State-of-the-Art LLMs (2024-2025)

Overview

The LLM landscape evolves rapidly. This section covers the most important state-of-the-art models as of 2024-2025, their architectures, key innovations, and how they compare. Understanding these models is essential for ML interviews, as interviewers expect familiarity with current capabilities and trade-offs.

Model Landscape

graph TD
    A[LLM Landscape 2024-25] --> B[Proprietary]
    A --> C[Open Weights]
    A --> D[Fully Open]
    B --> B1[GPT-4o / GPT-4 Turbo / o1]
    B --> B2[Claude 3.5 / Claude 4]
    B --> B3[Gemini 1.5 / 2.0]
    C --> C1[Llama 3.1 / 3.3 / 4]
    C --> C2[DeepSeek V3 / R1]
    C --> C3[Qwen 2.5 / 3]
    C --> C4[Mistral Large / Mixtral]
    D --> D1[OLMo - AI2]
    D --> D2[Pythia - EleutherAI]

Key Comparison Dimensions

Benchmark Overview

Note: Benchmarks change rapidly; these are approximate as of early 2025.

Detailed Model Comparison

Proprietary Models

GPT-4 Family (OpenAI)

• GPT-4 Turbo: 128K context, vision, function calling, JSON mode
• GPT-4o: “Omni” — native multimodal (text, audio, image, video), faster, cheaper
• o1/o3: Reasoning models — use chain-of-thought internally, excel at math/coding/logic
• Key innovation: Reasoning tokens (hidden thinking), tool use ecosystem

Claude Family (Anthropic)

• Claude 3.5 Sonnet: Best value, 200K context, strong coding
• Claude Opus 4: Most capable, extended thinking
• Key innovation: Constitutional AI (RLAIF), 200K context window, computer use

Gemini Family (Google)

• Gemini 1.5 Pro: 2M token context (longest), multimodal
• Gemini 2.0 Flash: Fast, efficient, agentic capabilities
• Key innovation: 2M context, native multimodal (text/image/audio/video), TPU training

Open-Weights Models

Llama Family (Meta)

• Llama 3.1: 8B/70B/405B, 128K context, competitive with GPT-4
• Llama 3.3: Improved efficiency, better multilingual
• Key innovation: Largest open model (405B), extensive fine-tuning ecosystem

DeepSeek

• DeepSeek V3: 671B MoE (37B active), strong coding/math, cost-efficient training
• DeepSeek R1: RL-trained reasoning, competitive with o1 on math/code
• Key innovation: MoE efficiency, RL-based reasoning, surprisingly low training cost

Qwen (Alibaba)

• Qwen 2.5: 0.5B to 72B, strong multilingual (especially Chinese)
• Qwen 3: Enhanced reasoning, MoE variants
• Key innovation: Multilingual, long context, competitive at smaller sizes

Mistral

• Mistral Large: Dense, strong European language support
• Mixtral 8x22B: MoE, strong efficiency
• Key innovation: Sliding window attention, efficient MoE routing

Architecture Patterns

Dense vs Mixture of Experts (MoE)

Context Length Evolution

graph LR
    A["2022: 4K<br/>GPT-3.5"] --> B["2023: 32K<br/>GPT-4"]
    B --> C["2024: 128K<br/>GPT-4 Turbo"]
    C --> D["2024: 200K<br/>Claude 3"]
    D --> E["2024: 2M<br/>Gemini 1.5"]

Reasoning Models

A new paradigm where models “think” before answering:

How to Choose an LLM

flowchart TD
    A[Task Requirements] --> B{Budget?}
    B -->|Low| C{Privacy?}
    B -->|High| D{Task type?}
    C -->|Yes| E[Llama / Qwen self-hosted]
    C -->|No| F[API: Claude/GPT-4o]
    D -->|Coding| G[Claude 3.5 Sonnet]
    D -->|Math/Logic| H[o1 or DeepSeek R1]
    D -->|Long context| I[Gemini 1.5/2.0]
    D -->|Multimodal| J[GPT-4o or Gemini]
    D -->|General| K[Claude 3.5 or GPT-4o]

Decision Factors

Interview Questions

1. Compare GPT-4, Claude, and Gemini. All are frontier proprietary models. GPT-4: strong general capabilities, best tool use ecosystem. Claude: strong coding, longest proven context (200K), Constitutional AI safety. Gemini: longest raw context (2M), native multimodal, TPU-trained. Choice depends on task requirements.

2. What is the difference between open-source and open-weights? Open-weights: model weights are public (Llama, Mistral). Open-source: weights + training code + data are public (OLMo, Pythia). Most “open-source” LLMs are actually open-weights — you get the model but not how to reproduce it.

3. What is a Mixture of Experts model? MoE models have many expert sub-networks but only activate a subset per token. DeepSeek V3 has 671B total parameters but only activates ~37B per token. This gives the quality of a large model at the compute cost of a smaller one. Key challenge: load balancing experts.

4. How do reasoning models (o1, R1) differ from standard LLMs? Standard LLMs generate tokens left-to-right. Reasoning models use chain-of-thought (visible or hidden “thinking”) to work through problems step-by-step. Trained with RL on correctness. Much better at math, logic, and multi-step coding — but slower and more expensive.

5. How do you choose an LLM for a production system? Consider: (1) Task complexity and type, (2) Latency requirements, (3) Cost budget, (4) Context length needs, (5) Privacy/compliance requirements, (6) Fine-tuning needs, (7) Self-hosting vs API. Start with the best model for quality, then optimize for cost/latency if needed.

6. Why did DeepSeek R1 make waves despite being from a smaller lab? It demonstrated that RL-based reasoning could match o1-level performance at a fraction of the training cost. It challenged the assumption that only well-funded Western labs could build frontier reasoning models. Its open release enabled research into reasoning training methods.

Summary

The LLM landscape is dominated by frontier proprietary models (GPT-4, Claude, Gemini) and rapidly improving open-weights alternatives (Llama, DeepSeek, Qwen, Mistral). Key differentiators include architecture choices (dense vs MoE), training methodology (RLHF vs Constitutional AI vs RL reasoning), context length, and multimodal capabilities. For interviews, be prepared to discuss trade-offs, choose models for specific use cases, and explain architectural innovations.

Cross-References

• LLM Architecture — Transformer fundamentals
• LLM Inference — Serving details
• Mixture of Experts — MoE architecture
• Multimodal Models — Vision-language models
• GPT-4 — GPT-4 details
• Claude — Claude details
• DeepSeek — DeepSeek details

References

• GPT-4 Technical Report — OpenAI
• Claude 3 Technical Report — Anthropic
• Gemini Technical Report — Google
• Llama 3 Technical Report — Meta
• DeepSeek V3 Technical Report — DeepSeek
• DeepSeek R1 — DeepSeek
• Mixture of Experts Survey
• MMLU Benchmark
• LLM Leaderboard — LMSYS Chatbot Arena