The pace at which large language models (LLMs) have evolved over the past few years is nothing short of astonishing. From the early days of GPT-2 astonishing researchers with coherent sentence generation, we’ve now entered an era where models not only generate context-aware text but also reason, execute code, and adapt to multimodal inputs. However, with this rapid evolution comes a complex challenge—how do you compare LLMs meaningfully?
In this article, we compare the top 5 LLMs of today—GPT-4, Claude 2, Gemini 1.5, Mistral, and LLaMA 2—from a purely technical perspective, without drowning in marketing buzzwords. We’ll dive deep into training data composition, architectural differences, parameter counts, context window capacities, and real-world performance benchmarks, everything that matters to ML engineers, AI architects, and enterprise decision-makers.
Contents
1. GPT-4 (OpenAI)
Training Data
While OpenAI has been notoriously secretive about the specifics of GPT-4’s dataset, credible industry sources and leaks suggest a mixture of licensed data, publicly available web data, code repositories (like GitHub), academic papers, and potentially curated datasets from publishers. What we do know: the model has been trained using Reinforcement Learning from Human Feedback (RLHF) to align output with human intent.
Architecture
GPT-4 is speculated to be a mixture-of-experts (MoE) model with up to 8 active expert subnetworks per inference pass, chosen from a larger pool. This MoE architecture is more efficient in terms of computational resource usage, especially at inference time. Each expert may contain up to 220 billion parameters, but only a subset is activated per prompt.
Parameters & Context Window
- Estimated Parameters: >1 Trillion (Total Experts)
- Active Parameters per Inference: ~220B
- Context Window: 128K tokens (GPT-4 Turbo variant)
Performance
GPT-4 is currently a state-of-the-art generalist. It leads in reasoning tasks, code synthesis, and cross-domain adaptability. In benchmarks like MMLU, Big-Bench Hard, and HellaSwag, GPT-4 often outperforms other LLMs with a margin of 5–10%. Its performance is remarkably consistent across domains.
2. Claude 2 (Anthropic)
Training Data
Anthropic’s Claude models are fine-tuned on data that heavily emphasize safety, alignment, and harmlessness. Training corpora include internet-scale data, QA datasets, instructional content, and possibly closed-book question-answer datasets to improve factual consistency.
Architecture
Claude 2 is built on a transformer backbone but with a focus on constitutional AI principles, which means the model is trained to critique and revise its own outputs based on a set of internal rules. While Anthropic hasn’t disclosed whether it uses an MoE, Claude 2 seems optimized for long-context reasoning and conversational stability.
Parameters & Context Window
- Parameters: Estimated ~130B–160B
- Context Window: 100K tokens
Performance
Claude 2 performs well in long-form summarization, knowledge retention, and factual correctness over extended contexts. It’s particularly strong in legal, scientific, and educational use cases due to its memory capabilities and non-hallucinatory tendencies.
3. Gemini 1.5 (Google DeepMind)
Training Data
Gemini 1.5 is trained on multimodal datasets including text, images, audio, and possibly video. Google’s access to YouTube transcripts, BooksCorpus, Wikipedia dumps, and internal datasets like C4 gives Gemini a broad and rich foundation. The training also includes heavy code pretraining from public repositories.
Architecture
Gemini uses a fused multimodal transformer architecture, inspired by Google’s Flamingo and PaLM-E models. Unlike separate encoders for each modality, Gemini features a unified architecture that treats all tokens (text, image embeddings, etc.) uniformly, which allows tight integration across modalities.
Parameters & Context Window
- Parameters: Estimated 300B–600B
- Context Window: 1 Million tokens (Gemini 1.5 Pro)
Performance
In multimodal benchmarks, Gemini outperforms both GPT-4 and Claude in image captioning, chart understanding, and visual reasoning. Its textual reasoning lags slightly behind GPT-4, but its ability to handle documents of extreme length (e.g., entire books) puts it in a league of its own.
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4. Mistral 7B & Mixtral (Mistral AI)
Training Data
Mistral models are trained on diverse internet-scale corpora, including multilingual web data, filtered for quality. The company emphasizes open weights, so researchers have access to model internals and training practices. Mixtral, the MoE variant, uses sparsity in activation to reduce compute load.
Architecture
Mistral 7B is a dense transformer model, while Mixtral uses a mixture-of-experts architecture (similar in concept to GPT-4) with 2 out of 8 experts active per token. This approach helps the model scale up capacity without increasing inference latency proportionally.
Parameters & Context Window
- Mistral 7B: 7B parameters
- Mixtral: 12.9B active / 45B total
- Context Window: 32K tokens
Performance
Mixtral matches or beats models 2x its size on tasks like QA, translation, and code synthesis. Despite its relatively small size, the efficient architecture and smart routing mechanisms make it highly competitive, especially for edge deployment and custom fine-tuning in enterprise use.
5. LLaMA 2 (Meta AI)
Training Data
LLaMA 2 was trained on 2 trillion tokens, incorporating Common Crawl, GitHub, arXiv, Stack Exchange, and Wikipedia, but deliberately excluding known toxic or low-quality content. Meta’s training strategy includes supervised fine-tuning and RLHF.
Architecture
LLaMA 2 follows a decoder-only transformer architecture, with improvements in tokenization, positional embeddings, and normalization strategies. It does not use MoE, but its efficient training pipeline enables scaling up to 65B parameters.
Parameters & Context Window
- Variants: 7B, 13B, 65B
- Context Window: 4K tokens (base), with experimental variants extending up to 32K
Performance
While not as advanced as GPT-4 or Gemini in general reasoning, LLaMA 2 is highly effective for fine-tuning and task-specific deployments. Open-weight availability and efficient inference make it ideal for use cases like enterprise RAG systems, custom agents, and local LLM deployments.
Comparative Summary
| Model | Architecture | Parameters | Context Window | Strengths |
| GPT-4 | MoE (Active Experts) | ~220B active | 128K | Reasoning, Consistency |
| Claude 2 | Constitutional AI | ~160B | 100K | Factual Accuracy, Long-form QA |
| Gemini 1.5 | Multimodal Transformer | ~300–600B | 1M | Multimodal, Large Context |
| Mixtral | MoE (2/8 active) | 12.9B active | 32K | Speed, Fine-tuning |
| LLaMA 2 | Dense Transformer | 7B–65B | 4K–32K | Open Source, Customization |
Real-World Considerations
While benchmarks are critical, real-world applicability hinges on more than just numbers:
- Inference Costs: GPT-4 and Gemini are expensive to run at scale. Mixtral and LLaMA offer lightweight options for startups and mid-sized businesses.
- Customization: LLaMA 2 and Mistral provide open weights, making them prime candidates for enterprise fine-tuning.
- Multimodality: Gemini stands out as the only model truly optimized for text + image inputs today.
- Trustworthiness: Claude 2’s safety-centric design makes it suitable for regulated industries like healthcare and finance.
Final Thoughts
Choosing the right LLM is not just a matter of performance on a leaderboard. It’s about matching the architecture, training approach, and performance characteristics to your organization’s needs. Whether you prioritize alignment, scalability, multimodality, or open access, the landscape of LLMs is now rich enough to accommodate nuanced decisions.
And as the field moves toward agentic architectures, in-context learning, and modular AI ecosystems, today’s “best” model might be tomorrow’s baseline. Keep your AI stack flexible—and keep iterating.
