> For the complete documentation index, see [llms.txt](https://help.doraverse.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://help.doraverse.com/help/faqs/chat-with-ai-models/choose-ai-model-based-on-their-capabilities.md). # Choose AI Model Based on Their Capabilities [Key Model Capabilities to Consider](#key-model-capabilities-to-consider) [Model Capabilities Breakdown](#model-capabilities-breakdown) ## Key Model Capabilities to Consider ### Modalities (Input & Output) Modality means the type or format of data an AI model can process as input and produce as output. ### Maximum Context Length (Context Window) Context Length refers to how much information (e.g., words or tokens) a model can “consider” at once when producing an output. * **Short Context (up to \~1,000 tokens):** Perfect for simple tasks like answering short queries, classifying emails, or recognizing objects in small images. * **Medium Context (around 2,000-4,000 tokens):** Useful for handling longer documents, paragraphs, or multi-turn conversations, like summarizing reports or supporting chatbots. * **Long Context (10,000+ tokens or more):** Enables deep understanding of lengthy documents, complex conversations, or large datasets. It ideal for legal document reviews, books, or extensive customer support dialogs. **Tips:** Match context length to your workload. Longer contexts improve understanding but usually come with higher computational cost and latency. ## Intelligence Intelligence refers to how effectively an AI model understands, processes, and generates information to meet your task requirements. It encompasses accuracy, contextual comprehension, reasoning ability, and adaptability. One common way to measure intelligence is the **MMLU (Massive Multitask Language Understanding)** score, which evaluates a model’s performance across a wide range of academic and professional tasks. Higher MMLU scores indicate stronger language comprehension and problem-solving abilities, helping you gauge which model best fits your needs. ### Speed and Latency Speed is how fast an AI model can complete a given task or process a piece of data. Latency is the delay between submitting a request to the AI model and receiving a response. These performance aspects are critical depending on the use case: for example, real-time applications like chatbots require low latency for seamless interactions, while batch processing tasks can tolerate higher latency. **Tips:** Understanding speed and latency helps you select models that deliver timely responses without overloading resources. ### Cost Cost depends on the amount of data processed by the AI model, measured in tokens. Input token cost is the expense for processing the data you send, while output token cost covers the tokens the model generates in response. These are usually billed per 1,000 tokens. **Tips:** Understanding token usage helps you manage expenses and get the most value from your AI. ### Key Features AI models often come with specialized features and skills designed to enhance their performance on particular tasks. Understanding these unique capabilities can help you choose a model that fits your needs precisely. ### **Best Suited Use Cases** Different AI models shine in different applications. Matching their strengths with your business needs is key. ## Model Capabilities Breakdown You can click a model name in the list below to jump to a detailed overview of its strengths and use cases. **GPT** * [GPT-4o](#gpt-4.0) * [GPT-4.1](#gpt-4.1) * [GPT-4.1-mini](#gpt-4.1-mini) * [o3-mini Thinking](#o3-mini-thinking) * [o4-mini (Thinking)](#o4-mini-thinking) **Claude** * [Claude 3.5 Hailku](#claude-3.5-hailku) * [Claude 3.7 Sonnet](#claude-3.7-sonnet) **Gemini** * [Gemini 2.5 Flash](#gemini-2.5-flash) * [Gemini 2.5 Pro](#gemini-2.5-pro) *** ### GPT-4.0 OpenAI GPT-4o is a multimodal model that supports text and images. It responds in real time and works well for lightweight development tasks and conversational prompts. **Multimodal support** * Input: Text, Image * Output: Text **Context Window:** 128,000 tokens **Intelligence: Higher** * MMLU score: **Speed: Medium** * token/s **Latency: Lower** * s **Price ($/M token)** * $2.50 input / $10.00 output **Best suit:** * Suitable for complex tasks, deep understanding, multi-step instructions *** ### GPT-4.1 This OpenAI’s latest model outperforms GPT-4o across the board, with major gains in coding, instruction following, and long-context understanding. It has a larger context window and features a refreshed knowledge cutoff of June 2024. OpenAI has optimized GPT-4.1 for real-world use based on direct developer feedback about: frontend coding, making fewer extraneous edits, following formats reliably, adhering to response structure and ordering, consistent tool usage, and more. This model is a strong default choice for common development tasks that benefit from speed, responsiveness, and general-purpose reasoning. **Multimodal support** * Input: Text, Image * Output: Text **Context Window:** 1,047,576 tokens **Intelligence: Higher** * MMLU score: 0.806 **Speed: Medium** * 120.9 token/s **Latency: Lower** * 0.57s **Price ($/M token)** * $2.00 input / $8.00 output **Best suit:** * Complex tasks and cross-domain problem solving * Excels in agentic coding tasks including frontend development, precise diff-based edits, consistent tool integration, and strict instruction