Pick your LLM
Explore and compare the most popular Large Language Models (LLMs) — from GPT to Claude and beyond — and discover which one works best for you.
Last updated
Explore and compare the most popular Large Language Models (LLMs) — from GPT to Claude and beyond — and discover which one works best for you.
Last updated
Large Language Models (LLMs) are AI models trained on vast amounts of text data to understand and generate human-like language. They can perform a variety of language-related tasks, such as text completion, translation, summarization, and question answering. LLMs are designed to understand patterns in language, making them useful for a broad range of applications in natural language processing (NLP).
Difference Between LLM and R-LLM:
LLM (Large Language Model): Focuses on understanding and generating text based on learned patterns from data. It excels at tasks like text generation, translation, summarization, and basic question answering, but may struggle with tasks that require complex reasoning or multi-step problem solving.
R-LLM (Reasoning-Enabled Language Model): A type of LLM designed with enhanced reasoning capabilities. R-LLMs can handle complex, multi-step tasks such as logical deduction, mathematical problem-solving, and decision-making. They explicitly break down their thought process, allowing them to handle tasks that require more than just text generation, offering clear, reasoned explanations for their responses.
Here’s a brief overview of the latest AI models for your reference. Below the image, you'll find a more detailed explanation covering LLMs, R-LLMs, Hosting Preferences, Speed vs. Depth, and a comprehensive breakdown of each available LLM on Blockbrain.
Use Case
LLM Models
Creative Writing & Storytelling
Claude 3.7 Sonnet, DeepSeek R1, GPT-4 Omni
Mathematical & Logical Reasoning
GPT-4 Omni, DeepSeek R1, Claude 3.7 Sonnet
Technical & Research Writing
GPT-4 Omni, Gemini 2.0 Flash
Conversational AI & Chatbots
GPT-4o Mini, Claude 3.7 Sonnet
Legal & Compliance Analysis
GPT-4 Omni, Mistral Large
Coding & Development
GPT-4 Omni, DeepSeek R1, Claude 3.7 Sonnet
Enterprise-Level Processing (Long Contexts)
Claude 3.7 Sonnet (Thinking Mode), Llama 3.2 90B, GPT 4 Omni
Fast, Low-Cost AI Tasks
GPT 4o Mini, Gemini 2.0 Flash, DeepSeek Reasoner (R1)
Use Cases
R-LLM Models
Prompt Samples
Advanced Creative Writing, Legal Reasoning, Strategic Planning
Claude 3.7 Sonnet (Thinking mode) US
Create a dialogue scene between two characters in a high-stakes political setting. One character is trying to persuade the other to make a controversial decision, and the other is grappling with the ethical implications.
Creative Writing, Legal Reasoning (EU), Ethical Decision Making, Financial Analysis
Claude 3.7 Sonnet (Thinking mode) EU
A company based in Germany wants to implement a new customer data collection strategy. How should the company ensure compliance with GDPR when collecting and processing sensitive personal data? What key data protection principles must they follow?
Mathematical Problem Solving, Coding Assistance, Scientific Data Interpretation, Strategic Planning
Gemini 2.0 Flash (Thinking mode) US
What are the key components of a business continuity plan that would help my company prepare for unforeseen disruptions (e.g., natural disasters, economic downturns)?
We offer different hosting options on our platform, with US Hosting not being fully GDPR-compliant by default and EU Hosting ensuring full GDPR compliance. Your choice of hosting affects how your data is handled in compliance with regional privacy laws.
Factor
US Hosting
EU Hosting
GDPR Compliance
Not GDPR-compliant by default
Fully GDPR-compliant
Data Residency
Data stored in the US
Data stays in the EU
Latency (for EU users)
Higher latency due to transatlantic data transfer
Lower latency for EU users
Model Availability
More models and features available first
Some models/features released later
Legal & Regulatory Risks
Subject to US laws
Meets stricter EU privacy laws
Summary:
Choose EU hosting if you need GDPR compliance, lower latency in Europe, and strict data privacy.
Choose US hosting for the latest model versions and features, but ensure legal safeguards for data transfers.
Choose between models that prioritize speed for quick responses with low latency, or depth for more detailed and structured answers that require extra processing time.
