Best 15 LLMs in February 2025: A Benchmark Comparison

The landscape of top-tier large language models (LLMs) is rapidly evolving, with several key players pushing the boundaries of AI capabilities.

These advanced models demonstrate exceptional performance across a wide range of tasks, including natural language understanding, code generation, factual accuracy, and complex reasoning.

To help guide your decision-making process, we’ve put together a comprehensive list of the top large language models (LLMs) currently available. Each of these models is specifically designed to tackle a wide range of challenges in the AI space, and they each excel in distinct areas.

Understanding their strengths and specializations will ensure you choose the model that aligns with your specific needs and goals.

To objectively evaluate these models, various benchmarks assess their performance across different domains, such as reasoning, general knowledge, and coding. This article compares and ranks the best LLMs based on three key benchmarks:

1. MMLU Pro (Massive Multitask Language Understanding Pro) – Measures complex reasoning and problem-solving abilities.
2. GPQA (General-Purpose Question Answering) – Evaluates general knowledge and factual accuracy.
3. HumanEval – Assesses performance in code generation and programming tasks.

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What Are LLMs?

LLMs (Large Language Models) are artificial intelligence systems trained on vast amounts of text data. They generate human-like text, answer questions, write code, and perform reasoning tasks. These models rely on deep learning architectures, typically transformer-based, to process and generate text at unprecedented scales.

The latest models push boundaries in context length (handling millions of tokens), multimodality (processing images, audio, and text together), and cost-efficiency (optimizing quality at a lower inference price).

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Why Benchmark LLMs?

Benchmarking LLMs ensures an objective comparison of their capabilities. Organizations, researchers, and businesses use these evaluations to choose the right model for their needs. Each benchmark highlights different strengths, whether it’s logical reasoning, factual correctness, or coding proficiency.

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Best LLMs in February 2025 (Benchmark Comparisons)

Below is a carefully curated compilation that brings together a selection of some of the most advanced large language models, presenting their performance across the three key benchmarks; MMLU, GPQA, and HumanEval.

By consolidating these diverse evaluation metrics, this list provides a comprehensive overview of the current landscape of LLM capabilities, highlighting how different models perform across a range of critical language understanding and problem-solving tasks:

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1. Claude 3.5 Sonnet – 90.4% MMLU, 67.2% GPQA, 93.7% HumanEval
2. o1 – 91.8% MMLU, 75.7% GPQA, 88.1% HumanEval
3. o1-mini – 85.2% MMLU, 60.0% GPQA, 92.4% HumanEval
4. DeepSeek-R1 – 90.8% MMLU, 71.5% GPQA, (No HumanEval Score)
5. o1-preview – 90.8% MMLU, 73.3% GPQA, (No HumanEval Score)
6. DeepSeek-V3 – 88.5% MMLU, 59.1% GPQA, (No HumanEval Score)
7. GPT-4o – 88.0% MMLU, 53.6% GPQA, (No HumanEval Score)
8. Grok-2 – 87.5% MMLU, 56.0% GPQA, 88.4% HumanEval
9. Kimi-k1.5 – 87.4% MMLU, (No GPQA Score), (No HumanEval Score)
10. Llama 3 1 405B Instruct – 87.3% MMLU, 50.7% GPQA, 89.0% HumanEval
11. Claude 3 Opus – 86.8% MMLU, 50.4% GPQA, 84.9% HumanEval
12. GPT-4 Turbo – 86.5% MMLU, 48.0% GPQA, 87.1% HumanEval
13. GPT-4 – 86.4% MMLU, 35.7% GPQA, 67.0% HumanEval
14. Mistral Large 2 – 84.0% MMLU, (No GPQA Score), 92.0% HumanEval
15. DeepSeek-V2.5 – 80.4% MMLU, (No GPQA Score), 89.0% HumanEval

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Top 6 LLMs for Reasoning (MMLU Pro)

The MMLU Pro benchmark measures models' ability to handle complex reasoning tasks across multiple domains, such as mathematics, logic, and knowledge-based problem-solving. Here are the top performers:

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1. DeepSeek-R1

Open-Source – Available on Eden AI - MMLU Pro: 84%

DeepSeek has emerged as a premier open-source AI provider, making cutting-edge models freely available to the developer community.

DeepSeek-R1 is highly regarded for its logical reasoning and problem-solving abilities, often matching or surpassing proprietary alternatives.

It is an attractive choice for AI engineers who prefer open models for custom fine-tuning and deployment in diverse applications.

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2. Claude 3.5 Sonnet

Multimodal – Available on Eden AI - MMLU Pro: 77.6%

Anthropic’s Claude 3.5 builds upon the previous versions with improved safety measures and context understanding.

It is particularly favored by developers working on applications requiring a blend of reasoning and multimodal capabilities.

While it doesn't have the longest context, it provides highly coherent and human-like responses across various tasks.

