DemandLab

Updated daily · Arena + Artificial Analysis

Call Transcripts & Long Context

Synthesizing a quarter of Gong/Granola calls, full CRM histories, or an entire content library in one pass.

One board from The GTM Model Leaderboard

Call Transcripts & Long Context Leaderboard

Synthesizing a quarter of Gong/Granola calls, full CRM histories, or an entire content library in one pass.

Artificial Analysis · context window leaders · Jul 27, 2026 ↗
  1. 1
    Llama 4 Scout
    Meta
    10M tokens
    Context window
  2. 2
    Grok 4.20 (0309)
    xAI
    2M tokens
    Context window
  3. 3
    Gemini 1.5 Pro / 3 Pro
    Google
    1-2M tokens
    Context window
  4. 4
    Claude Fable 5
    AnthropicBest recall quality per Arena Longer Query #1
    1M tokens
    Context window
  5. 5
    GPT-5.6 Sol
    OpenAI
    400K tokens
    Context window

Operator take · Raw window size is not recall quality. A 10M-token window that loses the middle of your call archive is worse than a 1M window that doesn't. Test with a needle from your own transcripts before committing.

Updated daily from the linked public leaderboards; last capture Jul 27, 2026. Elo and win-rate figures are preference-based measures, not task-completion guarantees. Always validate the top pick on your own representative work before routing production volume to it.

How to Read These Rankings

Four rules before you switch models

  • Every ranking is task-specific. The #1 agent model is not the #1 writer, and neither is the right pick for 10,000 Clay rows.
  • Scores come from public leaderboards (Arena, Artificial Analysis) with capture dates shown. Preference Elo measures which output people like, not whether work gets completed.
  • Reasoning effort and harness matter: the same model at a different effort tier or in a different agent harness ranks differently.
  • Cheap plus fast beats frontier for volume work. Route by job, not by brand loyalty.

We route these models inside production GTM systems every day. If you want help picking and wiring the right models into your own stack, that's what we do.

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