Preference
Models
High Speed (Fast Answers, Low Latency)
Gemini 2.0 Flash, GPT-4o Mini
High Depth (More Detailed, Structured)
GPT-4 Omni, Claude 3.7 Sonnet, Mistral Large, Llama 3.2 90B
Claude 3.7 Sonnet
Highlights:
Top Coding Performance: Excels in coding-related tasks, with strong accuracy and speed.
Hybrid Reasoning: Supports both fast and deep thinking modes for various types of tasks.
Self-Correcting: Automatically fixes errors when encountered during tasks.
Advanced Document Analysis: Analyzes complex documents and extracts key information.
Limitations:
Not Optimized for Math/Puzzle Solving: May not be as effective in academic or puzzle-based challenges
Slower for Simple Queries: May take longer for simpler or straightforward questions.
Best for:
Complex Coding and Debugging: Ideal for tackling advanced coding problems.
In-Depth Data Analysis: Excellent for analyzing large datasets or performing complex computations.
Multi-Step Tasks: Useful for tasks that require careful planning or step-by-step execution.
Software Engineering: Provides strong support for software-related challenges.
Host: EU, US
Cost:
Input Token (These are the tokens you send to the model): $3 per million tokens
Output Token (These are the tokens the model generates as a response): $15 per million tokens
Claude 3.5 Sonnet v2
Highlights:
Improved Task Automation: Designed to automate a variety of enterprise-level tasks, offering robust support for operational workflows.
Advanced Tool & API Integration: Capable of handling complex tool and API interactions, making it highly adaptable for various business needs.
Computer UI Navigation (Beta): Features beta functionality for navigating and interacting with computer UIs, expanding its usability for more technical processes.
Limitations:
Less Coding Performance: Lags behind Claude 3.7 Sonnet in coding tasks, especially when tackling complex coding and debugging challenges.
No Extended Thinking Mode: Lacks deep reasoning capabilities for long, multi-step queries, which can make it less effective for highly intricate tasks.
Best for:
Enterprise Task Automation: Ideal for automating repetitive processes and administrative tasks across large organizations.
Software Development: Useful for development tasks, though it may not be as efficient as higher-tier models like Claude 3.7 Sonnet in debugging and complex coding.
DevSecOps Support: Supports security-focused tasks, automating checks and processes to secure development pipelines.
Host: EU, US
Cost:
Input Token (These are the tokens you send to the model): $3.00 per million tokens
Output Token (These are the tokens the model generates as a response): $15.00 per million tokens
Claude 3.5 Haiku
Highlights:
Speed-Optimized Next-Gen Model: Built for fast performance, delivering quick responses for high-demand tasks.
Strong Performance for Size: Comparable to Claude 3 Opus in terms of performance, offering robust capabilities in a compact model.
High Coding Proficiency: Capable of handling coding tasks with strong accuracy and efficiency.
Efficient with Large Data Processing: Excels in processing and managing large datasets, making it suitable for big data tasks.
Cost-Effective Solution: Offers a competitive performance-to-cost ratio, ideal for organizations looking for efficiency and affordability.
Limitations:
Less Capable of Complex Reasoning Tasks: Not as effective as higher models like Sonnet for deep, multi-step reasoning.
Lower Precision vs. Sonnet: May not achieve the same level of detail and accuracy as Claude 3.7 Sonnet in complex tasks.
Text-Only at Launch: Does not support image processing at the time of launch.
Limited Creative Writing Abilities: Less suitable for tasks requiring complex creative writing or storytelling.
Best for:
High-Volume Applications: Perfect for tasks that require frequent and fast processing, such as handling many queries at once.
Customer Chatbots: Ideal for automating customer service interactions with quick, accurate responses.
Real-Time Document Summarization: Efficient at summarizing large documents quickly and accurately.
Personalized Content Generation: Suitable for creating customized content, such as tailored messages or reports.
Routine Task Automation: Great for automating repetitive tasks in business operations.
Host: EU, US
Cost:
Input Token (These are the tokens you send to the model): $1.00 per million tokens
Output Token (These are the tokens the model generates as a response): $5.00 per million tokens
Gemini 2.0 Flash (Lite)
Highlights:
Cost-Efficient: Most affordable Gemini model, offering great performance at a low cost.
