Choose AI Model Based on Their Capabilities
Key Model Capabilities to Consider
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
Claude
Gemini
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.
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