Best LLM Models
Compare the top llm models tools. Find the right one for your project.
Notable Features
Strengths
- + Multi-modal (text/audio/image/video)
- + Strong reasoning & coding
- + Fast responses
- + Improved multilingual performance
- + Cheaper than GPT-4 Turbo
Considerations
- - Closed-source weights
- - Usage-based costs
- - Rate limits & policies
- - Requires internet connectivity
Notable Features
Strengths
- + 200k-token context
- + Fast & articulate responses
- + Strong vision & reasoning
- + Interactive Artifacts panel
- + Safety-forward design
Considerations
- - Closed weights
- - Regional availability
- - Higher output cost
- - Limited free usage
Notable Features
Strengths
- + 1M-token context window
- + Native multimodal (text/image/audio/video/PDF)
- + Function calling & code execution
- + Caching to reduce cost
- + Google Workspace integration
Considerations
- - Closed source
- - Tiered pricing complexity
- - Features tied to Google Cloud
- - Limited to supported regions
Notable Features
Strengths
- + Open weights & permissive license
- + Strong reasoning & coding
- + Fine-tuning & quantization support
- + Active open-source community
- + Good English performance
Considerations
- - Shorter context (~8k)
- - No multimodal capability
- - Requires significant compute to host
- - Limited non-English performance
Notable Features
Strengths
- + Strong reasoning & coding
- + Multilingual (EN/FR/ES/DE/IT)
- + Competitive pricing ($2/M input, $6/M output)
- + Available via API & Azure
- + Function calling support
Considerations
- - Closed weights
- - Shorter context than some models
- - Not multimodal
- - Subscription only
Notable Features
Strengths
- + Low cost per token
- + Open-source Mixture‑of‑Experts
- + 128k context
- + Strong math & coding abilities
- + JSON & function calling support
Considerations
- - Experimental stability
- - Limited language support (EN/ZH)
- - Requires self-hosting for OSS
- - Less mature ecosystem
Notable Features
Strengths
- + Open-source model weights
- + Function calling & structured output
- + Multilingual (29+ languages)
- + Low cost ($0.07/M input, $0.26/M output)
- + Reasoning & content moderation modes
Considerations
- - Complex to fine-tune
- - Smaller community & docs
- - Less capable than top closed models
- - Compute heavy for local deployment
Notable Features
Strengths
- + Open weights with commercial license
- + 128k context window
- + Optimized for edge GPUs and ONNX
- + Strong code and reasoning for a 14B model
- + Low per-token cost on Azure AI
Considerations
- - No multimodal support
- - Smaller community than Llama
- - Requires quantization for consumer GPUs
- - Lower peak reasoning vs GPT-4 class
Notable Features
Strengths
- + Near real-time X data access
- + 128k context with tool use
- + Strong coding and math
- + Playful direct responses
- + Fast latency via xAI API
Considerations
- - Access gated to X Premium Plus
- - Closed-source weights
- - Limited enterprise controls today
- - Smaller ecosystem and docs
Popular Comparisons
gpt 4o vs claude 3-5-sonnet
GPT‑4o is the most versatile multi‑modal model, while Claude 3.5 Sonnet offers long‑context performance and strong safety.
gemini 1-5-pro vs gpt 4o
Gemini 2.5 Pro excels in ultra‑long contexts and multimodal analytics, while GPT‑4o offers the fastest, most polished general agent.
llama 3-1-70b vs mistral large
Llama 3 (70B) provides an open-source flagship with strong community support; Mistral Large offers a leaner, multilingual API with competitive pricing.
deepseek v3 vs qwen 2-5-72b
DeepSeek V3.2‑Exp delivers ultra‑long context and low costs for English/Chinese tasks, while Qwen 2.5 72B brings multilingual open weights at a slightly higher price.
phi 3-medium vs llama 3-1-70b
Phi-3 Medium excels for lightweight, cost-efficient deployments, while Llama 3 70B delivers maximum quality if you can host a large model.
grok 2 vs gpt 4o
Grok-2 taps live X data for up-to-the-minute answers, while GPT-4o delivers a polished multimodal assistant with the broadest ecosystem support.
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