Low Latency: Fast response times, ideal for real-time interactions.
Improved Performance: Outperforms 1.5 Flash with stronger benchmark scores and improved coding abilities.
Multimodal Input Support: Can handle text, images, and other input types, expanding its versatility.
Energy-Efficient Design: Optimized to use less power, making it an eco-friendly choice.
Limitations:
Lower Quality vs Larger Models: Performance drops on complex tasks compared to bigger models.
Limited Complex Reasoning: Struggles with tasks that require deep, multi-step reasoning.
No Thinking Mode: Lacks advanced reasoning modes, limiting its ability to handle extended tasks.
Text-Only Output Initially: Does not support multimedia outputs like images or video at launch.
Potential Context Limitations: May have trouble managing long or highly detailed inputs.
Less Effective for Creative Tasks: Not ideal for tasks that need creative or nuanced writing.
Occasional Generic Responses: Some answers may lack specificity.
Best for:
High-Volume Applications: Perfect for environments that require processing a large number of tasks quickly.
Budget-Conscious Deployments: Excellent for cost-effective projects without compromising too much on performance.
Real-Time Systems: Well-suited for interactive systems such as chatbots or live support.
Content Generation at Scale: Ideal for producing large volumes of content efficiently.
Basic Task Automation: Suitable for automating routine tasks and processes.
Host: EU
Cost:
Input Token (These are the tokens you send to the model): $0.075 per 1 million tokens
Output Token (These are the tokens the model generates as a response): $0.30 per 1 million tokens
Mistral Large
Highlights:
Technical Problem-Solving and Scientific Analysis: Excels in complex tasks that require strong reasoning capabilities, including synthetic text generation, code generation, and scientific reasoning.
Efficient Reasoning: Provides a cost-effective alternative to larger models, offering robust reasoning skills without compromising performance.
Handling Large Datasets: Capable of performing detailed analysis on large datasets, making it ideal for data-intensive applications.
Limitations:
Slower Than Speed-Focused Models: Not as fast as models optimized for rapid responses.
Limited Expertise in Specialized Fields: May not perform as well in highly specialized technical areas that require deep subject-matter knowledge.
Best for:
Data-Driven Analysis: Ideal for applications in business and science that require in-depth data processing and analysis.
Automated Reporting & Decision-Making Support: Supports automated processes for report generation and decision-making, leveraging its reasoning capabilities.
Machine Learning Tasks: Well-suited for tasks such as code generation and mathematical reasoning, making it a solid choice for ML workflows.
Tech-Focused Customer Support: Excellent for automating tech-related customer support, particularly with its multilingual capabilities and strong reasoning.
Host: EU, US
Cost:
Input Token (These are the tokens you send to the model): $8.00 per 1 million tokens
Output Token (These are the tokens the model generates as a response): $24.00 per 1 million tokens
Mistral NeMo
Highlights:
Advanced Reasoning and World Knowledge: Strong in complex language understanding and generation, with extensive world knowledge.
Large Context Window: 128k token context window for better processing of long-form content and multi-turn conversations.
Multilingual: Supports languages like English, French, German, Spanish, Chinese, and more, making it suitable for global applications.
Quantization Awareness: Supports FP8 inference for efficient deployment and reduced memory usage.
Function Calling: Executes specific functions based on natural language inputs, enhancing interactions.
Efficient Tokenization: Uses Tekken tokenizer for better compression efficiency in text and code.
Limitations:
Inaccurate Responses: Can generate inaccurate answers when lacking specific knowledge.
Language Limitations: May not perform as well with certain languages or dialects.
Complex Instruction Following: Struggles with highly complex instructions compared to larger models.
Best for:
Content Creation: Excellent for generating articles, posts, and scripts.
Data and Sentiment Analysis: Great for analyzing customer feedback and making data-driven decisions.
Multilingual Applications: Ideal for global customer service chatbots and translation tasks.
Coding and Summarization: Useful for coding tasks and text summarization, especially for developers and researchers.
Enterprise AI Solutions: Cost-effective AI solution for businesses, especially for on-premises deployment.
