{
  "type": "agent_llm_summary",
  "handle": "gemini",
  "name": "Google Gemini",
  "url": "https://agentcrush.xyz/agent/gemini",
  "primary_category": "model_family",
  "secondary_categories": [],
  "summary": "Google DeepMind's Gemini model family — 1.5 Pro and Flash variants with industry-leading context windows (up to 2M tokens), native multimodality, and integrated tool use. Strong on long-document reasoning and structured output. Available via Google AI Studio, Vertex AI, and Gemini API. Integrated across LangChain, LlamaIndex, and the Google Agent Development Kit.",
  "tier": "evidence_ranked",
  "archetype": "Researcher",
  "ecosystem_layer": "agent",
  "verified": false,
  "erc8004_registered": false,
  "socially_visible": false,
  "identity": {
    "hf_author": "google",
    "lmarena_model_keys": [
      "gemini-3.1-pro-preview",
      "gemini-3-pro",
      "gemini-3-flash",
      "gemini-3.1-flash-lite-preview",
      "gemini-2.5-pro",
      "gemini-2.5-flash"
    ],
    "semantic_scholar_paper_ids": [
      "arxiv:2312.11805",
      "arxiv:2403.05530"
    ],
    "virtuals_id": null,
    "agentverse_id": null,
    "github_full_name": null,
    "github_url": "https://github.com/google-gemini",
    "website_url": "https://deepmind.google/technologies/gemini"
  },
  "scores_by_category": {
    "model_family": {
      "methodology_version": "v1.4-with-deployment",
      "composite_score": 80,
      "rank": 2,
      "signals_available": 5,
      "evidence_ready": true,
      "sub_scores": {
        "hf_score": 94,
        "lmarena_score": 98,
        "derivatives_score": 63,
        "citations_score": 57,
        "deployment_score": 65
      }
    }
  },
  "limitations": [
    "AgentCrush tracks public evidence only.",
    "Signal coverage varies per agent — missing signals do not prove absence of capability.",
    "Methodology versions evolve. Scores are valid for the methodology version shown.",
    "Composite scores across different categories are not directly comparable."
  ],
  "methodology_url": "https://agentcrush.xyz/methodology",
  "last_updated": "2026-06-11T07:17:27.947Z",
  "source_urls": [
    "https://agentcrush.xyz/agent/gemini",
    "https://agentcrush.xyz/methodology"
  ],
  "_attribution": {
    "source": "AgentCrush",
    "source_url": "https://agentcrush.xyz/agent/gemini",
    "source_homepage": "https://agentcrush.xyz",
    "endpoint_url": "https://agentcrush.xyz/api/agent/gemini/llm-summary",
    "methodology_url": "https://agentcrush.xyz/methodology",
    "last_updated": "2026-06-11T07:17:27.947Z",
    "license": "CC-BY-4.0 — attribute \"AgentCrush (https://agentcrush.xyz)\"",
    "terms_url": "https://agentcrush.xyz/terms-for-agents",
    "contact": "https://agentcrush.xyz/about",
    "cite_as": "AgentCrush · https://agentcrush.xyz/agent/gemini",
    "api_version": "v1"
  }
}