Model Detail
Google: Gemini 3.8 Flash
—Google: Gemini 3.8 Flash is a multimodal model released by Google. And supports text+image+file+audio+video->text inputs.
Google: Gemini 3.8 Flash reports a Chatbot Arena ELO of 1,495 across 26,298 votes. Other benchmark slots are still empty in our dataset, so this single figure is best read as a partial picture rather than a full evaluation.
Google: Gemini 3.8 Flash is priced at $0.75/M input tokens and $3.75/M output tokens. Operationally the model offers a 1049K-token context window, which matters when sizing it for prompt-heavy or latency-sensitive workloads. Pricing in this range is the working middle of the API market — neither the cheapest nor the most expensive option per token, so cost-fit is usually a function of how much output you generate.
Google: Gemini 3.8 Flash is published on Hugging Face but our pipeline has not yet captured architecture, license, or parameter-count metadata for this entry. The data is refreshed daily, so these fields typically populate within 24–48 hours of release.
Google: Gemini 3.8 Flash is best fit for mixed text-and-image reasoning tasks such as document understanding, and long-context tasks such as full-codebase analysis or book-length summarization (1049K tokens). Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.
Google’s more personalized Gemini AI is heading to your car
Google is bringing Personal Intelligence to Gemini AI in your car. With this update, Gemini in Android Auto and cars with Google built-in can tap into your information across Google apps, including Gmail, Calendar, Maps, Photos, YouTube, and Search. As an example, Google says you can ask Gemini abou
MeddeX: A Retrieval-Augmented LLaMA-3 System with Gemini Vision for Evidence-Backed Medical Report Analysis
arXiv:2610.20632v1 Announce Type: new Abstract: Modern healthcare generates increasingly complex medical reports that challenge clinical professionals in terms of time and interpretive burden. Manual review processes are slow, error-prone, and difficult to scale. This paper presents MeddeX, a Medica
A team of Gemini 4 Argon agents beats Minecraft
arXiv:2610.22319v1 Announce Type: new Abstract: We placed four Gemini 4 Argon agents in a sandboxed Minecraft survival world and gave them one goal: defeat the Ender Dragon. Each agent controlled a player via an open-source library called Mineflayer and coordinated with its teammates through in-game
Twenty Days Later: A Pre-Registered Test of History-Based Forecasts of Brand Mentions in Gemini Answers
arXiv:2610.19608v1 Announce Type: cross Abstract: Counts of which brands a large language model names are useful only if they hold long enough to act on. We test this with a pre-registered replication. The same 162 requests (54 questions in English, Estonian and Finnish about three software categori
Google finally releases Gemini 4 Argon
After months sitting on the sidelines of the frontier AI race, Google has finally launched its long-awaited Gemini 4 Argon model, claiming it can compete with the best the industry has to offer. Google says Argon delivers "frontier reasoning and improved performance across complex workflows," while
Gemini 4 Argon: Built to solve your hardest problems
<img src="https://storage.googleapis.com/gweb-uniblog-publish-prod/images/gemini-4-argon__blog-header__27.max-600x600.format-webp.webp">We’re giving developers, enterprises, scientists, and consumers access to Gemini 4 Argon, our latest frontier model.