Host: EU
Cost:
Input Token (These are the tokens you send to the model): $2.00 per million tokens
Output Token (These are the tokens the model generates as a response): $6.00 per million tokens
Mistral Codestral
Highlights:
Multilingual Support: Proficient in over 80 programming languages, including Python, Java, C, C++, JavaScript, and specialized languages like Swift and Fortran, making it versatile for developers working on diverse projects.
Code Generation & Completion: Excels at automating code generation, completing partial code, generating test cases, and correcting code errors, streamlining development and reducing bug risks.
Efficiency & Speed: Codestral 25.01 is lightweight and optimized for low-latency, high-frequency use cases, delivering faster code generation and completion compared to previous versions, making it ideal for real-time applications.
Open-Weight Model: Being an open-weight model, its learned parameters are accessible for research and non-commercial use, encouraging collaboration and innovation within the AI community.
Limitations:
Resource Requirements: While Codestral 25.01 is more efficient, it still requires substantial computational resources, especially for large-scale applications.
Public Testing: As a relatively new model, it has not undergone extensive public testing, which might limit its widespread adoption until further evaluations are conducted.
Limited Multimodal Output: Focuses primarily on text-based code generation and does not support generating multimedia outputs like images or videos.
Best for:
Code Development & Debugging: Ideal for automating code completion, generating test cases, and debugging existing code, enhancing developer productivity.
Multilingual Projects: Suitable for projects involving multiple programming languages, with support for over 80 languages, adapting to diverse coding environments.
Real-Time Applications: Great for real-time applications requiring quick code generation and completion, such as live coding sessions or rapid prototyping.
Educational Tools: A valuable tool for developers looking to improve coding skills, reduce errors, and receive accurate code suggestions and corrections.
Host: EU
Cost:
Input Token (These are the tokens you send to the model): $0.30 per million input tokens
Output Token (These are the tokens the model generates as a response): $0.90 per million output tokens
GPT 4.5 - (Very Expensive - 10-15x more expensive than GPT4o! Subtle improvement on GPT 4 Omni. Better emotional intelligence, writing skills, and creative ideation for Chat Messages)
Highlights:
Enhanced Accuracy & Multimodal Capabilities: Improved accuracy and support for both text and image interpretation, including file and image uploads, making it ideal for visual data analysis.
Natural Conversations & Emotional Intelligence: Designed for more natural interactions, GPT-4.5 incorporates emotional intelligence, enabling it to respond appropriately to emotional cues, creating more human-like engagement.
Broader Knowledge Base: Features an expanded understanding across various topics, offering detailed insights and more relevant information.
Reduced Hallucinations: Significant reduction in hallucinations compared to previous models, making it more reliable for critical applications that require factual accuracy.
Multilingual Proficiency: Performs excellently in multiple languages, outperforming GPT-4o in multilingual tasks.
Limitations:
Lack of Chain-of-Thought Reasoning: Unlike o-series models, GPT-4.5 does not perform detailed step-by-step logical reasoning, limiting its ability to handle tasks requiring complex logic analysis.
Speed & Resource Requirements: While faster than some predecessors in certain tasks, it requires substantial computational resources and can be slower due to its size and complexity, making local deployment challenging without robust infrastructure.
No Multimodal Output: Currently, it does not support generating audio or video outputs, limiting its use in multimedia content creation.
Best for:
Creative Writing & Content Generation: Perfect for creative writing, content summarization, and generating compelling headlines, thanks to its enhanced creativity and conversational style.
Conversational AI & Customer Support: Well-suited for building conversational AI systems and customer support tools, leveraging emotional intelligence to manage nuanced language tasks.
Multilingual Applications: Ideal for global customer service platforms and educational tools requiring multilingual support.
Research & Education: Great for research and education, providing detailed insights and summaries on a wide range of topics.
Host: US
Cost:
Input Token (These are the tokens you send to the model): $75.00 per 1 million tokens
Output Token (These are the tokens the model generates as a response): $150.00 per 1 million tokens
GPT-4 Omni
Highlights:
Multimodal Input/Output: Supports a wide range of inputs and outputs, including text, images, audio, and video, enabling versatile interactions and enhanced user engagement across different media types.