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3. Gemini 2.0 Flash ‍

Multimodal - Available on Eden AI – MMLU Pro: 76.4%

Google’s Gemini 2.0 Flash is built for speed and efficiency, making it an excellent choice for real-time applications.

Its strong reasoning ability is coupled with a focus on optimizing latency, allowing it to handle live AI interactions seamlessly.

Developers looking for a blend of performance and cost-effectiveness often prefer this model for scalable AI solutions.

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4. DeepSeek-V3 ‍

Open-Source – MMLU Pro: 75.9%

An evolution of DeepSeek’s earlier models, DeepSeek-V3 refines problem-solving and computational reasoning further.

It maintains its reputation as a top-tier open-source model with robust performance, making it a preferred choice for enterprises needing transparency and adaptability in AI deployments.

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5. Gemini 1.5 Pro

Multimodal - Available on Eden AI - MMLU Pro: 75.8%

Google’s Gemini 1.5 Pro shines in handling long-form content, boasting one of the longest context windows available.

Developers leveraging AI for document analysis, extensive research, and complex interactions find this model indispensable.

Though it lags slightly in reasoning, its overall versatility makes it a compelling option.

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6. Grok-2

‍MMLU Pro: 75.5%

X’s (formerly Twitter) AI team developed Grok-2 with a focus on social and conversational AI.

While it may not be the best for pure problem-solving, it excels in real-world reasoning and discussions, making it ideal for chatbots and dialogue-heavy applications.

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Top 5 LLMs for General Knowledge (GPQA)

‍The GPQA benchmark evaluates models' ability to answer general knowledge questions accurately. Here are the best-performing models:

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1. Open AI o3

‍GPQA Score: 87.7%

OpenAI’s o3 model is designed for factual precision, making it one of the most accurate LLMs for retrieving and verifying general knowledge.

It has been fine-tuned to minimize hallucinations, making it ideal for enterprise applications requiring high factual accuracy.

This model is widely adopted in legal, financial, and medical research industries where credibility and precision are critical.

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2. Open AI o3-mini

Available at Eden AI - GPQA Score: 79.7%

OpenAI's o3-mini model excels on the GPQA Diamond benchmark, showcasing strong capabilities in complex scientific reasoning and knowledge application.

With adjustable reasoning effort levels, o3-mini offers a balance of performance, speed, and cost-efficiency.

It stands out as a valuable tool for scientific research, education, and applications requiring deep scientific knowledge, particularly in areas involving expert-level science questions not readily available in public databases.

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3. Open AI o1-pro

‍GPQA Score: 79%

OpenAI's premium "o1" series model, launched in December 2024, is optimized for both high accuracy and nuanced knowledge recall, making it well-suited for academic and enterprise knowledge applications.

The o1 model excels in complex reasoning tasks, utilizing chain-of-thought prompting to process information iteratively before responding.

This approach enables the model to tackle hard problems requiring multistep reasoning and complex problem-solving strategies.

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4. Open AI o1

Available on Eden AI - GPQA Score: 75.7%

OpenAI's o1 model is a robust generalist AI system with solid GPQA performance excelling in advanced reasoning and complex problem-solving, particularly in STEM fields. It demonstrates exceptional performance on difficult benchmarks and offers adjustable reasoning levels.

The o1 model is well-suited for applications requiring broad general knowledge and nuanced problem-solving capabilities, making it valuable for academic research and enterprise use while balancing powerful capabilities with cost efficiency.

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5. Gemini 2.0 Flash Thinking

‍GPQA Score: 74.2%

Google’s Gemini 2.0 Flash Thinking is a specialized model designed for fast responses and structured factual output.

It combines the speed of Gemini 2.0 Flash with enhanced capabilities for complex tasks, such as a long context window, multimodal input support, and real-time thought process display.

Excelling in math and science benchmarks, it offers quick responses and integrates with Google apps, making it ideal for real-time AI deployments requiring factual accuracy and complex problem-solving.

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Top 5 LLMs for Code Generation and Programming (HumanEval)

‍The HumanEval benchmark assesses how well models generate code solutions for programming problems. Here are the leading models:

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1. Claude 3.5 Sonnet ‍

Available on Eden AI - HumanEval: 93.7%

Claude 3.5 Sonnet demonstrates impressive performance on the HumanEval benchmark, indicating strong capabilities in code generation, syntax correctness, and logical problem-solving.

The model excels in understanding complex coding requirements, generating functional code with improved error handling, and breaking down complex challenges into manageable steps.

These features make Claude 3.5 Sonnet well-suited for software development and automation tasks. However, it's important to note that other models also perform well in this area, and the choice of model should be based on specific project requirements and use cases.

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2. Qwen2.5-Coder 32B Instruct

Open-Source – HumanEval: 92.7%

Qwen2.5-Coder is a specialized language model series that excels in programming tasks. It supports over 40 programming languages, including niche ones, and is strong in code generation, completion, review, debugging, and code repair.

