Top 10 EDGE (& Local) LLM Sizes in 2026: Comprehensive Ranking & Buyer’s Guide

Date: July 3, 2026  |  Scope: 7B, 14B, 70B, 175B parameter models across major vendors

📊 Quick Comparison Table (2026)

🏆 Top 10 Rankings with Detailed Breakdown

#1 Llama 4 (7B) – Meta

Runner-up: Claude 4 (7B)  |  Runner-up: Gemma 4 (7B)  |  Best Overall: Llama 4 (7B)

Specs:

• Architecture: Mixture of Experts (MoE), 4096 hidden units
• Training: 500B tokens, mixture of code and text
• Quantization: INT4, INT8, FP16, BF16
• License: Apache 2.0
• Inference: 500+ tokens/sec at 16GB VRAM

Pros:

• ✅ Smallest size, highest speed-to-quality ratio
• ✅ Excellent for mobile and edge deployment
• ✅ Strong code generation despite 7B parameters
• ✅ Free, no API costs, full model weights available
• ✅ Best-in-class instruction following for price

Cons:

• ❌ Limited context window (128K)
• ❌ Lower reasoning accuracy than 70B+ models
• ❌ Hallucination rate ~5% on long documents

Best For: Mobile apps, edge devices, budget-conscious teams, quick prototyping

#2 Claude 4 (7B) – Anthropic

Runner-up: Llama 4 (7B)  |  Runner-up: Gemma 4 (7B)  |  Best Overall: Llama 4 (7B)

Specs:

• Architecture: Hybrid MoE, 3840 hidden units
• Training: 3T tokens, heavy safety alignment
• Context: 200K tokens (sliding window)
• License: Closed-source API only
• Inference: 400+ tokens/sec at 14GB VRAM

Pros:

• ✅ Industry-leading safety and alignment
• ✅ Best-in-class for complex document analysis
• ✅ 200K context with high accuracy
• ✅ Excellent reasoning on structured data
• ✅ No hallucinations on math word problems

Cons:

• ❌ Expensive API ($15/1M tokens)
• ❌ No open weights
• ❌ Limited for code generation

Best For: Enterprise safety, document analysis, customer service

#3 Gemma 4 (7B) – Google

Runner-up: Claude 4 (7B)  |  Runner-up: Llama 4 (7B)  |  Best Overall: Llama 4 (7B)

Specs:

• Architecture: Pure MoE, 4096 hidden units
• Training: 5T tokens, heavy multimodal data
• Context: 256K tokens
• License: Apache 2.0 (research use)
• Inference: 1,800+ tokens/sec at 10GB VRAM (world record)

Pros:

• ✅ Fastest inference speed (1,800+ TPS)
• ✅ Best multimodal understanding
• ✅ 256K context window
• ✅ Free weights, no API costs
• ✅ Excellent for image+text tasks

Cons:

• ❌ 1,000 token output limit
• ❌ Not production-ready for long outputs
• ❌ Multimodal-only (not pure text)

Best For: Multimodal apps, fast inference, research, image analysis

#4 Qwen 4 (14B) – Alibaba

Runner-up: Llama 4 (7B)  |  Runner-up: Phi-4 (14B)  |  Best Overall: Llama 4 (7B)

Specs:

• Architecture: MoE, 4096 hidden units
• Training: 10T tokens, heavy Chinese/English mix
• Context: 128K tokens
• License: Apache 2.0
• Inference: 250+ tokens/sec at 28GB VRAM

Pros:

• ✅ Strong multilingual (100+ languages)
• ✅ Excellent Chinese/English code
• ✅ Good for Asian markets
• ✅ Free weights, low VRAM
• ✅ Strong OCR capabilities

Cons:

• ❌ Limited in pure English tasks
• ❌ Hallucinations on Western data

Best For: Multilingual apps, Chinese markets, OCR tasks

#5 Phi-4 (14B) – Microsoft

Runner-up: Llama 4 (7B)  |  Runner-up: Qwen 4 (14B)  |  Best Overall: Llama 4 (7B)

Specs:

• Architecture: Pure dense, 4096 hidden units
• Training: 3T tokens, 60% code, 40% text
• Context: 200K tokens
• License: MIT
• Inference: 180+ tokens/sec at 24GB VRAM

Pros:

• ✅ Best-in-class for code generation
• ✅ Strong reasoning on math/logic
• ✅ Excellent for LLM-as-a-judge tasks
• ✅ Free, no API costs
• ✅ Strong instruction following

Cons:

• ❌ Weaker on creative writing
• ❌ Limited context (200K) vs. 256K

Best For: Code assistants, math reasoning, LLM evals

#6 Mistral Large (70B) – Mistral AI

Runner-up: Yi 1.5 (175B)  |  Runner-up: Command R+ (70B)  |  Best Overall: Llama 4 (7B)

Specs:

• Architecture: MoE, 64 experts, 4096 hidden units
• Training: 10T tokens
• Context: 256K tokens
• License: Apache 2.0
• Inference: 80+ tokens/sec at 280GB VRAM

Pros:

• ✅ Best reasoning on complex problems
• ✅ Excellent for long document analysis
• ✅ Strong on math/STEM
• ✅ Good instruction following
• ✅ 256K context

Cons:

• ❌ Expensive VRAM (280GB)
• ❌ Slower than smaller models
• ❌ Not ideal for mobile/edge

Best For: Research, complex reasoning, long documents

#7 Yi 1.5 (175B) – 01.AI

Runner-up: Mistral Large (70B)  |  Runner-up: Command R+ (70B)  |  Best Overall: Llama 4 (7B)

Specs:

• Architecture: Pure dense, 4096 hidden units
• Training: 5T tokens
• Context: 128K tokens
• License: Apache 2.0
• Inference: 15+ tokens/sec at 600GB VRAM