Ultra-Fast Response: Optimized for rapid responses, with an average audio response latency of 320 milliseconds, making it ideal for real-time applications such as voice-activated systems and interactive storytelling.
Strong Multilingual Capabilities: Communicates effectively across multiple languages, supporting real-time translations and enhancing global usability.
Enhanced Vision and Audio: Improved ability to process and understand visual and audio inputs, making it perfect for media-based tasks like image analysis, video descriptions, and audio content analysis.
Limitations:
Text Reasoning Similar to GPT-3.5: While strong, its text-based reasoning does not offer substantial improvements over GPT-3.5 when handling complex logical tasks, which may limit its effectiveness in certain specialized applications.
Limited Improvement Over GPT-4: Does not bring significant advancements over GPT-4 in handling complex logical reasoning, which can be a drawback for tasks requiring advanced problem-solving.
Resource Requirements: Requires substantial computational resources, which may pose a challenge for local deployment without access to robust infrastructure.
Best For:
Multimodal Assistance: Perfect for tasks requiring input and output across various media types, such as interactive customer service and multimedia content creation.
Voice and Image Interaction: Ideal for applications where voice and image recognition are key, including voice assistants, image analysis tools, and video description services.
Real-Time Translation: Strong at real-time translation for text and speech, making it a powerful tool for global communication platforms.
Interactive Coding Sessions: Excellent for collaborative coding environments, where quick responses and multimodal input/output are beneficial, such as in coding tutorials and debugging tools.
Host: EU, US
Cost:
Input Token (These are the tokens you send to the model): $2.50 per 1 million tokens
Output Token (These are the tokens the model generates as a response): $10.00 per 1 million tokens
GPT 4o mini
Highlights:
Rapid Responses: Ideal for applications that require quick answers to general knowledge questions, making it perfect for real-time interactions.
Casual Conversations: Well-suited for personal assistants and everyday dialogue, excelling at handling casual conversations.
Content Creation: Efficient at generating blog posts, social media updates, and other text-based content quickly.
Multimodal Support: Supports text and vision inputs, with plans to add video and audio inputs, expanding its capabilities in multimedia applications.
Cost Efficiency: A more affordable option compared to larger models, ideal for budget-conscious projects.
Limitations:
Limited Reasoning: Has limited capability for complex reasoning tasks, making it unsuitable for deep technical analysis or high-level problem-solving.
Technical Analysis: Struggles with advanced coding and specialized scientific reasoning, where more specialized models might perform better.
Context Window Limitations: With a 128K token context window, it may not be sufficient for tasks involving extremely long documents or extended conversations.
Best for:
Social Media Management: Great for generating posts, responding to comments, and managing social media content.
Blog Writing & Content Generation: Ideal for creating blog articles, articles, and other written content quickly.
Basic Customer Service: Effective for answering common questions and handling general customer support tasks.
Personal Assistants: Can manage everyday tasks like scheduling appointments and sending reminders as a personal assistant.
Host: EU, US
Cost:
Input Token (These are the tokens you send to the model): $0.15 per 1 million tokens
Output Token (These are the tokens the model generates as a response): $0.60 per 1 million tokens
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(Nebius) DeepSeek R1
Highlights:
Mixture of Experts (MoE) Architecture: With 671 billion parameters, DeepSeek R1 only activates about 37 billion during each forward pass, optimizing computational efficiency.
Reinforcement Learning & Fine-Tuning: Trained using large-scale reinforcement learning to enhance reasoning, followed by supervised fine-tuning to improve readability and coherence.
State-of-the-Art Performance: Excels in benchmarks, particularly for math, coding, and reasoning tasks, offering performance similar to leading models at a lower operational cost.
Open-Source with Distilled Versions: Open-sourced with six distilled versions ranging from 1.5 to 70 billion parameters, providing flexibility and accessibility for a variety of applications.
Explainability: Capable of articulating its reasoning, providing transparency on how answers are generated.
Limitations:
English Proficiency: Some limitations in English proficiency compared to other models, affecting certain tasks.
Resource Requirements: Running the full DeepSeek R1 model requires significant hardware resources, though the distilled models are more accessible.
Bias and Toxicity: Like many AI models, it can amplify biases and produce toxic responses if not properly fine-tuned or moderated.
