multi-tool-model-comparison

name	multi-tool-model-comparison
description	Compare model options across GitHub Copilot, OpenAI Codex, and xAI Grok for a single task when the developer has not committed to one ecosystem. Produces a structured side-by-side that surfaces availability, tier mapping, and key tradeoffs without naming specific volatile numbers.

Skill: Multi-Tool Model Comparison

When a developer is evaluating AI coding tools and hasn't committed to one ecosystem, a comparison across Copilot/Codex/Grok is more useful than a deep dive into one. This skill structures that comparison so the result is methodology-grounded and resistant to lineup churn.

Step 0 — Establish the task shape first

The comparison is only as useful as the task description is precise. Before comparing, pin:

1. Use-case type — inline completion, supervised chat, or autonomous multi-step run
2. IDE surface — VS Code, JetBrains, Neovim, terminal, or CI/cloud
3. Team / org context — individual developer, small team, enterprise org with policy controls
4. Budget sensitivity — tight per-token ceiling or best-quality-regardless-of-cost

These four pins determine which models on each platform are even in scope. Skip the comparison until they are established.

Step 1 — Map the task to a vendor-neutral tier

Use the vendor-neutral task-shape tree (see ../../knowledge/ai-coding-decision-trees.md) to identify the target tier before touching any vendor lineup:

Tier	Inline/latency	Supervised chat	Autonomous/long
Fast inline	First choice	Not applicable	Not applicable
Balanced default	Fallback	First choice	First choice for routine
Frontier	Not applicable	Hard-reasoning tasks	Always start here

Step 2 — Map the tier to each ecosystem's current offering

For each of the three ecosystems, consult ../../knowledge/cross-tool-model-lineup-2026.md and fill in this table. Do not name a model without checking the lineup entry — mark every cell [verify-at-use] because picker contents churn:

Dimension	GitHub Copilot	OpenAI Codex	xAI Grok
Fast inline	[verify-at-use]	[verify-at-use]	[verify-at-use]
Balanced default	[verify-at-use]	[verify-at-use]	[verify-at-use]
Frontier	[verify-at-use]	[verify-at-use]	[verify-at-use]
Reasoning dial	No	Yes (Codex-specific)	No
Surface lock-in	IDE / GitHub.com	CLI + cloud	API
Org policy controls	Enterprise/Business	Org-level	API key only

Step 3 — Surface the differentiating axes

After filling the table, call out the axes that actually differentiate the three for this task:

• Reasoning lever — only Codex exposes an explicit reasoning-level dial; if the task is quality-limited (not cost-limited), this is often the deciding factor.
• Surface coupling — Copilot is tightly coupled to IDE + GitHub; Codex is terminal/API-first; Grok is API-first. If the developer lives in VS Code, Copilot's surface friction is zero.
• Org policy — enterprise teams using GitHub Enterprise Cloud can enforce model rules in Copilot; no equivalent in Codex or Grok at the org level.
• Context window — all three platforms have models with large windows, but the limits churn; verify at use and flag the task's actual context demand (see ../context-window-planning/SKILL.md).

Step 4 — Produce the recommendation

State a primary recommendation and a fallback — not a ranked list of seven options. The developer needs a decision, not a comparison table that passes the decision back.

Format:

Primary: [ecosystem] — [tier] tier — because [1-2 reasons from Step 3]
Fallback: [ecosystem] — [tier] tier — if [specific condition]
Verify before use: [any specific SKU mentioned] against the knowledge bank [retrieval date]

Pitfalls

• Starting the comparison before the task shape is pinned — produces a generic answer the developer already knew.
• Comparing model names rather than tiers — names change; tiers are stable.
• Treating absence from the verified lineup as proof of absence — offer to research rather than confirming a negative.
• Letting surface preference masquerade as model quality: "Copilot is better" when the real answer is "Copilot's IDE coupling is better for your use case."

See also

• ../../agents/codex-model-strategist.md — deep Codex reasoning-level logic
• ../../agents/copilot-model-strategist.md — Copilot surface/plan scoping
• ../../agents/grok-model-strategist.md — Grok lineup and retirements
• ../../knowledge/ai-coding-decision-trees.md — vendor-neutral tier tree