Model Detail
Google: Gemini 3.1 Pro Preview
—Google: Gemini 3.1 Pro Preview is a multimodal model released by Google. And supports text+image+file+audio+video->text inputs.
Google: Gemini 3.1 Pro Preview reports a Chatbot Arena ELO of 1,488 across 50,137 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 3.1 Pro Preview is priced at $2/M input tokens and $12/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 3.1 Pro Preview 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 3.1 Pro Preview 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.
The Granularity Gap: A Multi-Dimensional Longitudinal Audit of Sycophancy in Gemini Models
arXiv:2606.05183v1 Announce Type: cross Abstract: Large language models are increasingly deployed as high-stakes advisors, yet standard alignment benchmarks treat sycophancy as a binary failure mode. We introduce the Granularity Gap: coarse binary metrics mask substantial social-compliance behaviors
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I put Google’s 24/7 AI assistant Gemini Spark to work, and it’s actually pretty useful
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