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. Find Primary Use Cases LLMs & R-LLMs
General Productivity
GPT 4.1, GPT 5.2, Gemini 2.5 Pro, Claude Sonnet 4.6
Complex Reasoning
GPT 5.2 (Thinking), Gemini 3 Pro, Claude Opus 4.6
Structured Writing & Synthesis
GPT 5.2, Gemini 3 Pro, Claude Sonnet 4.6
Coding & Technical Workflows
GPT 5.2 Codex, Gemini 3.1 Pro, Claude Sonnet 4.6
Fast & Scalable Processing
GPT 5.1 (Non-Thinking), Gemini 2.5 Flash (Lite), Claude Sonnet 4.6 (Fast), Claude Haiku 4.5
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
Not GDPR-compliant by default
Data Residency
Data stays in the EU
Data stored globally
Model Availability
Late model release depending on EU Data Center availability
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 and strict data privacy within Europe.
Choose US hosting if you want the latest models and global data centers.
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 (Lite), Claude Sonnet 4.6 (Fast), GPT 4.1 Mini
High Depth (Detailed, Structured)
For complex prompts, multi-step logic, or detailed analysis that needs reflection.
Gemini 3 Pro, Claude 4.6 Sonnet, GPT 5.2 (Thinking)
4. Detailed Descriptions
Gemini 3.1 Pro (Preview)
Highlights
Great for complex reasoning, coding, and long-context work across text, images, audio, video, PDFs, and large codebases.
Best used for deep research, multi-step agent workflows, technical planning, and document-heavy analysis where strong reasoning and broad multimodal understanding matter most.
Host: US
Gemini 3 Pro
Highlights
High-performance multimodal model designed for teams that work with text, images, documents, videos and code.
It offers strong long-context reasoning, reliable analysis across large files and advanced tool use for more automated workflows.
For businesses that rely on visual data, technical documentation or development tasks, this provides broader multimodal coverage and deeper file understanding than text-focused models
Host: US
Claude Sonnet 4.6 (Fast)
Highlights
A configuration of Claude Sonnet 4.6. It operates strictly in "Non-Reasoning" mode, bypassing long thinking steps in order to have low-latency quality outputs.
Host: EU, US
Claude Sonnet 4.6
Highlights
Improved Sonnet model with stronger coding, computer use, and long-context reasoning.
Better suited for agent workflows, large-codebase tasks, document analysis, and other multi-step work.
Host: EU, US
Claude Opus 4.6
Highlights
Improves on the coding strengths of prior models with more reliable performance for agentic tasks, codebase management, and structured code review and debugging.
It’s also stronger in everyday work. From handling analysis, document review, and multitasking more consistently and efficiently.
Host: EU, US
Claude Haiku 4.5
Highlights:
Fast, efficient, and built for scale. Delivers near-Sonnet-level coding and reasoning at about 3× cheaper and 2× faster performance.
Excels in tool use, UI interaction, and parallel task execution, ideal as a worker model in multi-agent or production setups.
Strong on coding reliability. Best for backend automations, chat workloads, and agent systems needing speed and low cost.
Host: EU, US
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
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 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
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