The ongoing AI models comparison reveals how artificial intelligence is transforming business operations in areas like language processing, coding, and multilingual tasks. Here’s a quick breakdown of the top AI models and their standout features:
- GPT-4: Best for legal, healthcare, and multimodal tasks; excels in safety and accuracy.
- DeepSeek-V3: Great for advanced math, coding, and cost-effective operations with a 128,000-token context window.
- Qwen 2.5: Ideal for multilingual and coding tasks; supports 29 languages and 92 programming languages.
- LG EXAONE 3.0: Focused on bilingual tasks and enterprise solutions like document analysis and coding.
- LlaMA 3.2: Open-source with multimodal capabilities; handles text and high-resolution images efficiently.
- Claude 3.5 Sonnet: Strong in coding, problem-solving, and vision tasks; optimized for speed and collaboration.
- Mistral Large 2: Balances performance and cost; supports 80+ programming languages and excels in math reasoning.
Now let’s take a look at how they compare.
Quick Comparison
| Model | Strengths | Limitations |
|---|---|---|
| GPT-4 | Multimodal, professional-grade, accurate | High cost, resource-intensive |
| DeepSeek-V3 | Cost-efficient, excels in math/coding | Limited in niche domain expertise |
| Qwen 2.5 | Multilingual, advanced coding | Limited deployment data |
| LG EXAONE 3.0 | Bilingual, enterprise-ready | Limited to specific datasets |
| LlaMA 3.2 | Open-source, large context window | Licensing restrictions for EU |
| Claude 3.5 Sonnet | Speed, coding, and collaboration | Limited multilingual support |
| Mistral Large 2 | Cost-effective, strong math skills | Fewer parameters than competitors |
Each model offers unique strengths suited to specific business needs. Consider factors like budget, capabilities, and integration requirements to choose the right one. Let’s dive deeper into each model.
Comparing LLMs: GPT-4, Claude, Gemini, LLaMA – What Are They Good At?
1. GPT-4 by OpenAI
Exploring GPT-4 features, launched in March 2023, highlights OpenAI’s advancements in language models, designed to deliver better reliability, creativity, and deeper understanding. These improvements make it a strong choice for businesses in need of top-tier language processing tools.
One standout feature of GPT-4 is its multimodal capability, allowing it to handle tasks involving both text and images. This opens the door to a wide range of applications across industries. The model also excels in safety, factual accuracy, and managing longer conversations, setting a high standard for AI performance.
Companies like Morgan Stanley, Duolingo, and DoNotPay are already using GPT-4 for various purposes, such as financial analysis, personalized learning, and automating legal tasks.
Deployment options include:
- ChatGPT Plus: Designed for individual users at $20/month.
- ChatGPT Team: Aimed at small and medium-sized businesses.
- API: Tailored for enterprise-level integration.
GPT-4’s enhanced safety measures and ability to deliver accurate information make it ideal for complex tasks that require ongoing interaction. With flexible pricing and advanced features, it’s a compelling option for businesses looking to harness cutting-edge NLP and multimodal capabilities.
Next, we’ll take a closer look at how DeepSeek-V3 stacks up in terms of specialized features and applications.
2. DeepSeek-V3 Overview
Released in December 2024, DeepSeek-V3 brings a new level of performance to AI language models. Designed for businesses looking to balance high performance with cost savings, this model uses a Mixture-of-Experts architecture. By activating only a portion of its parameters for each task, it delivers excellent results while keeping computational demands low.
DeepSeek-V3 stands out in specialized areas, particularly in:
- Advanced mathematics, achieving a 90.2% score on the MATH-500 benchmark
- Competitive coding, with an 82.6% pass rate on HumanEval
- Complex reasoning tasks
- Multilingual processing, excelling in English and Chinese
With an extended context window of 128,000 tokens, it processes documents three times faster than its predecessor. Features like advanced attention mechanisms and predictive algorithms boost both speed and accuracy, handling up to 60 tokens per second.
| Feature | Benefit |
|---|---|
| Processing Speed | Handles documents 3x faster |
| Context Window | Ideal for analyzing long texts |
| Resource Efficiency | Smarter parameter usage |
| Cost Performance | Reduces operational expenses |
DeepSeek-V3 shines in technical fields like scientific research and software development. Its ability to manage complex calculations and competitive programming tasks makes it a reliable choice. The model’s extensive training dataset ensures high accuracy and adaptability across various use cases.
