# ⚡️GPT 4.1: The New OpenAI Workhorse

Page: https://stenobird.com/podcast/latent-space-ai-engineer/gpt-4-1-the-new-openai-workhorse
Text version: https://stenobird.com/podcast/latent-space-ai-engineer/gpt-4-1-the-new-openai-workhorse.md
Podcast: [Latent Space: The AI Engineer Podcast](https://stenobird.com/podcast/latent-space-ai-engineer)
Published: 2025-04-15T15:00:00+00:00
Episode link: https://www.latent.space/p/gpt-41-the-new-openai-workhorse
Audio file: https://api.substack.com/feed/podcast/186632768/3cc10438ec04e08b890b62b2b6f7d69f.mp3
Processing state: processed
JSON: https://stenobird.com/v1/public/podcasts/latent-space-ai-engineer/episodes/gpt-4-1-the-new-openai-workhorse
Duration seconds: 2512

## Resource

We’ll keep this brief because we’re on a tight turnaround: GPT 4.1 , previously known as the Quasar and Optimus models , is now live as the natural update for 4o/4o-mini (and the research preview of GPT 4.5). Though it is a general purpose model family, the headline features are: Coding abilities (o1-level SWEBench and SWELancer, but ok Aider) Instruction Following (with a very notable prompting guide) Long Context up to 1m tokens (with new MRCR and Graphwalk benchmarks) Vision (simply o1 level) Cheaper Pricing (cheaper than 4o, greatly improved prompt caching savings) We caught up with returning guest Michelle Pokrass and Josh McGrath to get more detail on each! Full Video Episode Timestamps Part 1 00:00:00 Introduction and Guest Welcome 00:00:57 GPT 4.1 Launch Overview 00:01:54 Developer Feedback and Model Names 00:02:53 Model Naming and Starry Themes 00:03:49 Confusion Over GPT 4.1 vs 4.5 00:04:47 Distillation and Model Improvements 00:05:45 Omnimodel Architecture and Future Plans 00:06:43 Core Capabilities of GPT 4.1 00:07:40 Training Techniques and Long Context 00:08:37 Challenges in Long Context Reasoning 00:09:34 Context Utilization in ModelsPart 2 00:10:31 Graph Walks and Model Evaluation 00:11:31 Real Life Applications of Graph Tasks 00:12:30 Multi-Hop Reasoning Benchmarks 00:13:30 Agentic Workflows and Backtracking 00:14:28 Graph Traversals for Agent Planning 00:15:24 Context Usage in API and Memory Systems 00:16:21 Model Performance in Long Context Tasks 00:17:17 Instruction Following and Real World Data 00:18:12 Challenges in Grading Instructions 00:19:09 Instruction Following Techniques 00:20:09 Prompting Techniques and Model Responses 00:21:05 Agentic Workflows and Model PersistencePart 3 00:22:01 Balancing Persistence and User Control 00:22:56 Evaluation…

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## Transcript

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