Gemini 4 Argon Explained
Gemini 4 Argon is Google's new frontier model, and the most interesting part isn't the benchmarks: it's a 1 million token output limit in a single response, up from 64K in earlier Gemini models. In this video I go through Argon's official benchmarks, Arena's cost-per-task testing, pricing, and what a million-token output means for test-time compute and long-running reasoning. Gemini 4 Argon isn't publicly available yet. It's rolling out first to trusted testers through Google's Fairwind program, with paid API customers and Google AI Ultra next. Sources: Google, Gemini 4 Argon announcement: https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-4-argon/ Google DeepMind, Argon evaluation methodology: https://deepmind.google/models/evals-methodology/gemini-4-argon Arena, cost per task results: https://x.com/arena/status/2105449871671173257 Arena Agent leaderboard (Pareto view): https://arena.ai/leaderboard/agent Vals Index: https://www.vals.ai/benchmarks/vals_index Zapier AutomationBench: https://zapier.com/benchmarks CWE-bench: https://cwe-bench.com/?v=v1 Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters (UC Berkeley, Google DeepMind): https://arxiv.org/abs/2408.03314 Gemini 1.5 announcement (1M context): https://blog.google/technology/ai/google-gemini-next-generation-model-february-2024/ Gemini 1.0 announcement (natively multimodal): https://blog.google/technology/ai/google-gemini-ai/ Fairwind Program: https://deepmind.google/fairwind-program/ Claude pricing (for the Sonnet 5.5 / Opus 5.5 comparison): https://platform.claude.com/docs/en/about-claude/pricing #Gemini4 #GeminiArgon #GoogleDeepMind #LLM #AI My voice to text App: whryte.com Website: https://engineerprompt.ai/ RAG Beyond Basics Course: https://prompt-s-site.thinkific.com/courses/rag Signup for Newsletter, localgpt: https://tally.so/r/3y9bb0 💻 Pre-configured localGPT VM: https://bit.ly/localGPT (use Code: PromptEngineering for 50% off). Chapters: 0:00 Gemini 4 Argon is here 0:52 Benchmarks: Arena cost per task and the Pareto frontier 1:39 Enterprise knowledge work: Vals Index and AutomationBench 2:22 Coding: DeepSWE and Google's Rust migrations 3:10 Cybersecurity defense: CWE-bench 3:24 The 1M output token limit 4:08 Why it matters: test-time compute 5:12 Paper: small models that think longer vs 14x larger models 6:48 Fun fact and final thoughts