On top of its performance, DeepSeek-V3 is budget-friendly. It offers competitive API pricing for both the deepseek-chat and deepseek-reasoner models. Built at a fraction of the cost of similar models, it demonstrates efficiency in both development and deployment.
For those planning to integrate it, DeepSeek-V3 provides versatile deployment options. Businesses can choose API access or local deployment, offering enhanced data privacy. Its open-source framework also allows for easy customization and seamless integration into existing systems.
Next, we’ll take a closer look at Qwen 2.5 and how it builds on these strengths with added capabilities in multilingual and coding tasks.
3. Qwen 2.5 Features
Released in late 2024, Qwen 2.5 marks a leap forward in AI capabilities, offering scalable parameter options to meet various needs. Trained on a massive 18-trillion-token dataset, it delivers strong performance across a wide range of applications.
The model is designed with advanced multimodal capabilities, allowing it to process text, images, and audio inputs smoothly. It also supports 29 languages, making it an excellent tool for global communication and enterprise use.
| Feature | Capability | Application |
|---|---|---|
| Context Window | Extended capacity | Analyzing long-form documents |
| Language Support | Covers 29 languages | Facilitating global communication |
| Specialized Variants | Math, Coding | Solving technical challenges |
| Parameter Range | Scalable options | Tailored deployment solutions |
Qwen 2.5-Math stands out with an 84% score on the MATH Benchmark, excelling in mathematical problem-solving. Meanwhile, Qwen 2.5-Coder supports 92 programming languages, outperforming competitors like GPT-4 and Claude 3.5 in coding tasks.
The model has already been adopted in various enterprise settings. Xiaomi uses it in their AI assistant, Xiao Ai, to power advanced functionalities in smartphones and smart electric vehicles. Perfect World Games leverages it for plot development and real-time content creation in gaming.
Qwen 2.5 supports both cloud and local deployment, giving businesses the flexibility to optimize performance and manage costs. Its multilingual abilities and technical expertise make it a strong choice for enterprises with diverse needs.
Performance benchmarks further underscore its capabilities, with scores of 89.4 in preference tasks, 76.1 in knowledge-based tasks, and 73.2 in coding challenges.
For businesses, the model also includes enhanced safety measures and bias mitigation, addressing key ethical considerations in AI use. Its modular design and pre-trained baselines make it easier to customize for specific industries.
Next, we’ll delve into LG EXAONE 3.0’s features and see how they stack up against Qwen 2.5.
4. LG EXAONE 3.0 Capabilities
LG EXAONE 3.0 pushes the boundaries of bilingual AI, targeting enterprise needs like document analysis, coding, and real-time language tasks. The name, “EXpert AI for EveryONE”, reflects LG AI Research’s mission to make advanced AI tools accessible to a broader audience.
This version boasts major upgrades compared to its predecessor, cutting down on processing time, memory demands, and operating costs – all while maintaining top-tier performance.
“As it is important to create AI that can be used in real-world industries, we plan to strengthen partnerships between LG affiliates and other companies and institutions with EXAONE 3.0.” – Bae Kyunghoon, President of LG AI Research
EXAONE 3.0 is trained on a massive dataset of over 60 million entries, including patents, software code, and chemistry-related data, along with 350 million images. This extensive training allows it to excel in specialized areas, offering precise language understanding and generation capabilities.
In benchmark tests, EXAONE 3.0 scored an impressive 9.01 in MT-Bench evaluations. For Korean language tasks, it achieved an average score of 74.1 across KoBEST categories such as BoolQ, COPA, and HellaSwag.
One standout application is LG Display’s use of an AI service powered by EXAONE 3.0 to handle queries across 300,000 proprietary documents. ChatEXAONE, based on this model, enables real-time Q&A, document analysis, and advanced coding solutions.
The model’s integration with Google Cloud’s MLOps ensures smooth scalability and dependable performance. Looking ahead, LG AI Research aims to expand the training dataset to over 100 million entries by the end of the year, further boosting its capabilities.
