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METHODOLOGY

How We Benchmark AI Models

Rigorous, transparent evaluation methodology designed for enterprise decision-making. Every model tested under identical conditions.

PromptDesignModelExecuteScoreRubricReportPublishStandardized Evaluation Pipeline
01

What the Index Measures

The SlymeLab Enterprise Intelligence Index measures performance on real-world enterprise tasks. We evaluate four core dimensions:

Correctness

Factual accuracy and reasoning precision on complex problems

Instruction-following

Adherence to specific constraints, formats, and guidelines

Usefulness

Whether outputs are actionable and complete for the task context

Risk discipline

Honest uncertainty, refusal of out-of-scope requests, compliance awareness

02

Suite Composition & Versioning

Suite Weight Distribution

Financial
Legal
Technical
Long-form
Factual
Instruction

SlymeLab Enterprise Intelligence Index v1.0 (February 2026)

Our index is a weighted suite of 12 curated evaluation tasks covering:

  • 1Financial reasoning (balance sheet analysis, investment decisions)
  • 2Legal document review (contract analysis, compliance checks)
  • 3Technical problem-solving (code design, system architecture)
  • 4Long-form reasoning (multi-step logic, nuanced judgment)
  • 5Factual grounding (knowledge cutoff, hallucination resistance)
  • 6Instruction adherence (format compliance, constraint respect)

Each evaluation is versioned. This ensures reproducibility and allows tracking of model improvements over time.

03

Standardization Controls

All models are evaluated under identical conditions:

Same prompts: Identical task descriptions, no model-specific tuning.
Fixed parameters: Temperature, max_tokens, top_p configured per task type (e.g., 0.0 for reasoning, 0.7 for generation).
Token limits: Consistent maximum output lengths prevent artificial length bias.
Scoring rules: Rubrics applied uniformly across all model outputs.
Execution environment: Same hardware, same latency measurement methodology.
04

Confidence & Uncertainty

95% Confidence Intervals

Model A
94
Model B
92
Model C
88
80859095100

We report results with explicit uncertainty:

  • --Each task is run multiple times to estimate variance.
  • --Results include 95% confidence intervals (shown as +/- bands in leaderboards).
  • --Sample sizes reported per task (e.g., "n=50 samples" for legal reasoning).
  • --Uncertainty is higher for newer models or niche capabilities.

This transparency allows enterprises to distinguish signal from noise when comparing models.

05

Speed & Cost Methodology

Speed

We measure output tokens per second (tok/s) on standardized runs. This reflects real-world latency for enterprise workloads.

Cost

Price per 1M input + output tokens, sourced from public provider pricing. We normalize across different pricing tiers.

Blended

For multi-task workflows, we compute weighted cost based on typical token distributions.

06

Version History & Changelog

v1.0 -- February 2026

Initial public release. 12-task enterprise suite. Claude Opus 4.6, GPT-5.3, Gemini 3.1, Grok 4.20 as baseline.

v1.1 -- March 2026 (planned)

Add multimodal reasoning task. Expand legal corpus. Add safety evals.

Rolling -- Ongoing

New models added monthly. Prices updated as announced.

07

Limitations & Caveats

  • --Benchmark saturation: Public benchmarks can become "solved" by newer models, reducing discriminative power.
  • --Task specificity: Our suite reflects enterprise workflows but may not generalize to all use cases.
  • --Pricing lag: Published prices may differ from negotiated enterprise rates.
  • --Model updates: Models change (e.g., fine-tuning, new versions). We track major releases but cannot capture all variants.
  • --Hallucination variance: Particularly sensitive to prompt phrasing. Our rubrics are conservative but not exhaustive.
08

How to Use These Results

For Enterprise Decision-Makers

  • --Compare 2-3 top models under your specific use case before committing.
  • --Factor in cost, latency, and governance in addition to raw intelligence scores.
  • --Confidence bands matter -- models with overlapping CIs are not meaningfully different.

For AI Teams & Researchers

  • --Use this index as a reference, not a benchmark for model training.
  • --Implement your own task-specific evals on top of these baseline comparisons.
  • --Report your methodology explicitly if you diverge from SlymeLab methodology.

Questions or feedback?

Have questions about our methodology? Want to suggest a task or model? Reach out to our research team.

Contact us