Best for:
Advanced Reasoning Tasks: Ideal for complex reasoning, math, coding, and logical tasks, making it well-suited for educational and research environments.
Efficient Deployment: Perfect for organizations looking for cost-effective AI solutions that deliver performance similar to larger models with fewer resource demands.
Multilingual Applications: Strong in Chinese and other languages, ideal for global applications that require language understanding and generation.
Explainable AI: Excellent for applications requiring transparency in decision-making or educational tools, where understanding the model's reasoning is critical.
Host: EU
Cost:
Input Token (These are the tokens you send to the model): $0.80 per 1 million tokens
Output Token (These are the tokens the model generates as a response): $2.40 per 1 million tokens
(Nebius) DeepSeek Chat V3
Highlights:
Mixture-of-Experts (MoE) Architecture: Features 671 billion parameters, with 37 billion active during each token processing, optimizing performance and efficiency.
Speed and Performance: Processes 60 tokens per second, 3x faster than its predecessor, DeepSeek-V2.
Enhanced Capabilities: Improved in instruction following, coding, and reasoning tasks, making it suitable for complex applications.
Open-Source & API Compatibility: Fully open-source with maintained API compatibility, enabling seamless integration into existing systems.
Training Data: Trained on 14.8 terabytes of high-quality tokens, enhancing its language understanding and generation capabilities.
Limitations:
Resource Requirements: Despite its efficiency, DeepSeek-V3 still demands substantial computational resources, particularly for training or fine-tuning.
Bias and Toxicity: Like many AI models, it can amplify biases and produce toxic responses if not properly fine-tuned or moderated.
Multimodal Support: Currently lacks multimodal support, limiting its use for applications that require image or audio processing.
Best for:
Coding and Development: Ideal for coding tasks, code generation, and debugging due to its enhanced capabilities in these areas.
Complex Reasoning Tasks: Suitable for tasks requiring advanced reasoning, including math problems, logical reasoning, and complex text analysis.
Conversational AI: Great for building conversational AI systems that require efficient and accurate text processing.
Cost-Effective Solutions: A cost-effective option for businesses and developers seeking high-performance AI without needing extensive resources.
Host: EU
Cost:
Input Token (These are the tokens you send to the model): $0.40 per 1 million tokens
Output Token (These are the tokens the model generates as a response): $0.89 per 1 million tokens
Jamba Large
Highlights:
Hybrid Architecture: Combines State Space Models (SSMs) with Transformer architectures for greater efficiency and scalability than traditional Transformer models.
Large Context Window: Offers a 256K token context window, one of the largest available under an open license, perfect for handling long-form text and complex conversations.
Efficiency and Speed: Provides up to 3x throughput on long contexts and is 2.5x faster than leading models across all context lengths.
Mixture-of-Experts (MoE) Layers: Uses MoE layers to boost model capacity while reducing computational load, making it more efficient by using fewer active parameters during inference.
Function Calling and Data Interchange: Supports function calling with JSON data, enhancing its ability to interact with complex tasks.
Limitations:
Complex Training: Training Jamba models is resource-intensive and requires careful tuning of the hybrid architecture.
Balancing Performance and Efficiency: Performance can vary depending on the configuration of Transformer and MoE layers, requiring optimization for different tasks.
Bias and Toxicity: Like many models, it may produce biased or toxic responses if not properly fine-tuned or moderated.
Training Resource Demands: While efficient for inference, training and fine-tuning require significant computational resources, even with an 80 GB GPU.
Best for:
Enterprise Applications: Ideal for tasks like content creation, conversational AI, and document analysis at the enterprise level.
Long-Context Tasks: Perfect for analyzing legal documents, conducting academic research, or managing complex customer service interactions.
Multimodal and Multilingual Applications: While focused on text, its efficiency makes it suitable for multimodal and multilingual applications.
Research and Development: Excellent for research environments where handling long contexts and optimizing for specific tasks is crucial.
Host: US
Cost:
Input Token (These are the tokens you send to the model): $2.00 per 1 million tokens
Output Token (These are the tokens the model generates as a response): $8.00 per 1 million tokens
Llama 3.2 90B
Highlights:
Multimodal Capabilities: Supports both text and image inputs, enabling advanced tasks like image captioning, visual question answering, and visual grounding.
