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Get Started Free →Pick the right LLM model for an Output SDK prompt file. Use when writing a new .prompt file, reviewing a model choice, or upgrading a stale model. Walks through priority (reasoning/balance/speed/cost), provider selection, and a live lookup against the Vercel AI Gateway model index.
.claude/skills/growthxai-output-dev-model-selection/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 76% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 113% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 180% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 129% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 328% | 0% |
This skill is the single source of truth for model selection across Output SDK skills and agents. Other skills link here instead of pinning specific model IDs, because model rosters drift faster than docs.
We run this at skill-load time to fetch the 10 most recently released models per provider from the Vercel AI Gateway:
bashoutput=$(curl -fsS https://ai-gateway.vercel.sh/v1/models 2>/dev/null | jq ' .data as $models | { anthropic: ([ $models[] | select(.id | startswith("anthropic/")) ] | sort_by(.released) | reverse | .[0:10]), openai: ([ $models[] | select(.id | startswith("openai/")) ] | sort_by(.released) | reverse | .[0:10]), google: ([ $models[] | select(.id | startswith("google/")) ] | sort_by(.released) | reverse | .[0:10]) } ' 2>/dev/null) if [ -n "$output" ]; then printf '%s\n' "$output"; else echo "(snapshot unavailable)"; fi
!output=$(curl -fsS https://ai-gateway.vercel.sh/v1/models 2>/dev/null | jq ' .data as $models | { anthropic: ([ $models[] | select(.id | startswith("anthropic/")) ] | sort_by(.released) | reverse | .[0:10]), openai: ([ $models[] | select(.id | startswith("openai/")) ] | sort_by(.released) | reverse | .[0:10]), google: ([ $models[] | select(.id | startswith("google/")) ] | sort_by(.released) | reverse | .[0:10]) } ' 2>/dev/null) if [ -n "$output" ]; then printf '%s\n' "$output"; else echo "(snapshot unavailable)"; fi
If the block above is empty, the script didn't execute automatically — likely because part of it (jq, curl, or network access) is missing. Query and filter the snapshot yourself before continuing.
jsonc{ "anthropic": [ <model>, ..., <up to 10> ], "openai": [ <model>, ..., <up to 10> ], "google": [ <model>, ..., <up to 10> ] }
Each <model> is the unmodified gateway payload. Useful fields per model:
| Field | What to use it for | |---|---| | id | The provider-prefixed ID (eg anthropic/claude-sonnet-4.6) — translate to prompt-file form (Step 5) | | released | Unix timestamp of release. Snapshot is already sorted newest-first per provider. | | name | Human-readable name | | description | One-paragraph capability summary — read this when comparing similarly-named tiers | | context_window | Max input tokens. Matters when prompts include large context (codebases, long docs) | | max_tokens | Max single-response output tokens | | tags | Capability flags. reasoning, tool-use, vision, file-input, web-search, image-generation, explicit-caching, implicit-caching | | pricing.input / pricing.output | Per-token cost (USD). Multiply by 1,000,000 for "per 1M tokens" | | pricing.input_cache_read | Cached-input price — usually 10× cheaper than input | | type | language for chat models; image models surface as image-generation and aren't valid for .prompt files |
Pick the first row that fits. If unclear, default to reasoning.
| Priority | Use when | |---|---| | reasoning (default) | Complex multi-step logic, structured output extraction, judges with edge cases, anything where wrong > slow | | balance | Most generative work — summarization, classification, content drafting, conversation | | speed | Short interactive responses, low-latency UI loops, simple transforms | | cost | Bulk batch processing where token spend dominates and quality floor is forgiving |
Scan existing *.prompt files in the workflow (and its siblings under src/workflows/) and tally what provider: they declare.
anthropic.useSearchGrounding, OpenAI's maxToolCalls) is provider-specific.Output SDK provider: values don't always line up with the snapshot keys, since Vercel groups Gemini under google/:
| Output SDK provider | Snapshot key | |---|---| | anthropic | anthropic | | openai | openai | | google-vertex (Gemini models) | google | | google-vertex (Claude models) | anthropic (then re-add the @vertex suffix manually) | | amazon-bedrock | anthropic (then translate to bedrock namespace manually) |
The list is already sorted newest-first. Walk it top-down and pick the first model whose id matches the tier for your priority.
Skip these by default:
type != "language" (eg gpt-image-2, gemini-embedding-2) — not valid for .prompt files.preview, alpha, or beta. Use stable / GA models only, even if a newer preview/alpha/beta exists. Only pick a non-stable model when the user explicitly asks for it ("use the preview", "I want the new beta", etc.).| Priority | Anthropic — match id containing | OpenAI — match id | Google — match id | |---|---|---|---| | reasoning | claude-opus- (and tags includes reasoning) | ends with -pro | contains -pro | | balance | claude-sonnet- | base gpt-N.M (no -mini/-nano/-pro suffix) | contains -pro | | speed | claude-haiku- | ends with -mini | ends with -flash (not -flash-lite) | | cost | claude-haiku- | ends with -nano | contains -flash-lite |
Tie-breakers when multiple stable models match:
claude-sonnet-4.6) over a dated snapshot (claude-sonnet-4-20250514) unless reproducibility is required (eg eval judges).context_window, then lower pricing.input.If every match in the snapshot is a preview/alpha/beta — meaning the entire tier is in pre-release — surface that to the user and ask before picking one. Don't silently use a preview because it was the only thing available.
