LLMs Tools
Large Language Models for your apps.
Recently Launched
2026OpenAI API
Build next-gen apps with OpenAI's powerful models.
AI IntelligenceClaude Opus 4.7
LatestAnthropic
Anthropic's April 2026 flagship model featuring unprecedented agentic capabilities.
Advanced Reasoning & AgentsMistral AI
Open-weight models.
Open ModelsStarCoder
Open-source code generation model.
Code GenerationLM Studio
Run local LLMs on your desktop.
Local InferenceLlama 2
Open-weight large language model from Meta.
Open ModelsTogether AI
The fastest cloud for open-source AI.
Inference / TrainingHugging Face
The AI community building the future.
Model HubOllama
Get up and running with Llama 2 locally.
Local InferenceLangChain
Building applications with LLMs.
LLM FrameworkLlamaIndex
Data framework for LLM applications.
RAG FrameworkCohere
Enterprise AI platform.
Enterprise NLPDeepSeek V4
LatestDeepSeek
DeepSeek's 2026 flagship model targeting advanced coding and long-context comprehension.
Advanced Coding & Long-Context AnalysisGrok 4.3 Beta
LatestxAI
xAI's newest iterative update featuring enhanced real-time data integration.
Real-time Information & CodingGPT-5.3 Codex
LatestOpenAI
OpenAI's specialized coding model combining GPT-5 training with best-in-class code generation.
Specialized Code GenerationGemini 3.1 Pro
LatestGoogle's most capable 2026 model with 77.1% ARC-AGI-2 score and breakthrough complex reasoning.
Complex Reasoning & Problem SolvingMistral Large 3
LatestMistral AI
Mistral's frontier model with 128K context, strong multilingual support, and competitive coding.
Multilingual Tasks & General ReasoningGemma 4
LatestGoogle's powerful open-weights model family (Apache 2.0 license).
Local AI & Fine-tuningMeta Muse Spark
LatestMeta
Meta's first proprietary closed-weight frontier model, launched April 2026.
General AI AssistantRelated Categories
Choosing the Right Tool
- 1Chatbots and conversational AI applications
- 2Content generation and copywriting tools
- 3Code generation and programming assistants
- 4Data analysis and document understanding
- 5Customer support automation and FAQ systems
- ✓Model capabilities (intelligence, reasoning quality)
- ✓Context window size (tokens supported)
- ✓Pricing per token and rate limits
- ✓API features (streaming, function calling, vision)
- ✓Privacy and data retention policies
- ✓Latency and response speed
About LLMs Tools
Large Language Models (LLMs) are transforming how we build intelligent applications. From OpenAI's GPT-4 to Anthropic's Claude and open-source models like Llama, these powerful AI models enable natural language understanding, generation, and reasoning capabilities in your applications.
Whether you're building chatbots, content generation tools, code assistants, or data analysis applications, modern LLM APIs and platforms provide easy integration with features like streaming responses, function calling, vision capabilities, and fine-tuning. The ecosystem includes cloud-hosted APIs (OpenAI, Anthropic, Google), open-source models you can run locally (Ollama, LM Studio), and platforms for orchestration and prompt management.
Consider factors like pricing (per token), context window size, response quality, latency, privacy requirements, and whether you need open-source control or prefer managed solutions. Many applications use multiple models for different tasks to optimize cost and performance.
Frequently Asked Questions
ChatGPT vs Claude: which LLM should I use?
GPT-4 (via OpenAI API) is generally more capable and has broader knowledge. Claude (Anthropic) excels at longer contexts, complex reasoning, and often gives more nuanced, careful responses. Many developers use both: GPT-4 for breadth, Claude for depth and safety.
Should I use hosted APIs or run models locally?
Hosted APIs (OpenAI, Anthropic) are easier, faster, and more capable but cost per request and send data externally. Local models (Ollama, LM Studio) offer privacy and zero ongoing costs but require powerful hardware and are less capable. Choose based on privacy needs, budget, and quality requirements.
How much do LLM APIs cost?
Varies widely. GPT-4: ~$0.03/1K tokens. GPT-3.5: ~$0.002/1K tokens. Claude: similar to GPT-4. For a typical chatbot, expect $0.01-0.10 per conversation. High-volume applications can cost hundreds to thousands monthly. Budget based on expected token usage.
What is a context window and why does it matter?
The context window is how much text the model can 'remember' at once. GPT-4 Turbo: 128K tokens (~300 pages). More context means the model can reference longer documents, entire codebases, or extended conversations. Critical for document analysis and long-form content.
Can I fine-tune LLMs for my specific use case?
Yes! OpenAI, Anthropic, and open-source models support fine-tuning. Use it to teach models domain-specific knowledge, brand voice, or specific behaviors. However, prompt engineering and RAG (retrieval augmented generation) often work better and are much cheaper.