GPT-5.6 Field Guide

An evidence-driven guide for choosing the right GPT-5.6 model and reasoning level.

Comment

Inspiration

GPT-5.6 arrives with a rich set of model tiers, reasoning levels, execution modes, benchmark scores, estimated costs, latency measurements, and token counts. That evidence is valuable, but it is published in the language of evaluations. People make decisions in the language of work: “I need to debug this repository,” “I need to find an obscure fact,” or “I need to complete an important professional task.”

The project began with a simple product question: what if benchmark evidence behaved like a field guide instead of a leaderboard?

What it does

GPT-5.6 Field Guide turns published benchmark evidence into a task-first decision experience. A user chooses the kind of work, selects a recommendation tendency, and expresses whether money or waiting matters more. The guide then proposes one model and reasoning configuration, explains why, and shows quantified cheaper and stronger alternatives.

Below that fast path, the Capability Atlas reorganizes evidence around meanings such as building and operating software, finding hard-to-reach facts, completing professional work, and automating with tools. Each benchmark introduction states the capability it represents, the real tasks it resembles, what it does not measure, how to read its score, and which benchmark supplies the evidence.

Interactive SVG charts expose estimated cost, latency, output tokens, and a clearly labeled project-specific composite resource cost. Recommended points, Pareto-efficient points, dominated points, provenance, missing fields, source conflicts, formulas, and limitations remain visible. Artificial Analysis Intelligence Index v4.1 provides a separate general-capability overview, while score-only records remain in their own evidence section so unavailable resource values are never guessed.

Every recommendation and synthesis is labeled as project interpretation, not official OpenAI advice.

How we built it

The product was created in a primary Codex build thread powered by GPT-5.6. Codex first audited three pre-existing JSON snapshots, their schema versions, record counts, missing-value patterns, provenance fields, and source conflicts. The human creator then set the core product thesis: capabilities before benchmark brands, task choice only where it is meaningful, transparent uncertainty, and an editorial field-guide identity rather than a dashboard shell.

Codex and GPT-5.6 translated that direction into an original, framework-free implementation: one self-contained index.html with inline CSS, JavaScript, SVG, and embedded derivative data. The build added shared preference state, recommendation utilities, within-benchmark normalization, capability synthesis, Pareto analysis, keyboard-selectable chart points, responsive layouts, two themes, and offline behavior. Playwright-driven browser checks exercised desktop, mobile, keyboard, controls, themes, every rendered benchmark, console output, and local network activity.

The final site does not call an AI model or external API at runtime. GPT-5.6 and Codex were the build-time reasoning and implementation environment.

Challenges we ran into

The hardest problem was epistemic, not visual: dense benchmark evidence can look more certain than it is. Some records have complete resource curves; others publish only scores. Some table values conflict with chart-derived values. Multi-agent and single-agent configurations are not interchangeable. Raw scores from unlike benchmarks cannot be averaged honestly.

The solution was to make those boundaries part of the interface. Missing stays missing. Score-only evidence cannot drive cost or speed claims. Conflicts are preserved and described. Synthesis normalizes only within each benchmark, reports coverage, and labels confidence as an evidence-breadth signal rather than a statistical interval.

A second challenge was connecting a fast recommendation experience with a deep evidence atlas without creating a generic four-workspace application. The final page uses one global resource-preference state, but task choice remains local to Quick Decision. Detailed charts can show suggested evidence or independent exploration without pretending every everyday task has a specialist benchmark.

Accomplishments that we are proud of

  • A substantial editorial product delivered as one portable HTML file with no framework or runtime dependency.
  • 39 benchmark definitions across the source files and 278 source points represented across general, resource-complete, and score-only evidence roles.
  • 196 points with complete cost, latency, and output-token data used without inventing values for the remaining records.
  • Capability-first explanations that foreground real task meaning and measurement limits.
  • Linked recommendations, alternatives, metrics, chart states, and plain-language explanations.
  • Strong responsive, keyboard, theme, reduced-motion, offline, privacy, and console validation.
  • A clear public record separating pre-existing data from original Build Week work.

What we learned

The most useful benchmark interface is not the one with the most numbers. It is the one that helps a person understand what a number can support, what it cannot support, and what to test next.

We also learned that a single-file constraint can improve product discipline. Without a framework or component library, every interaction and visual motif had to justify itself. Codex made that constraint practical by accelerating schema inspection, implementation, regression testing, and iterative design while the human creator retained responsibility for product judgment and evidence policy.

What is next

The public narrated demo and primary Codex /feedback evidence are complete. The immediate next step is the Devpost submission, which has not yet been claimed as submitted. After Build Week, the project could add user-supplied evaluation results, exportable comparison notes, and versioned benchmark snapshots. Any expansion should preserve the current rules: no hidden averaging, no guessed resources, no universal winner, and a visible boundary between source evidence and project interpretation.

Built With

Share this project:

Updates