AI Showdown 2025: Comparing the Best Models – DeepSeek, GPT-4o, Grok 3, and More!

Key Points

Diverse Lineup: The top models include GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro, DeepSeek-R1, Llama 3.1 405B, Grok 3, Mistral Large, Falcon-180B, Amazon Nova Pro, Phi-3.5-MoE, MM1-30B, and OpenELM-3B.
Unmatched Performance: DeepSeek-R1 leads with an impressive MMLU score of 90.8%, excelling in reasoning and knowledge tasks.
Surprising Underperformance: OpenELM-3B, Apple's model, scores only 24.8% on MMLU, indicating it may be better suited for niche on-device tasks rather than broad language understanding.

Model Overview

The AI landscape in 2025 is more competitive than ever, with models engineered by tech giants and innovative startups. Each model is designed to address different needs—from general language processing to specialized tasks like coding and multimodal analysis. This guide uses the MMLU benchmark, which evaluates performance across 57 subjects, to deliver a reliable comparison.

Performance Comparison

The MMLU benchmark provides an objective measure of each model’s capacity to tackle diverse tasks. Below is a summary of the key performance metrics:

GPT-4o (OpenAI): 88.7%
Claude 3.5 Sonnet (Anthropic): 88.3%
Gemini 1.5 Pro (Google): 85.9%
DeepSeek-R1 (DeepSeek): 90.8%
Llama 3.1 405B (Meta): 87.3%
Grok 3 (xAI): ~85%
Mistral Large (Mistral AI): 81.2%
Falcon-180B (TII): 61.2%
Amazon Nova Pro (Amazon): 69.1%
Phi-3.5-MoE (Microsoft): ~75%
MM1-30B (Apple): ~85%
OpenELM-3B (Apple): 24.8%

Analysis

Leaders: DeepSeek-R1’s exceptional score (90.8%) confirms its superior reasoning and knowledge, likely thanks to its innovative reinforcement learning techniques.
Strong Competitors: GPT-4o and Claude 3.5 Sonnet, both scoring around 88%, are solid choices for general-purpose applications.
Specialized Options: Llama 3.1 405B and Gemini 1.5 Pro offer robust performance for large-scale deployments.
Underperformers: Models like Falcon-180B and notably OpenELM-3B, with its low score, may cater to niche applications rather than broad language understanding.

Background and Methodology

Driven by breakthroughs in transformer architectures and large-scale training, today's AI models power everything from chatbots to advanced content generation. This guide evaluates each model using the MMLU benchmark—a standardized test covering 57 subjects—to ensure an accurate assessment of their reasoning and general knowledge capabilities. Data was compiled through comprehensive research, including technical reports, model cards, and benchmark leaderboards.

Model Details and Performance

1. GPT-4o (OpenAI)

Type: Likely multimodal LLM
Parameters: Rumored around 1.7 trillion
Training Data: Extensive web data
MMLU Score: 88.7% (5-shot)
Accessibility: API-based, closed-source
Key Feature: Exceptional reasoning and language understanding.
Citation: Azure OpenAI Service models

2. Claude 3.5 Sonnet (Anthropic)

Type: LLM
Parameters: Not disclosed
Training Data: Likely a mix of web and synthetic data
MMLU Score: 88.3% (5-shot)
Accessibility: API-based, closed-source
Key Feature: Strong reasoning, perfect for versatile applications.
Citation: Papers with Code MMLU Benchmark

3. Gemini 1.5 Pro (Google)

Type: Multimodal LLM
Parameters: Not specified (Gemini 1.0 is 1.8 trillion)
Training Data: Text, images, and videos
MMLU Score: 85.9% (5-shot)
Accessibility: API via Google Cloud, closed-source
Key Feature: Supports a 1 million token context window, excelling in multimodal tasks.
Citation: Gemini 1.5 Pro Model Card

4. DeepSeek-R1 (DeepSeek)

Type: Reasoning-focused LLM
Parameters: 671 billion total, 37 billion active per forward pass (MoE)
Training Data: Uses reinforcement learning techniques
MMLU Score: 90.8%
Accessibility: Open-source under MIT License
Key Feature: Outperforms competitors in math and coding while lowering training costs.
Citation: DeepSeek R1: All you need to know

