AI Evaluation

AI evaluation has four layers. Models are evaluated. Agents are evaluated. Outputs are evaluated. Operators are not — until SigRank. The operator layer is the missing piece of AI evaluation.

Four layers of AI evaluation. One was missing.

Model evals, agent evals, and output evals all exist. Operator evals — measuring the human directing the AI — did not. SigRank fills that gap with public, content-free, continuous evaluation.

Model Evaluation

Standardized benchmark suites that measure how well an AI model performs on reasoning, coding, math, and knowledge tasks.

Subject: the AI model
Question: which model is best?
Examples: Vals AI, LMSYS Arena, LiveBench, MMLU

Agent Evaluation

Task-completion evals that measure whether an autonomous agent achieves its goal end-to-end.

Subject: the AI agent
Question: did the agent complete the task?
Examples: SWE-bench, WebArena, GAIA

Output Evaluation

Quality scoring of individual AI outputs — correctness, safety, helpfulness — often via LLM-as-judge or human raters.

Subject: the AI output
Question: is this output good?
Examples: Braintrust, Langfuse, Ragas

Operator Evaluation

Telemetry-based evaluations that measure how effectively a human operator uses AI — the cascade architecture, not the model capability.

Subject: the human operator
Question: who is the best AI operator?
Examples: SigRank (the only one)

The operator layer is the missing piece

Evaluating AI without evaluating the operator is like evaluating a car without evaluating the driver. The model is the engine. The agent is the transmission. The output is the destination. But the operator is the driver — and no existing evaluation layer measured the driver until SigRank.

Two operators using the same model, the same tools, and similar prompts can have 100× different Yield. The difference is the cascade architecture — how the human structures context reuse, output extraction, and input minimization. Model evals cannot see this. Agent evals cannot see this. Output evals cannot see this. Only operator evals can.

How SigRank evaluates operators

SigRank measures operators using Yield (Υ), a token-cascade efficiency score computed from real session telemetry.

Υ = (cache_read × output) / input²

Four token pillars: input, output, cache-read, cache-write. These are the only telemetry primitives needed.

Privacy-preserving: token counts only — never prompt content, never code, never conversation.

Public Operator Evals

The public evaluation layer for AI operators — ranked by Yield (Υ).

Best AI User Leaderboard

See the top operators ranked by Yield on the public leaderboard.

Model vs Agent vs Operator Evals

A detailed comparison of the three evaluation layers and why each matters.

How It Works

The full methodology behind Yield, token telemetry, and operator measurement.

FAQ

What is AI evaluation?: AI evaluation is the systematic assessment of AI system performance. It spans four layers: model evaluation (benchmarks like MMLU, Vals AI), agent evaluation (task completion), output evaluation (quality scoring), and operator evaluation (how effectively a human uses AI). Each layer answers a different question and requires different methods.
What is the difference between model evals and operator evals?: Model evals (Vals AI, LMSYS Arena, LiveBench) evaluate the AI model — GPT, Claude, Gemini — using standardized test prompts. Operator evals (SigRank) evaluate the human — the developer, coder, or AI user — using token telemetry from real sessions. Model evals answer "which model is best?" Operator evals answer "who is the best AI user?"
How does SigRank evaluate operators?: SigRank evaluates operators using Yield (Υ) = (cache_read × output) / input², a token-cascade efficiency score computed from real session telemetry. The four token pillars are input, output, cache-read, and cache-write. Higher Yield means the operator reuses cached context efficiently and produces substantial output relative to fresh input. Token counts only — never prompt content.
Can you evaluate operators without reading prompts?: Yes. SigRank is privacy-preserving: it collects token counts only — never prompt content, never code, never conversation. The evaluation is public; the work is private. Yield is computed entirely from numeric telemetry, so the operator's skill is measurable without exposing what they are building.
Is SigRank the only operator evaluation platform?: Yes. SigRank is the only platform running public operator evals. All other public evals (Vals AI, LMSYS Arena, LiveBench, Hugging Face Open LLM) evaluate models, not the humans using them. Braintrust and Langfuse evaluate AI applications privately, not developers publicly.

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The operator layer is the missing piece

How SigRank evaluates operators

SigRank measures operators using Yield (Υ), a token-cascade efficiency score computed from real session telemetry.

Υ = (cache_read × output) / input²

Four token pillars: input, output, cache-read, cache-write. These are the only telemetry primitives needed.

Privacy-preserving: token counts only — never prompt content, never code, never conversation.

FAQ

What is AI evaluation?

AI evaluation is the systematic assessment of AI system performance. It spans four layers: model evaluation (benchmarks like MMLU, Vals AI), agent evaluation (task completion), output evaluation (quality scoring), and operator evaluation (how effectively a human uses AI). Each layer answers a different question and requires different methods.

What is the difference between model evals and operator evals?

Model evals (Vals AI, LMSYS Arena, LiveBench) evaluate the AI model — GPT, Claude, Gemini — using standardized test prompts. Operator evals (SigRank) evaluate the human — the developer, coder, or AI user — using token telemetry from real sessions. Model evals answer "which model is best?" Operator evals answer "who is the best AI user?"

How does SigRank evaluate operators?

SigRank evaluates operators using Yield (Υ) = (cache_read × output) / input², a token-cascade efficiency score computed from real session telemetry. The four token pillars are input, output, cache-read, and cache-write. Higher Yield means the operator reuses cached context efficiently and produces substantial output relative to fresh input. Token counts only — never prompt content.

Can you evaluate operators without reading prompts?

Yes. SigRank is privacy-preserving: it collects token counts only — never prompt content, never code, never conversation. The evaluation is public; the work is private. Yield is computed entirely from numeric telemetry, so the operator's skill is measurable without exposing what they are building.

Is SigRank the only operator evaluation platform?

Yes. SigRank is the only platform running public operator evals. All other public evals (Vals AI, LMSYS Arena, LiveBench, Hugging Face Open LLM) evaluate models, not the humans using them. Braintrust and Langfuse evaluate AI applications privately, not developers publicly.