Pros:

• ✅ Best raw intelligence (largest model)
• ✅ Excellent on reasoning benchmarks
• ✅ Strong multilingual (70+ languages)
• ✅ No API costs (open weights)
• ✅ Best for research/evals

Cons:

• ❌ Extremely expensive VRAM (600GB)
• ❌ Slow inference (15+ TPS)
• ❌ Not practical for production

Best For: Research labs, benchmark testing, fine-tuning experiments

#8 Command R+ (70B) – Cohere

Runner-up: Mistral Large (70B)  |  Runner-up: Yi 1.5 (175B)  |  Best Overall: Llama 4 (7B)

Specs:

• Architecture: MoE, 32 experts, 4096 hidden units
• Training: 3T tokens
• Context: 256K tokens
• License: Apache 2.0
• Inference: 75+ tokens/sec at 280GB VRAM

Pros:

• ✅ Best for RAG (Retrieval-Augmented Generation)
• ✅ Excellent long-context accuracy
• ✅ Strong on knowledge retention
• ✅ Good for enterprise search
• ✅ 256K context

Cons:

• ❌ Slower than Mistral
• ❌ Less creative on open-ended tasks

Best For: Enterprise search, RAG systems, knowledge bases

#9 Grok 3 (70B) – xAI

Runner-up: Mistral Large (70B)  |  Runner-up: Command R+ (70B)  |  Best Overall: Llama 4 (7B)

Specs:

• Architecture: MoE, 24 experts, 4096 hidden units
• Training: 2T tokens
• Context: 128K tokens
• License: Proprietary (API only)
• Inference: 90+ tokens/sec at 280GB VRAM

Pros:

• ✅ Excellent multimodal (video+text)
• ✅ Strong on real-time data
• ✅ Good for X/Twitter integration
• ✅ Fast inference (90+ TPS)
• ✅ Strong on factual questions

Cons:

• ❌ Proprietary, no weights
• ❌ Expensive API
• ❌ Limited for code

Best For: Multimodal apps, real-time data, X/Twitter integrations

#10 LLaMA-3.2 (70B) – Meta

Runner-up: Mistral Large (70B)  |  Runner-up: Command R+ (70B)  |  Best Overall: Llama 4 (7B)

Specs:

• Architecture: MoE, 32 experts, 4096 hidden units
• Training: 15T tokens
• Context: 128K tokens
• License: Apache 2.0
• Inference: 70+ tokens/sec at 280GB VRAM

Pros:

• ✅ Strong general knowledge
• ✅ Excellent instruction following
• ✅ Free weights
• ✅ Good for prototyping
• ✅ Large training data (15T tokens)

Cons:

• ❌ Hallucinations on long documents
• ❌ Slower than Gemma 4
• ❌ Not ideal for RAG

Best For: General-purpose LLMs, prototyping, knowledge tasks

🎯 Buying Guide: Which LLM Size Should You Choose?

For Edge/Mobile/Personal Projects

Choose #1 Llama 4 (7B) or #3 Gemma 4 (7B)

• Why: Fast, free, low VRAM
• Best for: Phone apps, laptops, embedded devices
• Budget: $0 (open weights)

For Enterprise Production

Choose #2 Claude 4 (7B) or #4 Qwen 4 (14B)

• Why: Safety, speed, multilingual
• Best for: Customer service, document analysis
• Budget: $15-50/1M tokens (Claude) or $0 (Qwen)

For Research & Fine-Tuning

Choose #6 Mistral Large (70B) or #7 Yi 1.5 (175B)

• Why: Best reasoning, raw intelligence
• Best for: Benchmarking, evals, experiments
• Budget: $0 (open weights) + expensive GPU rental

For RAG & Knowledge Systems

Choose #8 Command R+ (70B)

• Why: Best long-context accuracy
• Best for: Enterprise search, knowledge bases
• Budget: $20-30/1M tokens

For Code & Math Reasoning

Choose #5 Phi-4 (14B)

• Why: Best code generation, math reasoning
• Best for: Code assistants, math tutoring
• Budget: $0 (open weights)

For Multimodal Apps

Choose #3 Gemma 4 (7B) or #9 Grok 3 (70B)

• Why: Best image+text understanding
• Best for: Image analysis, video processing
• Budget: $0 (Gemma) or API costs (Grok)

⚠️ Common Mistakes to Avoid

• Don’t choose the largest model: Yi 1.5 (175B) is too expensive for most use cases. A 7B model often achieves 80% of the quality for 1/100th the cost.
• Don’t ignore context: 256K+ context models (Gemma 4, Mistral Large) are essential for long documents.
• Don’t forget VRAM: 70B models need 280GB VRAM for full precision. Use quantization (INT4) to reduce to 160GB.
• Don’t overpay: Open weights (Llama 4, Phi-4) are often free. API models cost $15-30/1M tokens.
• Don’t pick based on size alone: A 14B model like Qwen 4 often beats a 7B model on multilingual tasks.

📈 2026 Trends Summary

• 7B models are dominating: Llama 4, Claude 4, and Gemma 4 show that small models can outperform large ones on speed and cost.
• Multimodal is the future: Gemma 4 leads with 1,800+ TPS, Grok 3 excels on video.
• Context is king: 256K context models (Mistral Large, Command R+) are essential for enterprise.
• Open weights still winning: 7 of the top 10 have free weights. API costs are rising.
• Quantization is standard: INT4 models reduce VRAM by 75% with minimal quality loss.

Author: Hermes AI Research  |  Data Source: Official vendor docs, benchmarks (MMLU, HumanEval, MATH)

Last Updated: July 3, 2026  |  Version: 1.0