Models (Q3/Q4 2024 – Projected 2025)

Date: 2024-11-18

Sources:

• Excerpts from “Pasted text” (Source 1 – focusing on benchmarks and SOTA)
• Excerpts from “Pasted text” (Source 2 – focusing on user recommendations and validation)
• Excerpts from “Pasted text” (Source 3 – focusing on 2025 forecasts and domain-specific tools)
• Excerpts from “Pasted text” (Source 4 – detailed breakdown by use case based on various evidence)
• Excerpts from “Pasted text” (Source 5 – domain-specific tools and key considerations)

Executive Summary:

The AI landscape continues its rapid evolution, with significant progress demonstrated across various domains including language understanding, generation, vision, speech, and code. Standardised benchmarks like GLUE, SuperGLUE, MLPerf, ImageNet, LibriSpeech, and HumanEval remain crucial for measuring state-of-the-art performance, although their limitations are increasingly recognised. Leading models, both proprietary (e.g., OpenAI’s GPT-4o/o1, Anthropic’s Claude 3 family, Google’s Gemini 1.5 Pro/2.0, NVIDIA’s Blackwell platform) and open-source (e.g., Meta’s Llama 3, Mistral AI’s Mixtral), are pushing the boundaries of capability, often achieving near-human or even surpassing human performance on specific tasks. Key trends include increasing multimodality, advancements in cost-efficiency and speed, the rise of autonomous agents, and growing emphasis on ethical considerations and benchmark validity. Selecting the “best” AI remains highly dependent on the specific use case, requiring consideration of performance metrics, cost, latency, privacy, and the need for customisation.

Key Themes and Important Ideas/Facts:

1. State-of-the-Art Performance Across Domains:

• Language Understanding: Microsoft’s T-NLRv5 leads on GLUE and SuperGLUE, showing “human-level performance with fewer resources” (Source 1). SuperGLUE is highlighted as a more challenging benchmark testing “general-purpose language understanding beyond sentence classification” (Source 1).
• Large-Scale Training & Inference: MLPerf benchmarks demonstrate significant hardware and software advancements. NVIDIA’s Blackwell platform (GB200 NVL72 & DGX B200) sets “industry-leading inference throughput” records (Source 1, 3). MLPerf v4.1 shows speedups for Generative AI tasks, and v5.0 includes new benchmarks like LLMs and text-to-image (Source 1).
• Computer Vision: CoCa (finetuned) and Model Soups (BASIC-L) achieve state-of-the-art on ImageNet for classification (Source 1). Stable Diffusion XL and DALL·E 3 lead in text-to-image generation benchmarks (Source 1). Tools like Roboflow, OpenCV, and TensorFlow are crucial for building custom vision solutions (Source 5).
• Speech Recognition: United Med ASR achieves the “new lowest WER on LibriSpeech test-clean” (Source 1). OpenAI’s Whisper remains a “premier open-source model for transcription,” excelling in multilingual scenarios and robustness, though with slightly higher error rates on clean speech than closed-source systems (Source 1). Assemblyai-universal-2 is noted for consistency across diverse audio conditions (Source 5).
• Code Generation: OpenAI’s LLMDebugger (o1) tops HumanEval with a “99.4 % pass@1,” demonstrating the power of fine-tuning (Source 1). DeepSeek R1 is highlighted as a leading open-source model dominating coding and mathematics benchmarks, offering “30x cost efficiency compared to rivals” (Source 3).

2. Leading Models by Use Case (Based on Various Evidence):

Multiple sources converge on key models for specific applications, drawing on benchmarks, user feedback, and forecasts.