With its focus on bilingual functionality and cost efficiency, LG EXAONE 3.0 sets a strong benchmark. Up next, we’ll take a look at how LlaMA 3.2 takes a different approach, emphasizing open-source development.
5. LlaMA 3.2 Overview
Meta‘s LlaMA 3.2, launched in September 2024, introduces multimodal capabilities to the LlaMA series for the first time. Staying true to open-source principles, it offers developers four versions tailored for various needs: 1B and 3B for text-only tasks, and 11B and 90B for advanced multimodal applications.
One of its standout features is the massive context length of 128,000 tokens, which dwarfs GPT-4’s 8,192 tokens. This allows LlaMA 3.2 to handle lengthy documents with consistent accuracy.
The model’s multimodal abilities extend to high-resolution images (1120×1120 pixels), making it suitable for tasks like image captioning, software testing, and even medical imaging.
| Task Type | Capabilities |
|---|---|
| Visual Analysis | Image captioning, document visual QA |
| Quality Control | Software UI testing, manufacturing inspection |
| Information Processing | Data extraction from images/PDFs |
| Medical Applications | Medical imaging interpretation, patient history integration |
Performance-wise, LlaMA 3.2 holds its ground in standard benchmarks. For instance, the 3B model achieved 63.4% on MMLU and 69.8% on HellaSwag, highlighting its strong language understanding.
Meta AI markets LlaMA 3.2 as a powerful tool for handling both text and visual inputs. Deployment is flexible, offering cloud-based options through Azure AI, AWS Bedrock, and Vertex AI, or local hosting for organizations with the required hardware. For edge deployment, GPUs like NVIDIA A100 or L40S with 40GB memory are necessary.
Being open-source, LlaMA 3.2 allows developers to fine-tune it for specific needs. However, licensing restrictions for multimodal models apply in the EU. The model’s knowledge cutoff of December 2023 ensures it has relatively up-to-date information.
Its multilingual capabilities and adaptability to regional dialects make it a strong option for global use cases.
Next, we’ll dive into Claude 3.5 Sonnet and see how it compares in tackling similar challenges.
6. Claude 3.5 Sonnet Features
Released in June 2024, Claude 3.5 Sonnet is Anthropic‘s latest AI model designed for enterprise use. It showcases advanced reasoning, extensive knowledge, and strong coding skills.
The Artifacts feature allows for real-time editing and collaboration on AI-generated content, making it easier to refine and improve workflows. This capability is especially useful in team-based development projects.
Claude 3.5 Sonnet has achieved impressive results in various benchmarks, excelling in tasks like problem-solving and math. Its versatility makes it a go-to solution for industries that demand both speed and precision.
| Capability Area | Performance Metrics |
|---|---|
| Problem-solving | 64% success rate (up from 38% in Claude 3 Opus) |
| Math Proficiency | 71.1% accuracy score |
| Visual Math Reasoning | 67.7% on the MathVista test |
| BIG-Bench-Hard | 93.1% benchmark score |
Deployment options include free access through Claude.ai, as well as Pro and Team Plans, the Anthropic API, and cloud platforms like Amazon Bedrock and Google Cloud Vertex AI. Pricing is set at $3 per million input tokens and $15 per million output tokens.
The model also excels in vision-related tasks, such as transcribing text from imperfect images and analyzing visual data. In the finance sector, it can interpret charts, extract insights from reports, and run Monte Carlo simulations for forecasting.
Another standout feature is its built-in prompt generator, which simplifies AI product development. With its problem-solving strengths and advanced vision capabilities, Claude 3.5 Sonnet is well-suited for tackling complex enterprise challenges.
Next, we’ll explore how Mistral Large 2 takes these advancements further with its emphasis on scalability and open-source development.
7. Mistral Large 2 Key Features
Launched in July 2024, Mistral Large 2 packs a punch with its 123 billion parameters, making it a noteworthy contender in the AI language model space. Designed to handle diverse, large-scale projects, it offers a balance of performance, multilingual support, and affordability. Its 128,000-token context window is ideal for handling extensive documents and detailed analysis.