Gateway IDs carry a provider prefix and use dots; prompt-file IDs strip the prefix and use hyphens. Apply two transformations: drop everything up to and including the first /, then replace . with -.
| Gateway id | Prompt-file model: | |---|---| | anthropic/claude-sonnet-4.6 | claude-sonnet-4-6 | | openai/gpt-5.5 | gpt-5-5 | | google/gemini-3-flash | gemini-3-flash |
Drop the translated string into your .prompt frontmatter:
yaml--- provider: anthropic model: claude-sonnet-4-6 temperature: 0.7 maxTokens: 4096 ---
output-dev-prompt-file — overall .prompt file structureoutput-dev-upgrade-prompt-models — bulk-upgrade existing prompts to the latest version of their current familyoutput-eval-judge-prompt — judge-specific selection guidance (start small, escalate on TPR/TNR failures)| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 10,521 | 4,744 | -55% | 1 | 1 | 0% | 1,717 | 3,024 | +76% | 0 | 0 | — |
case-02 | fail→pass | 7,124 | 4,447 | -38% | 1 | 1 | 0% | 1,365 | 2,907 | +113% | 0 | 0 | — |
case-03 | fail→pass | 5,908 | 4,048 | -31% | 1 | 1 | 0% | 1,001 | 2,806 | +180% | 0 | 0 | — |
case-04 | fail→pass | 7,487 | 5,385 | -28% | 1 | 1 | 0% | 1,344 | 3,078 | +129% | 0 | 0 | — |
case-05 | fail→pass | 4,524 | 5,136 | +14% | 1 | 1 | 0% | 707 | 3,024 | +328% | 0 | 0 | — |
case-06 | pass→pass | 5,696 | 4,489 | -21% | 1 | 1 | 0% | 918 | 2,782 | +203% | 0 | 0 | — |
case-07 | fail→pass | 7,175 | 5,039 | -30% | 1 | 1 | 0% | 1,132 | 3,032 | +168% | 0 | 0 | — |
case-08 | fail→pass | 6,575 | 5,156 | -22% | 1 | 1 | 0% | 1,152 | 2,980 | +159% | 0 | 0 | — |
case-09 | pass→pass | 4,929 | 6,113 | +24% | 1 | 1 | 0% | 783 | 3,129 | +300% | 0 | 0 | — |
case-10 | fail→pass | 4,755 | 4,932 | +4% | 1 | 1 | 0% | 794 | 2,996 | +277% | 0 | 0 | — |
case-11 | pass→fail | 6,684 | 2,868 | -57% | 1 | 1 | 0% | 1,131 | 2,551 | +126% | 0 | 0 | — |
case-12 | pass→pass | 10,155 | 4,680 | -54% | 1 | 1 | 0% | 1,560 | 2,841 | +82% | 0 | 0 | — |
case-13 | fail→pass | 4,258 | 2,072 | -51% | 1 | 1 | 0% | 759 | 2,447 | +222% | 0 | 0 | — |
case-14 | fail→pass | 5,055 | 2,137 | -58% | 1 | 1 | 0% | 825 | 2,431 | +195% | 0 | 0 | — |
case-15 | fail→pass | 11,183 | 6,644 | -41% | 1 | 1 | 0% | 2,022 | 3,288 | +63% | 0 | 0 | — |
case-16 | pass→pass | 6,714 | 8,652 | +29% | 1 | 1 | 0% | 1,192 | 3,646 | +206% | 0 | 0 | — |
case-17 | fail→pass | 9,601 | 1,758 | -82% | 1 | 1 | 0% | 1,388 | 2,355 | +70% | 0 | 0 | — |
case-18 | fail→pass | 10,884 | 2,804 | -74% | 1 | 1 | 0% | 1,669 | 2,556 | +53% | 0 | 0 | — |
case-19 | pass→pass | 10,057 | 2,946 | -71% | 1 | 1 | 0% | 1,533 | 2,528 | +65% | 0 | 0 | — |
case-20 | fail→fail | 12,231 | 2,346 | -81% | 1 | 1 | 0% | 1,786 | 2,461 | +38% | 0 | 0 | — |
case-21 | fail→pass | 9,616 | 2,253 | -77% | 1 | 1 | 0% | 1,489 | 2,407 | +62% | 0 | 0 | — |
case-22 | pass→pass | 5,689 | 3,113 | -45% | 1 | 1 | 0% | 883 | 2,542 | +188% | 0 | 0 | — |
case-23 | fail→fail | 10,643 | 2,273 | -79% | 1 | 1 | 0% | 1,681 | 2,398 | +43% | 0 | 0 | — |
case-24 | pass→pass | 7,143 | 5,228 | -27% | 1 | 1 | 0% | 1,176 | 2,923 | +149% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 24 cases were attempted. The headline lift of +54 percentage points is the difference between those two pass rates over the 24 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.