audit-reasoning-options

name	audit-reasoning-options
description	Audit or write models.dev reasoning_options in provider TOML files and reasoning-option PRs. Use when verifying toggle, effort, budget_tokens, provider reasoning controls, or citations.

Audit Reasoning Options

AGENTS.md → Reasoning options is authoritative. This skill is the workflow.

Provider capability = this host’s HTTP request surface (not the npm package, SDK types, or UI).

Schema shapes

[[reasoning_options]]
type = "toggle"

[[reasoning_options]]
type = "effort"
values = ["low", "medium", "high"]

[[reasoning_options]]
type = "budget_tokens"
min = 1_024
max = 32_000

• effort values may include null, none, minimal, low, medium, high, xhigh, max, default — never dump the full enum.
• budget_tokens = reasoning tokens only, not max_tokens. Bounds only when verified.
• [] = model reasons, no caller control. Omitted = not authored (invalid once reasoning = true).

Step 1 — classify the host (role, not npm)

Kind	Definition	Options source
First-party lab	providers/<id> is the model creator (OpenAI, Anthropic, DeepSeek, Alibaba, Google, …)	That lab’s docs + existing providers/<lab>/ entries
Multi-model relay	Hosts many labs (OpenRouter, aggregators, most new “OpenAI-compatible” startups)	Lab entry for the underlying model + same-surface relay peers

Critical: npm = "@ai-sdk/openai-compatible" is used by both labs (DeepSeek, Alibaba) and relays. It does not mean “apply GPT L/M/H gateway defaults.”

• DeepSeek first-party: thinking.type + reasoning_effort high|max
• Alibaba first-party: enable_thinking + often thinking_budget; Responses API may use reasoning.effort
• A random relay of GPT-5.4: usually passthrough reasoning_effort with GPT-like levels

Never compare a native Anthropic Messages route to an OpenAI chat-completions relay as if they shared one control surface.

Step 2 — establish options

1. Resolve underlying model (base_model / lab id).
2. Read first-party providers/<lab>/models/… for that model.
3. If authoring a relay, also sample 1–2 established relays of the same model.
4. Copy the intersection that this host can actually expose:
   • Effort values from native/peers (may be high/max only, or low/medium/high, or include none/xhigh, …)
   • Toggle if native/peers have a real on/off and this host forwards it
   • Budget only if a reasoning-budget field exists on this path
5. On relays: if native/peers have caller controls, do not write [] from uncertainty.
6. On labs: match that lab; do not paste another lab’s enum.

What “baseline” means

Baseline = the effort (and toggle/budget) set used by the lab and/or same-surface peers for this model.

It is not “always low/medium/high.” That triple is only the usual GPT-style relay case.

Example	Typical options
GPT-5.4 on a relay	effort none/low/medium/high/xhigh as peers/native show
DeepSeek V4 on DeepSeek or a faithful relay	toggle + effort high/max
Qwen3.5 Plus on Alibaba	toggle + budget_tokens (chat path)
Always-on thinking model	[]

Step 3 — toggle rules

Situation	Shape
none ∈ effort and other graded levels	effort only — no toggle
Separate on/off field + graded effort (no none in effort)	toggle + effort
Binary on/off only	toggle

Toggle requires a leading top-of-file wire comment, e.g.:

# Toggle: thinking.type = enabled|disabled
# Effort: reasoning_effort = high|max

# Toggle: enable_thinking true|false
# Budget: thinking_budget

Not toggle: split model IDs; UI-only; effort=low as “off”; pairing toggle with effort that already includes none.

Step 4 — budget rules

• Reasoning-token budget only.
• Legitimate families: older Anthropic extended thinking, some Alibaba/Qwen thinking_budget, some older Gemini budgets.
• Not for GPT-5.x effort-only, Claude 4.7+ adaptive effort, DeepSeek V4, or random MoE relays without a budget API.
• Never derive min/max from limit.output or context.

Evidence bar

Claim	Bar
Effort/toggle/budget matching first-party lab entry on that lab	Lab docs or existing lab TOML
Same options on a relay	Lab + peer relays, or this host docs/test; no contradiction
Extra levels beyond lab/peers	This host docs or live meaningful effect
[]	Affirmative no control — not “I didn’t check”

Anti-patterns

• Treating every @ai-sdk/openai-compatible host as a GPT L/M/H gateway
• Forcing low/medium/high onto DeepSeek V4 (or any narrower native set)
• [] on a relay of a controlled reasoner from uncertainty
• Full schema effort enum dumps
• Bogus budget_tokens / bounds from output limits
• toggle + none inside the same effort list
• Wrong wire comments in examples or files

Audit workflow

1. Classify host: first-party lab vs multi-model relay.
2. List changed models and proposed options.
3. For each: lab entry + peers → expected shape.
4. Fix invented L/M/H, false [], dual none+toggle, bad budgets.
5. bun validate when authoring.
6. PR body: host kind, wire fields, why this option set.

PR audit output

• Host classification per provider
• Models and options; verdict per option
• Toggle wire path when present
• Whether baseline was copied from lab vs peers
• Validation result