With advanced mathematical reasoning and support for long contexts up to 128K tokens, Qwen2.5-Coder offers flexibility for different computational needs.

Its ability to generate structured outputs like JSON enhances its real-world application, while its state-of-the-art coding benchmark performance makes it a major advancement in AI-assisted programming.

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3. o1-mini

Available on Eden AI - HumanEval: 92.4%

OpenAI's o1-mini is a compact, cost-effective yet powerful model designed for efficient coding applications. It achieves an impressive 92.4% score on the HumanEval benchmark, demonstrating strong code generation and problem-solving capabilities.

This model offers a balance of performance and computational efficiency, making it well-suited for developers and small teams who need AI assistance for programming tasks without the full resource requirements of larger models.

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4. Mistral Large 2

Open-Source – HumanEval: 92.0%‍

Mistral Large 2 is an advanced open-source language model that demonstrates exceptional performance in code generation and problem-solving tasks. It has strong capabilities in algorithmic reasoning and code synthesis.

The model excels in multiple programming languages, including Python, C++, Java, and others. Mistral Large 2 offers a balance of high performance and open accessibility, making it a popular choice among developers for various coding applications.

Its strong multilingual support and advanced reasoning capabilities in mathematics and scientific domains further enhance its versatility for complex problem-solving tasks.

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5. DeepSeek-V2.5

Open-Source –  Available on Eden AI - HumanEval: 89.0%

DeepSeek-V2.5 is a powerful open-source language model that excels in coding tasks. It combines the strengths of DeepSeek-V2-Chat and DeepSeek-Coder-V2-Instruct, offering enhanced capabilities for both general and coding-specific applications.

With a 128K token context length, it efficiently handles complex coding tasks and excels in various programming languages.

The model’s improved alignment with human preferences makes it ideal for software development, code generation, and problem-solving. Its robust performance and open-source nature make DeepSeek-V2.5 a valuable tool for developers seeking reliable AI assistance in coding.

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Best LLMs for Cost and Quality

Cost is a key factor when choosing an LLM, particularly for large-scale applications. Here’s how the top models perform in the GPQA benchmark while considering cost per million input tokens:

1. o3-mini – GPQA Score: 80%, Cost: $1.10 per million input tokens
   • OpenAI’s compact model provides excellent factual accuracy at a reasonable price, making it a great choice for applications that require extensive knowledge retrieval with controlled expenses.
2. DeepSeek-R1 – GPQA Score: 72%, Cost: $0.55 per million input tokens
   • This open-source model balances affordability with strong performance, making it the ideal option for developers seeking a cost-effective yet high-quality LLM.
3. Claude 3.5 Sonnet – GPQA Score: 67%, Cost: $3.00 per million input tokens
   • While more expensive, Claude 3.5 Sonnet provides superior reasoning and safety features, making it a preferred choice for applications where quality and precision outweigh cost concerns.

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Best LLMs for Quality and Context Length

‍Context length plays a crucial role in how effectively an LLM processes and retains information. Here are the leading models balancing high-quality performance with extensive context handling:

1. Gemini 1.5 Pro – Max Context: 2,097,152 tokens, MMLU: 86%
   • This model offers the longest available context window, making it the ideal choice for document-heavy tasks, in-depth research, and extended conversational interactions.
2. Gemini 1.5 Flash – Max Context: 1,048,576 tokens, MMLU: 79%
   • A slightly more streamlined variant of Gemini 1.5 Pro, optimized for speed while still supporting large-scale input sizes.
3. Claude 3.5 Sonnet – Max Context: 200,000 tokens, MMLU: 90%
   • While it has a shorter context length than the Gemini models, Claude 3.5 Sonnet excels in reasoning and comprehension, making it the best choice for applications requiring high-quality responses with moderate context processing.

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Why choose Eden AI to manage your LLMs ? 

Eden AI simplifies LLM integration for industries like Social Media, Retail, Health, Finance, and Law, offering access to multiple providers in one platform to optimize cost, performance, and reliability.

Key Benefits:

• Multi-Provider Access: Easily switch between LLMs for flexibility and optimization.
• Fallback & Performance Routing: Set up backup providers and route requests to the best-performing LLM.
• Cost-Effective AI: Balance cost and accuracy by selecting the most efficient providers.
• Enhanced Accuracy: Combine multiple LLMs to improve output quality and reliability.

Why Eden AI?

• Unified API & Billing: Manage multiple AI providers in one place.
• Standardized Responses: Consistent JSON format across all LLMs.
• Top AI Engines: Access Google, AWS, Microsoft, and specialized providers.
• Data Security: No data storage; GDPR-compliant options available.

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Sources

LLM leaderboard: https://llm-stats.com/

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