Model Selection

Hosted CLI providers (Gemini, Codex, Claude, Antigravity) auto-select a sensible default model with automatic fallback on provider-specific conditions — quota/rate-limit exhaustion, and for Antigravity also a rejected shipped model slug (see the table below). Grok and Ollama never substitute another model: Grok sends the selected API or Grok CLI catalog ID unchanged, while Ollama uses exactly the model you pull. Most users should never override the model parameter; the defaults are tuned for quality.

Defaults & Fallbacks

When the default model hits quota, the executor retries once on the fallback and reports the actual model used in the response.

Provider	Default	Fallback	Trigger
Gemini	gemini-3.1-pro-preview	gemini-3.8-flash	RESOURCE_EXHAUSTED quota error or "exhausted your capacity" pattern
Codex	gpt-5.6-sol	gpt-5.6-terra	Quota errors (rate_limit_exceeded, 429, insufficient_quota)
Claude	opus	sonnet	Claude Code native fallback when Opus is overloaded or unavailable
Grok	grok-4.6 (reasoning.effort=high)	none	Every error is terminal; requested ID is sent unchanged
Antigravity	gemini-3.1-pro (--effort high)	gemini-3.5-flash; one model-less retry when agy rejects a model whose value equals gemini-3.1-pro or gemini-3.5-flash (reported as agy default). Both retain the effective effort (high, or the ASK_ANTIGRAVITY_EFFORT override)	Subscription rate limit; model-unavailable (shipped slug values only — other rejected models fail actionably)
Ollama	qwen3.8:27b	none	Local, no fallback; a missing model returns a clear ollama pull error

For Gemini, Codex, Claude, and Antigravity, fallback is automatic and structured output exposes the actual model plus usage.fellBack. Antigravity reports gemini-3.5-flash after a rate-limit fallback, or the literal agy default after a model-less recovery (agy does not reveal which model it picked). Grok and Ollama never fall back, so their fellBack values are always false.

Codex uses medium reasoning effort for ordinary calls to preserve the previous default behavior. The quality-first /codex-review and /brainstorm skills use high. Direct ask-codex calls can override this with reasoningEffort (low, medium, high, xhigh, max, or ultra). ultra is opt-in only: Codex's catalog describes it as maximum reasoning with automatic task delegation, so review and brainstorm defaults stay at high.

Choosing a Provider

Different providers excel at different things. Pick by what you're doing, not by which is "best":

Task	Suggested provider	Why
Targeted code reasoning, refactor critique	Codex	GPT-5.6 Sol is the flagship agentic coding model; Terra keeps the fallback balanced
Claude second opinion while working in Codex	Claude	Opus review through Claude Code CLI, with native session continuation and read-only file access
Grok 4.6 independent API critique	Grok	Exact model selection, configurable reasoning depth, structured JSON Schema output
Private / air-gapped analysis	Ollama	Runs locally, nothing leaves your machine
Subscription-backed second opinion, larger context	Antigravity	agy via your Google AI Pro/Ultra plan, the Gemini CLI successor
Whole-codebase review (enterprise seats)	Gemini	1M+ token context fits what others can't (enterprise-gated from 2026-06-18)
"What do they all think?" comparison	Multi-LLM (multi-llm tool or /compare skill)	Parallel dispatch, see all responses side-by-side
Code review with verified findings	/multi-review skill	Antigravity + Codex in parallel, then verifies each finding against source

Overriding the Model

Pass model explicitly when you have a reason to:

Use ask-llm with provider gemini and model gemini-3.8-flash to quickly check this CSS file

Or programmatically:

{ "name": "ask-llm", "arguments": { "provider": "gemini", "model": "gemini-3.8-flash", "prompt": "..." } }

For Grok, choose the harness first. Discover API IDs with authenticated GET https://api.x.ai/v1/models, or CLI IDs with grok models, then pass the desired ID unchanged. Reasoning depth is a separate reasoningEffort parameter (low, medium, high, xhigh), not a model suffix. There is no harness or model fallback. Cursor Agent is separate and model-neutral: ask-cursor-agent requires a canonical provider family (claude, codex, gemini, grok) plus an exact account ID from agent --list-models (for example a Cursor Grok ID may differ from xAI's API ID); the ID must belong to that family, and Cursor Auto or other noncanonical IDs are refused.

For Codex, common overrides:

Use ask-codex with model gpt-5.6-terra to summarize this commit

For Ollama, you can request any model you've pulled:

ollama pull deepseek-coder:6.7b

Use ask-ollama with model deepseek-coder:6.7b to review this implementation

For Antigravity, ask-antigravity requires agy ≥1.1.5 and has no per-call model argument — configure it via env vars:

• ASK_ANTIGRAVITY_MODEL — an agy model slug from agy models (e.g. claude-sonnet-4-6). Legacy effort-carrying display strings like Gemini 3.1 Pro (High) still resolve for backward compatibility.
• ASK_ANTIGRAVITY_EFFORT — reasoning effort, low | medium | high (default high). Invalid values log a warning and fall back to the default behavior. Note agy limits tiers per model (gemini-3.1-pro has no medium), and effort-carrying model names reject --effort entirely — so the default effort is only sent with the shipped base slugs and model-less recovery runs, while an explicitly set value is always sent.

Token Limits & Cost

Provider	Context window	Cost model
Gemini Pro	~1M tokens (~250k LOC)	Gemini Code Assist Standard/Enterprise seat (from 2026-06-18)
Gemini Flash	~1M tokens	Cheaper than Pro; fallback target for quota relief
Codex GPT-5.6 Sol	Per OpenAI's published context window	Per OpenAI billing
Grok 4.6	500k tokens on xAI API	API is metered with long-context rates at 200k; Grok CLI follows its authenticated plan
Cursor Agent harness	Per selected catalog model	Included usage/on-demand spend follows the user's Cursor plan; Ask LLM never changes spend settings
Codex GPT-5.6 Terra	Per OpenAI's published context window	Balanced fallback target
Ollama	Per model (e.g., 256k for qwen3.8)	Free, runs locally

Track What You're Spending

Token usage is exposed live via:

• Per-call: result.structuredContent.usage on ask-* tool responses (provider, model, inputTokens, outputTokens, cachedTokens, thinkingTokens, durationMs, fellBack). Antigravity reads these values from agy's JSON envelope; cached tokens are unavailable on agy 1.1.5.
• Per-session aggregate: call the get-usage-stats MCP tool, or read the usage://current-session MCP Resource for a JSON snapshot
• In the REPL: type /usage for a markdown-formatted breakdown

This is in-memory only; no persistence to disk, resets when the MCP server restarts.

Recommendations by Use Case

• General code review → defaults are correct; let the fallback chain handle quota
• Whole-codebase analysis → ask-gemini (Pro) if you have an enterprise seat, otherwise ask-antigravity for large-context reads without per-token billing
• Quick fixes, fast iteration → request Flash or gpt-5.6-terra explicitly to skip the flagship→fallback round-trip
• Grok consultation → ask-grok; confirm xAI pricing and data transfer first, then use exact model IDs with no fallback
• Privacy-sensitive code → ask-ollama, never leaves your machine
• Multi-perspective debate → multi-llm or /brainstorm skill; Claude weighs verified vs inferred