This model delivers strong results across benchmarks, scoring 84.0% on MMLU and 92% on HumanEval, placing it just behind GPT-4 for coding tasks. When it comes to mathematical reasoning, it achieves an impressive 93% accuracy rate on the GSM8K benchmark.
| Feature | Specification |
|---|---|
| Parameters | 123 billion |
| Programming Languages | 80+ supported |
| Natural Languages | 11 languages |
| Input Cost | $2.00 per million tokens |
| Output Cost | $6.00 per million tokens |
One of its standout features is its broad language support. It handles major languages like English, French, and German, while also covering specialized languages like Hindi and Korean, making it a practical choice for global enterprises.
The model offers native JSON output, simplifying integration into various applications. Its advanced function calling capabilities allow for both parallel and sequential operations, adding flexibility for complex workflows.
Mistral Large 2 is available for deployment on major cloud platforms or through local hosting, catering to different operational needs. Cost-wise, it’s a budget-friendly option, priced at 12.5 times lower than GPT-4.
For developers, the model supports over 80 programming languages, including Python, Java, C++, and JavaScript. Enhanced instruction-following ensures accurate and transparent responses, reducing errors – especially useful for software development.
Its architecture is tailored for single-node inference, making it efficient for production environments without sacrificing performance. This mix of advanced functionality and practical deployment options makes Mistral Large 2 an attractive choice for both research and business applications.
Next, we’ll dive into how it stacks up against other models in this space.
Strengths and Weaknesses of Each AI Model
Let’s take a closer look at how these AI models stack up in terms of strengths and weaknesses. This breakdown can help organizations decide which model aligns best with their goals and resources.
| Model | Strengths | Limitations |
|---|---|---|
| GPT-4 | • Excels in professional fields like healthcare and law • 90th percentile Bar Exam performance • Advanced problem-solving • Strong safety mechanisms | • High deployment costs • Resource-intensive • Limited expertise in niche domains |
| Qwen 2.5 | • Extensive training data • Supports 29 languages • Large context window • Advanced coding abilities | • Still gaining traction in the market • Development is ongoing • Limited real-world deployment data |
| Claude 3.5 Sonnet | • Exceptional coding performance • Processes tasks twice as fast • Vision capabilities • Focused on software development | • Limited multilingual features • High resource requirements • Complex to implement |
| Mistral Large 2 | • 123 billion parameters • Supports over 80 coding languages • Cost-efficient • Strong math reasoning | • Fewer parameters compared to competitors • Lacks deep domain expertise • Limited enterprise experience |
These differences translate into specific advantages depending on the use case. For example, Claude 3.5 Sonnet’s speed boost makes it a strong choice for fast-paced tasks like customer support. On the other hand, Qwen 2.5’s multilingual support and extended token handling make it a go-to option for global operations.
GPT-4 continues to shine in professional settings, with its top-tier Bar Exam performance highlighting its legal proficiency. For businesses focused on cost savings, Mistral Large 2 delivers solid functionality at a fraction of GPT-4’s price.
Choosing the right model depends on aligning its capabilities with your specific needs. While some models specialize in niche areas, others provide a broader range of features, each with varying levels of performance. This comparison sets the stage for a closer look at how these models perform in real-world scenarios.
Final Thoughts
Our AI models comparison highlights an evolving landscape, with each model offering specific strengths tailored to different needs.
For businesses needing advanced reasoning and professional-grade applications, GPT-4 sets the standard. It shines in specialized fields but comes with higher deployment costs, making it ideal for organizations with larger budgets.
For those prioritizing affordability, Mistral Large 2 provides strong performance at a lower cost. With 123 billion parameters and support for over 80 coding languages, it offers solid capabilities without the premium price tag.
Claude 3.5 Sonnet is a standout for software development. It doubles the processing speed of its previous version while maintaining competitive pricing, making it a great choice for teams that value both speed and precision.
When it comes to multilingual tasks, Qwen 2.5 is a top pick. Supporting 29 languages and backed by extensive training data, it’s especially useful for global businesses handling diverse language needs.
If you’re selecting an AI model, keep these factors in mind:
- Budget: Balance current costs with future scalability.
- Capabilities: Look at specific needs like multilingual support or coding expertise.
- Integration: Factor in how easily the model fits into your existing systems.
“While we believe there is no one-size-fits-all LLM for every use case, we hope that this list can help you identify the most current and well-suited LLM model that meets your business’s unique requirements.”
As these technologies advance, knowing their strengths and limits will help you make smarter decisions in this rapidly changing field.
