⛏️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.

Definition of LLMs (Large Language Models):

Large Language Models (LLMs) are AI systems trained on large amounts of text to understand and generate natural language. They can help with a wide range of tasks like answering questions, summarizing documents, translating text, or drafting content. LLMs recognize patterns in language, allowing them to respond in ways that feel human-like and helpful, making them useful in everything from chatbots and search to business automation and research.

Difference Between LLM and R-LLM:

• LLM (Large Language Model)

  LLMs are trained to understand and generate human-like text based on patterns in data. They're great at tasks like writing, translating, summarizing, and answering straightforward questions. However, they may fall short when tasks require deeper reasoning, step-by-step logic, or complex decision-making.

• R-LLM (Reasoning-Enabled Language Model)

  R-LLMs take things a step further. They’re designed not just to generate text, but to reason through problems. These models can handle more complex tasks like explaining decisions, solving multi-step problems, or making logical inferences by breaking down their thought process and offering more structured, explainable answers.

Overview of LLMs

1. Initial Filtering for Users: Find Primary Use Cases LLMs & R-LLMs

Use Case	LLM Models
Creative Writing & Storytelling	Claude 4.5 Sonnet, GPT-5, Gemini 2.5 Pro
Mathematical & Logical Reasoning	Claude 4.1 Opus, Claude 4.5 Sonnet, GPT-5, Gemini 2.5 Pro, Grok 4
Technical & Research Writing	Claude 4.1 Opus, Claude 4.5 Sonnet, GPT-5, Gemini 2.5 Pro
Conversational AI & Chatbots	Gemini 2.5 Flash, GPT-5
Legal & Compliance Analysis	Claude 4.1 Opus, Gemini 2.5 Pro
Coding & Development	Claude 4.5 Sonnet, GPT-5, Claude 4.1 Opus, Grok 4
Enterprise-Level Processing (Long Contexts)	Gemini 2.5 Pro, Claude 4.1 Opus
Fast, Low-Cost AI Tasks	GPT-5 Mini, Gemini 2.5 Flash

2. Hosting Preference

Choose where your data is processed based on your privacy needs and access priorities. We offer two hosting options, EU and US, each with different benefits around compliance, speed, and model access.

Factor	EU Hosting (Privacy First)	US Hosting (Feature First)
GDPR Compliance	Fully GDPR-compliant (by default)	Not GDPR-compliant by default
Data Residency	Data stays in the EU	Data stored in the US
Latency (for EU users)	Lower latency (servers in-region)	Higher latency due to transatlantic transfer
Model Availability	Some models/features released later	Full access to the latest models and features first
Legal & Regulatory Risks	Meets stricter EU privacy laws	Subject to US law and transfer safeguards

Summary:

• Choose EU hosting if you prioritize GDPR compliance, strict data privacy, and low latency within Europe.

• Choose US hosting if you want the latest model features and broader model access, and are okay with US-based data transfer protections.

3. Speed vs. Depth: What Matters More to You?

Some models are designed for quick, lightweight tasks. Others are built to dive deeper, think harder, and handle more complexity. Choose based on the kind of experience you need.

Preference	When to Choose	Models
High Speed (Fast, Responsive)	For fast answers, live chat, or simple tasks where low latency matters most.	Gemini 2.5 Flash, GPT-5 Mini, Grok 4 (Fast)
High Depth (Detailed, Structured)	For complex prompts, multi-step logic, or detailed analysis that needs reflection.	Claude 4.1 Opus, Claude 4.5 Sonnet, GPT-5

4. Detailed Descriptions

Claude 4.5 Sonnet

• Highlights

  • Best-in-class for coding and complex agents. Anthropic describes Sonnet 4.5 as “the best coding model in the world,” and “the strongest model for building complex agents,” with substantial gains in reasoning and math.

  • Long-running, controllable thinking. Can give near-instant answers or show extended step-by-step reasoning

  • Enhanced knowledge for coding, finance, and cybersecurity; built to power research and analytical agents.

• Limitations

  • Latency/cost trade-offs for deep runs

  • Overkill for lightweight chat. Sonnet 4.5 is optimized for complex agents, coding, and computer use

• Best For

  • Software engineering & code assistance at production depth

  • Agentic workflows that operate tools/computers over many steps

  • Analytical domains like finance, cybersecurity, and technical research needing accurate, explainable steps and long-running tasks

• Host: EU, US

Claude 4.1 Opus

• Highlights

  • Flagship deliberate reasoning for Anthropic’s 4.x line; excels at decomposing complex prompts into clear, defensible steps.

  • Reliable structure for long documents (policies, contracts, research packs) with consistent sectioning, summaries, and point-by-point analysis

  • Strong at precise edits/refactors (code or prose) with minimal collateral change; good at “explain why” and “show what changed."

• Limitations

  • Latency/cost higher than mid-tier models; not ideal for lightweight chat

  • More cautious by default; may require tighter instructions/tooling to move quickly in agentic runs.

