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Evaluation decision desk

Benchmark the system you will operate.

Turn representative work, failure evidence and operating constraints into a reviewable decision—not a leaderboard screenshot.

Design the evaluationHow AccessAllGPT evaluates evidence →
Decision path

From benchmark question to an auditable decision.

Complete the stages in order. Capability scores cannot compensate for prohibited behavior or failed security, privacy, legal and operational gates.

  1. 01

    Define the decision before the score

    Name the configured system, users, workload, acceptance boundary, prohibited outcomes and evidence required for ship, bounded trial or reject.

    Precommit the decision rule →
  2. 02

    Freeze a production-shaped suite

    Sample representative tasks and environment failures. Version prompts, tools, permissions, graders, time limits, reviewer rules and stop conditions before running candidates.

    Build the evaluation protocol →
  3. 03

    Read failures, cost and uncertainty

    Preserve trajectories, interventions, unsafe attempts, latency and cost per accepted outcome. Refuse a ranking when the evidence cannot separate candidates.

    Carry evidence into procurement →
Evidence boundary

What this desk does—and does not—prove.

This sequence is AccessAllGPT editorial guidance, not an original benchmark, an empirically validated scoring system or a vendor ranking. The linked guides identify their sources, methods and limitations.

A private evaluation improves local relevance but does not establish universal performance or eliminate rare failures. Teams must test the exact configured system, disclose changes and adapt controls to their workload and consequence level.

Read publication disclosures →
Supporting decisions

Test whether a paper result transfers

Move from reported research evidence to a minimal reproduction and a bounded test under your own constraints.

Open guide →

Evaluate coding agents on your repositories

Measure accepted patches, reviewer intervention and unsafe actions before granting broader repository authority.

Open guide →
Benchmark research

Current evaluation guides.

Methods for preserving case-level evidence, consequential failures, human intervention and uncertainty.

Benchmarks8 min

Ai2 Releases StateBench as Frontier Models Falter on Long State-Tracking Chains

Gemini 3.8 Flash, Claude Opus 5.5 and GPT-6 Sol and Luna fell to 13% accuracy or lower on the longest general-finite cases, while the open benchmark separates four kinds of state updates

Ai2 released StateBench, its code and data on November 3. The benchmark tests whether language models can preserve changing entity state across long inputs—and shows that strong frontier models still break down on the hardest update chains.

Published Read analysis →
Benchmarks8 min

Terminal X Says GPT-6.1 Sol Matched Senior Investor Reviews 88.6% of the Time

The October 20 customer study covers 500 scored investment judgments and a 54-criterion evaluator, but leaves the comparison model, sample composition and uncertainty undisclosed

OpenAI published new Terminal X customer evidence on October 20: an Eval Agent using GPT-6.1 Sol agreed with a senior reviewer on 88.6% of 500 scored judgments and reportedly caught 25% more mandate drift than an unnamed next-best model. The result is useful evaluation-design evidence, not a general finance benchmark.

Published Read analysis →
Benchmarks8 min

Microsoft and Hugging Face Bring ThinkingBox’s 507 Stateful Agent Workflows to OpenEnv

The benchmark checks terminal database state across 20 repeated runs; author-reported results show that clean tool execution often masks wrong or incomplete outcomes

Microsoft and Hugging Face announced ThinkingBox’s OpenEnv integration on October 3, 2026. The public benchmark covers 507 synthetic business workflows, grades final state and side effects, and exposes a large gap between one successful run and repeat reliability.

Published Read analysis →
Benchmarks20 min

RAG Chunk Overlap: 25% Recovered Split Evidence; 75% Tripled the Index

A reproducible retrieval test and deployment gate for choosing overlap from evidence rather than a framework default

In a deterministic boundary test, zero overlap never kept the two required facts together. Twenty-five percent fixed every case; raising overlap to 75% added no retrieval win and produced three times as many chunks.

Published Read analysis →