adherence. *** ### GPT-4.1-mini GPT-4.1 mini provides a balance between intelligence, speed, and cost that makes it an attractive model for many use cases. **Multimodal support** * Input: Text, Image * Output: Text **Context Window:** 1,047,576 tokens **Intelligence: High** * MMLU score: 0.781 **Speed: Slower** * 74.2 token/s **Latency: Lower** Slower * 79.2s **Price ($/M token)** * $0.40 input / $1.60 output **Best suit:** * Suitable for complex tasks, deep understanding, multi-step instructions *** ### o4-mini (Thinking) **Multimodal support** * Input: Text, Image * Output: Text **Context Window:** 200,000 tokens **Intelligence: Higher** * MMLU score: 0.832 **Speed: Faster** * 139.9 token/s **Latency: Higher** * 51.15s **Price ($/M token)** * $1.10 input / $4.40 output **Best suit:** * Efficient performance in coding and visual tasks. *** ### o3-mini (Thinking) **Multimodal support** * Input: Text * Output: Text **Context Window:** 200,000 tokens **Intelligence: Higher** * MMLU score: 0.791 **Speed: Faster** * 189.2 token/s **Latency: Higher** * 13.01s **Price ($/M token)** * $1.10 input / $4.40 output *** ### Llama 4 Maverick **Multimodal support** * Input: Text, Image * Output: Text, Code **Context Window:** 1,048,576 tokens **Intelligence: Higher** * MMLU score: 0.809 **Speed: Faster** * 129.0 token/s **Latency: Lower** * 0.36s **Price ($/M token)** * $0.16 input / $0.60 output **Key Features** * Mixture-of-experts (MoE) architecture * Optimized for vision-language tasks * Instruction-tuned for assistant-like behavior * Image reasoning * Long-context, multiple languages supports * Demonstrates high coding and memorization abilities **Best suit:** * Suited for research and commercial applications requiring advanced multimodal understanding and high model throughput. *** ### Llama 4 Scout **Multimodal support** * Input: Text, Image * Output: Text, Code **Context Window:** 10,000,000 tokens **Intelligence: Higher** * MMLU score: 0.752 **Speed: Faster** * 121.8 token/s **Latency: Lower** * 0.43s **Price ($/M token)** * $0.08 input / $0.30 output **Key Features** * Superior text and visual intelligence * Assistant-style interaction and visual reasoning * Long-context, multiple languages supports * Demonstrates high coding and memorization abilities **Best suit:** * Use in multilingual chat, captioning, and image understanding tasks *** ### Gemini 2.5 Flash **Multimodal support** * Input: Text, Images, Audio, Video * Output: Text **Context Window:** 1,000,000tokens **Intelligence: Higher** * MMLU score: 0.832 **Speed: Faster** * 339.5 token/s **Latency: Higher** * 7.46s **Price ($/M token)** * $0.15 input / $0.60 output **Best suit:** * Responding instantly in live chats and AI assistants * Summarizing emails, documents, and web content * Handling lightweight code or text generation on the fly * Powering fast, embedded AI across mobile and browsers * Scaling to thousands of users without slowing down *** ### Gemini 2.5 Pro **Multimodal support** * Input: Text, Images, Audio, Video * Output: Text **Context Window:** 1,000,000tokens **Intelligence: Higher** * MMLU score: 0.858 **Speed: Faster** * 154.3 token/s **Latency: Higher** * 34.26s **Price ($/M token)** * $1.25 input / $10.00 output **Best suit:** * Solving multi-step technical or logical problems, advanced reasoning in math and science * Analyzing large documents or datasets in context * Generating, debugging, and refactoring code * Assisting with scientific writing, research, and analysis * Creating visually compelling web apps that require structure and long-term memory *** ### Claude 3.5 Hailku **Multimodal support** * Input: Text * Output: Text **Context Window:** 200,000 tokens **Intelligence: Lower** * MMLU score: 0.634 **Speed: Slower** * 64.0 token/s **Latency: Lower** * 0.93s **Price ($/M token)** * $0.80 input / $4.00 output **Key Features** * Optimized for fast, effective responses. * Improved comprehension and precise instruction following. * Delivers high-performance, autonomous coding solutions. * Balanced combination of speed, accuracy, and cost-effectiveness. **Best suit:** * Fast, accurate code completions to boost developer productivity. * Interactive chatbots for customer service, e-commerce, and education. * Efficient extraction and labeling of unstructured data in finance, healthcare, and research. * Real-time content moderation with advanced reasoning for safe, appropriate online communities and media. *** ### Claude 3.7 Sonnet **Multimodal support** * Input: Text * Output: Text **Context Window:** 200,000 tokens **Intelligence: Higher** * MMLU score: 0.837 **Speed: Slower** * 88.2 token/s **Latency: Lower** * 1.64s **Price ($/M token)** * $3.00 input / $15.00 output **Key Features** * Hybrid Reasoning: * Standard Mode for quick responses to simple tasks. * Extended Thinking Mode for detailed, step-by-step reasoning on complex problems. * Adjustable Thinking Budget to balance speed and accuracy. * Full-Stack Development: Supports coding across multiple languages and environments. * Enhanced NLP: Better instruction following and more relevant, useful replies. * Effective with structured data and long-form text. **Best suit:** * Instruction-following task * General reasoning, multimodal capabilities * Agentic coding with extended thinking providing a notable boost in math and science. --- # Agent Instructions This 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