• Best For

  • High-stakes reasoning where traceability matters

  • Long-form research & technical drafting that needs consistent structure and careful justification

  • Code reviews and targeted refactors in production codebases when correctness > speed

• Host: EU, US

GPT-5

• Highlights

  • Best-in-class for coding & agentic tasks: SOTA on major coding benchmarks; strong at multi-file refactors, bug-fixes, and long chains of tool calls

  • Unified system with auto-routing between Chat and Thinking modes (GPT-5 Auto on Blockbrain)

• Limitations

  • Higher cost/latency than mini/flash-type models

  • “Minimal reasoning” is great for speed but not suited to deep multi-step planning

  • For highly specialized legal writing style or conservative tone, some teams still prefer Claude 4.x in review loops

• Best For

  • Production-depth software engineering

  • Analytical builds that mix reasoning with automation

  • Enterprise assistants needing long context, tool integration, and reliable mode switching between fast answers and deeper thinking

• Host: EU, US

Gemini 2.5 Pro

• Highlights

  • Long-context synthesis: excels at digesting very large briefs

  • Multimodal + tool use: handles text and vision inputs; solid function calling and retrieval grounding for doc-heavy workflows

  • Stable summarization & structuring: good at outlines, tables, and point-by-point comparisons across many sources

  • Balanced cost/latency vs top “deliberate” models

• Limitations

  • Not the top coder/agent versus GPT-5 or Claude 4.5 Sonnet on repo-wide refactors and autonomous long chains

  • Latency rises with very large contexts; Flash is faster but less deep

• Best For

  • Enterprise long-doc workloads

  • Data + narrative: summarize analytics into briefings, create comparison tables, generate exec summaries

• Host: EU

Gemini 2.5 Flash

• Highlights

  • Speed + cost leader in the Gemini family; excellent for high-throughput chat

  • Strong at summarization, extraction, and classification over short–medium inputs.

  • Plays well as a prep/post step with Pro (e.g., Flash → filter/route → Pro for depth)

  • Solid stability under load

• Limitations

  • Not designed for deep multi-step reasoning or long autonomous chains

  • Shorter practical context than Pro; quality drops on very large documents unless chunked/grounded.

  • Weaker on coding and complex refactors vs GPT-5 / Claude 4.5 Sonnet.

• Best For

  • Live chat, FAQs, routing, and intent detection at scale.

  • Lightweight RAG and meeting/email summaries where turnaround time and cost dominate.

  • Pre/post-processing around heavier models

• Host: EU

Grok 4

• Highlights

  • Excellent at competitive math and coding: xAI’s Grok 4 materials showcase results and demos across USAMO, HMMT, AIME (competition math)

  • Strong coding capability

  • Large context options: Grok 4 supports ~256k tokens

• Limitations

  • Leads to significantly slower response times compared to other leading models

  • May need more explicit prompting: Users note Grok 4 often performs best when instructions are highly detailed (“handholding”).

• Best For

  • Long-document/chat workflows that benefit from very large context

  • Engineering and coding assistants

• Host: US

Grok 4 (Fast)

• Highlights

  • Excellent at competitive math and coding (e.g., AIME/HMMT-style problems and competitive programming)

  • Huge context for long proofs, repos, or chats: up to 2M tokens on Grok 4 Fast.

  • Uses about 40% fewer “thinking” tokens on average than Grok 4

  • Low cost with optional cached-token pricing

• Limitations

  • Slower than average, but still comfortably fast.

• Best For

  • Contest math practice (step-by-step reasoning, proof drafts, solution checking) and competitive programming (fast iterations, tool use)

  • Agentic dev workflows and code copilots that need rapid loops

Claude 4 Opus

• Highlights

  • Exceptional at multi-step reasoning and logic-heavy workflows

  • Strong performance in legal, policy, and strategic planning use cases

  • Handles large documents and long prompts with clarity and consistency

  • Performs well in code generation, data interpretation, and tool use

• Limitations

  • Slower and more expensive than smaller models

  • Not ideal for casual conversations or lightweight interactions

  • May be overkill for simple tasks

• Best For

  • Analysts, researchers, consultants, and power users needing accurate, explainable outputs

  • Use cases requiring reliable autonomy and deep thought (e.g. research summaries, compliance, strategic docs)

• Host: EU, US

Claude 4 Sonnet

• Highlights

  • Refined reasoning and better adherence to complex instructions

  • More efficient than Claude 4 Opus, with faster responses at lower cost

    • Strong performance in coding, structured generation, and tool use

  • Great for medium-length tasks and daily business workflows

• Limitations

  • Less capable than Claude 4 Opus for large-scale, high-stakes reasoning tasks

  • Doesn’t support multimodal input (text-only)

  • May require extra guidance on highly ambiguous or technical prompts

• Best For

  • Teams building AI features into products (e.g. dashboards, assistants, workflows)

  • Users who want both speed and reasoning without the premium price tag

  • Prompt designers or analysts needing accuracy, not depth overload

• Host: EU, US

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

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

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

(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

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