key Specifications:

• Context Window: 2,000,000 tokens
• Maximum Output: 100,000 tokens
• Reasoning: Toggle (on/off)

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Choosing the Right Model

Selecting the appropriate model depends on your specific task requirements, balancing factors like context needs, reasoning depth, response speed, and cost. For complex architectural work and detailed code review, Claude Sonnet 4.5 and Grok 4 provide the deepest reasoning capabilities. Their thorough analysis is worth the additional response time when you need to make important decisions or understand subtle issues. For working with large codebases, GPT-5.2 Codex and Grok 4.1 Fast offer the largest context windows. GPT-5.2 Codex provides 400K tokens with strong reasoning, while Grok 4.1 Fast offers 2M tokens at lower cost when you need maximum context capacity. For agentic workflows with many tool calls, Kimi K2 and MiniMax M2.1 excel at coordinating complex sequences of operations. Their architectures are specifically optimized for the kind of multi-step execution that characterizes autonomous development assistance. For cost-sensitive high-volume work, DeepSeek V3 and Grok 4.1 Fast provide strong capabilities at lower per-token costs. This makes them excellent choices for iterative development where you expect to make many requests. For general-purpose development with balanced capabilities, any of the available models will serve well. The differences become most apparent at the edges of capability, when you’re pushing context limits, requiring deep reasoning, or coordinating complex tool sequences.