5. Llama 3.1 405B (Meta)

Type: LLM
Parameters: 405 billion
Training Data: 15 trillion tokens from diverse sources
MMLU Score: 87.3% (5-shot)
Accessibility: Open-source with a custom license
Key Feature: Largest open-source model, excelling in coding and multilingual tasks.
Citation: Meta releases new Llama 3.1 models

6. Grok 3 (xAI)

Type: Reasoning-focused LLM
Parameters: Not disclosed
Training Data: Includes synthetic data and vast GPU resources
MMLU Score: ~85% (estimated)
Accessibility: API and app-based, closed-source with premium options
Key Feature: Designed to excel in math and coding, with ongoing improvements.
Citation: Elon Musk’s xAI releases its latest flagship model, Grok 3

7. Mistral Large (Mistral AI)

Type: LLM
Parameters: Likely around 70B
Training Data: Focus on code and multilingual data
MMLU Score: 81.2%
Accessibility: API via Mistral platform, open-weights for research
Key Feature: Excels in multilingual tasks and coding.
Citation: Mistral Large | Prompt Engineering Guide

8. Falcon-180B (TII)

Type: Open-source LLM
Parameters: 180 billion
Training Data: 3.5 trillion tokens from web data
MMLU Score: 61.2%
Accessibility: Open-source under Apache 2.0 license
Key Feature: Ideal for research and commercial use, outperforming earlier models.
Citation: Spread Your Wings: Falcon 180B is here

9. Amazon Nova Pro (Amazon)

Type: Multimodal LLM
Parameters: Not specified
Training Data: Text, images, and videos
MMLU Score: 69.1%
Accessibility: API via Amazon Bedrock, closed-source
Key Feature: Fast, cost-effective, and supports over 200 languages.
Citation: Nova Pro - Intelligence, Performance & Price Analysis

10. Phi-3.5-MoE (Microsoft)

Type: Mixture-of-Experts LLM
Parameters: 60.8B total, 6.6B active
Training Data: 4.9 trillion tokens from web and synthetic sources
MMLU Score: ~75% (estimated)
Accessibility: Open-source on Azure under MIT License
Key Feature: Lightweight with strong multilingual capabilities and a 128K context window.
Citation: microsoft/Phi-3.5-MoE-instruct

11. MM1-30B (Apple)

Type: Multimodal LLM
Parameters: 30 billion
Training Data: Combines image-caption pairs, interleaved image-text, and text-only data
MMLU Score: ~85% (estimated)
Accessibility: Research-only (not publicly available)
Key Feature: Exceptional few-shot learning and efficiency with both text and images.
Citation: MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training

12. OpenELM-3B (Apple)

Type: On-device optimized LLM
Parameters: 3 billion
Training Data: 1.8 trillion tokens from public datasets
MMLU Score: 24.8%
Accessibility: Open-source, available on Hugging Face
Key Feature: Highly efficient in parameter allocation, though its low score indicates a focus on niche, on-device tasks.
Citation: apple/OpenELM-3B-Instruct

Comparative Analysis

The significant spread in MMLU scores underscores the varied strengths of these models. DeepSeek-R1’s stellar 90.8% score sets the benchmark for advanced reasoning, while GPT-4o and Claude 3.5 Sonnet remain reliable all-rounders. Specialized models such as Llama 3.1 405B and Gemini 1.5 Pro are ideal for large-scale deployments, whereas models like Falcon-180B and OpenELM-3B target more specific applications.

Additional Considerations

Beyond raw performance:

Context Window: Gemini 1.5 Pro supports an impressive 1 million token context, perfect for lengthy documents.
Cost-Effectiveness: Phi-3.5-MoE offers a cost-efficient solution with its 128K context.
Accessibility: Open-source models provide flexibility for research and customization, while closed-source options like GPT-4o ensure robust performance through API access.

Conclusion

This guide lays out the competitive landscape of AI models in 2025. Whether you need a model for general-purpose tasks or specialized applications, the insights provided here will help you make an informed choice. DeepSeek-R1, GPT-4o, and Claude 3.5 Sonnet are top contenders for broad applications, while models like Grok 3 and MM1-30B excel in niche areas. Embrace the future of AI, and choose a model that propels your business ahead of the competition.