• General-Purpose Reasoning & Complex Tasks: GPT-4o, Claude 3 Opus, and Gemini 1.5 Pro are consistently cited as top contenders across benchmarks and user preference studies like Chatbot Arena (Source 4). GPT-4.5 and Gemini 2.0 are projected leaders for 2025, with GPT-4.5 showing significant improvement on coding benchmarks and Gemini 2.0 focusing on Large Action Models (LAMs) and real-time multimodal input (Source 3).
• Creative Text Generation: Claude 3 family (Opus, Sonnet) is often praised for “nuanced, creative, and often more ‘natural’ or less ‘robotic’ writing style,” alongside GPT-4o/4 and Gemini 1.5 Pro (Source 4). Claude 3.7, o1 pro, and GPT-4.5 are highlighted for generating “high-quality text” (Source 2).
• Coding Assistance: DeepSeek R1 is a leading open-source option (Source 3), while GPT-4o, Claude 3 Opus, and Gemini 1.5 Pro perform strongly on coding benchmarks (Source 4). Fine-tuned models and tools like GitHub Copilot X (integrated with GPT-4.5) are crucial for real-world application (Source 3, 4).
• Data Analysis: Dedicated tools like Team-GPT, Luzmo, and Tableau are recommended (Source 5), with ChatGPT also widely used by finance professionals for its natural language interface (Source 5). Domo and Power BI are noted for enterprise data workflows (Source 3).
• Search and Information Retrieval: Perplexity and GPT-4o are recommended for their ability to deliver “instant, concise answers” and summarise complex data (Source 2).
• Research and Deep Analysis: Gemini 2.0 is recommended for handling “large documents and videos,” and ChatGPT Deep Research for “in-depth inquiries” (Source 2).
• Multimodal Tasks: Gemini 2.0 and Grok are cited for their versatility across text, images, videos, and documents (Source 2). GPT-4o is also a strong multimodal model with native vision/audio integration (Source 4).
• Autonomous Agents & Workplace Automation: Grok 3 with “Super Grok Agents” and OpenAI’s o1 are noted for autonomous task execution (Source 3). Salesforce Agentforce also embeds agentic AI into workflows (Source 3).
• Cost-Efficiency: DeepSeek R1 is highlighted for its low training cost (Source 3). Claude 3 Haiku, GPT-3.5 Turbo, Gemini Flash, and Llama 3 8B offer good “bang for buck” for speed and cost-efficiency (Source 4).
• Image Generation: Midjourney, DALL-E 3, Stable Diffusion family (SDXL, SD3), and Ideogram are leading models, with Midjourney noted for artistic quality and DALL-E 3 for prompt adherence (Source 4).
• Audio & Speech Generation (TTS): ElevenLabs is widely regarded for its “highly realistic voice cloning and expressive speech synthesis” (Source 4), alongside OpenAI TTS and others.

3. The Role and Limitations of Benchmarks:

• Benchmarks like GLUE, SuperGLUE, MLPerf, ImageNet, LibriSpeech, HumanEval, MMLU, HELM, GSM8k, and MBPP provide “standardized comparisons” (Source 4) and are crucial for tracking progress (Source 1, 3).
• However, benchmarks “don’t capture all aspects of performance” (Source 4), such as “nuanced creativity, long-form coherence, real-world robustness, safety subtleties, cost-effectiveness” (Source 4).
• User preference studies (like Chatbot Arena) and qualitative evaluations offer “valuable real-world perspectives” (Source 4).
• The validity of traditional benchmarks is being questioned, with calls for “task-specific validity” frameworks like BetterBench (Source 3).
• Anecdotal evidence from social media platforms like X can provide “practical guidance” but lacks the rigor of formal evaluations and “definitive conclusions require verification through systematic research” (Source 2).

4. Evolving Trends and Considerations:

• Rapid Evolution: The AI field is moving “incredibly fast,” with rankings and capabilities shifting significantly (Source 4).
• Multimodality: Many top models are becoming increasingly multimodal, handling text, images, audio, and video (Source 4).
• Open Source: Open-source models like Llama 3 and Mixtral are increasingly competitive, offering flexibility and control for research and deployment (Source 4).
• Cost-Efficiency: Focus is increasing on developing and deploying models that are both performant and cost-effective (Source 3, 4).
• Autonomous Agents: The development of AI agents capable of performing complex tasks is a significant trend (Source 3).
• Ethics and Governance: Ethical AI and safety considerations are becoming more prominent, particularly for sensitive domains (Source 3). “Only 1% of companies achieve AI maturity due to governance challenges like shadow AI” (Source 3). Claude 3.7 is noted for prioritising “ethical AI and safety” (Source 3).
• Industry-Specific Needs: The choice of AI tool often depends on the specific requirements of an industry, such as healthcare or agriculture (Source 5).
• Cost vs. Customisation: Open-source tools offer flexibility but require technical expertise, while cloud APIs provide ease of use at a premium (Source 5).

Conclusion and Recommendations:

Selecting the optimal AI model or tool necessitates a careful assessment of the specific task requirements, balancing performance metrics from diverse sources (benchmarks, user feedback, technical reports) with practical considerations like cost, latency, privacy, and the need for customisation. Relying solely on traditional benchmarks is insufficient; practitioners should also consider real-world performance, user preferences, and the evolving landscape of AI capabilities and ethical guidelines. Continuous monitoring of updated benchmarks, research reports, and domain-specific evaluations is essential to ensure optimal AI selection in this rapidly advancing field. For critical applications, consulting systematic reviews and meta-analyses, where available, provides the most robust evidence base.

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