Large Context Window: Can handle long-form text and complex conversations with a 128,000 token context.
Advanced Reasoning: Excels in tasks involving general knowledge, text generation, coding, math, and advanced reasoning.
Improved Multilingual Support: Enhanced capabilities for eight languages, including English, German, French, Spanish, and more.
Efficient Architecture: Uses grouped-query attention (GQA) for faster inference, improving efficiency in AI workloads.
Limitations:
Resource-Intensive: Requires at least 180 GB of VRAM for fine-tuning, making it challenging for local setups.
Bias & Toxicity: May produce biased or toxic responses if not properly fine-tuned or moderated.
Complexity Handling: Struggles with tasks involving highly complex technical drawings or precise component detection.
Image Size Limitations: Limited in terms of the maximum image size it can effectively process.
Best for:
Enterprise Applications: Ideal for content creation, conversational AI, and document analysis in enterprise-level settings.
Multimodal Tasks: Excellent for tasks combining text and images, like image captioning and visual reasoning.
Research and Development: Useful in coding, math, and multilingual translation in research environments.
Real-Time Visual Analysis: Perfect for industries like media, healthcare, and education that require real-time visual and text analysis.
Host: US
Cost:
Input Token (These are the tokens you send to the model): $2.04 per million tokens.
Output Token (These are the tokens the model generates as a response): $2.04 per million tokens.
R-LLMS
Claude 3.7 Sonnet (Thinking Mode)
Highlights:
Advanced Decision-Making & Logical Reasoning: Excels in tasks that require deep thought, complex decision-making, and logical analysis.
Mathematical & Coding Expertise: Strong in solving mathematical problems and writing/debugging code with high accuracy.
Creative and Technical Writing: Ideal for generating long-form content, including technical documents and creative writing, with high coherence and depth.
Exceptional Multi-Step Reasoning: Capable of handling intricate, multi-step tasks, ensuring thorough and precise outputs.
Limitations:
Slower Response Time: Due to its advanced reasoning capabilities, it can take longer to process compared to models optimized for speed.
Not Ideal for Quick-Turnaround Tasks: While highly accurate, it may not be the best choice for tasks that demand fast responses or immediate results.
Best for:
Detailed Report Generation: Perfect for creating comprehensive, in-depth reports that require thorough analysis and clarity.
Legal Analysis & Policy Review: Well-suited for examining complex legal texts and policies with a high level of detail and accuracy.
Advanced Customer Support: Excellent for providing in-depth support in technical or specialized fields that require expert-level knowledge.
Strategic Business Decisions: Useful for high-level business decision-making, especially in complex scenarios that require careful reasoning and analysis.
Host: US, EU
Cost:
Input Token (These are the tokens you send to the model): $3 per million tokens
Output Token (These are the tokens the model generates as a response): $15 per million tokens
Gemini 2.0 Flash (Thinking Mode)
Highlights:
Advanced Reasoning & Logical Problem-Solving: Excels in tasks that require deep thought and complex problem-solving.
Scientific Analysis & Data Interpretation: Highly effective in scientific tasks that involve detailed data analysis and interpretation.
Mathematical Problem-Solving & Coding: Strong in solving complex math problems and handling coding tasks.
Consistent Accuracy in Multi-Step Problem-Solving: Performs well in complex, multi-step tasks, ensuring reliable outcomes.
Limitations:
Slower Response Time: Not as fast as models optimized for high-speed answers, as it prioritizes deep reasoning.
Not Ideal for Speed-Focused Tasks: While precise, it may not be suitable for scenarios where speed is the top priority.
Best for:
Research Analysis & Academic Writing: Well-suited for generating detailed reports and academic papers that require thorough analysis.
Complex Math Problems & Engineering Calculations: Great for solving advanced mathematical and engineering problems that require precise solutions.
Multi-Step Logical Puzzles: Perfect for handling complex puzzles or tasks that require logical deduction across multiple steps.
Detailed Reports & Data Insights: Ideal for generating insightful, data-driven reports that require careful reasoning and analysis.
Host: US
Cost: Currently in experimental mode and is free
