which-llm

name	which-llm
description	Compare current LLM quality, price, speed, context, modality, and OpenRouter availability for recommendations and implementation choices.

which-llm

This skill provides a current Artificial Analysis plus OpenRouter snapshot and small Python commands for model selection data. Data readiness is internal to each command: cached data is used immediately, missing data is created automatically, and stale or undated data stops recommendations until refreshed.

Run commands from this skill directory with python, or call scripts by path with ${CLAUDE_SKILL_DIR} in Claude Code.

Capabilities

Capability	Command	Produces
Inspect ranked models under constraints	python pick.py [preset] [filters]	Ranked evidence table
Compare named models	python compare.py <model>...	Side-by-side table
Inspect one model	python profile.py <model>	Model profile
Resolve natural names	python resolve.py <model>...	Selected slugs plus alternates
Resolve endpoint names	python slug.py <model>	Provider endpoint record
Generate Pareto frontier	python frontier.py [preset] [filters]	PNG chart plus CSV data
Export filtered rows	python export.py [preset] [filters]	CSV or JSON file

query.py and plot_pareto.py remain available for compatibility, but the atomic commands above are the fastest surface for normal use.

Evidence, Not Conclusions

The commands organize current evidence. They do not choose a model for the agent or user. A default sort order is not a recommendation.

• Start with the user's stated constraints and objective.
• Present relevant tradeoffs, scope, and uncertainty before a conclusion.
• Recommend a model only when the user asks for a recommendation. State the objective that produced it.
• If the user requests a different metric or weighting, show that view without treating the earlier view as universally correct.

Cost Context

Token prices are rates. They do not establish workload or task cost without the input and output token volumes. Caching, tool calls, reasoning tokens, retries, and model behavior can materially change total spend.

• Do not use a blended token price as workload-cost evidence. A blend assumes a token mix that may have no relationship to the user's workload.
• intel-task$ and agent-task$ are benchmark-specific task-cost evidence. They are not estimates of the user's application spend.
• idx-run$ is the cost of a full benchmark run. It is not a per-call price.
• When useful, show benchmark task cost and input and output token rates side by side, with each scope labeled.
• If a user asks why token price was not used, explain the rate versus workload distinction and offer a direct rate-only comparison.
• Calculate a workload estimate only when token volume, cache behavior, tool use, and retry assumptions are available. Label every assumption.

Pick Presets

Preset	Meaning
best	Highest intelligence.
vision	Text and image capable models.
long-context	Context window at least 256K tokens.
open-weights	Open-weight models.
free	Models with OpenRouter free prototype endpoints.
coding	Ranked by Artificial Analysis coding index.

Frontier Presets

Preset	X metric	Y metric
cost-intel	Intelligence Index cost per task, minimized	Intelligence, maximized
agentic-cost	Agentic Index cost per task, minimized	Agentic Index, maximized
speed-intel	End to end latency, minimized	Intelligence, maximized
tokens-intel	Benchmark-run tokens, minimized	Intelligence, maximized
context-intel	Context window, maximized	Intelligence, maximized
input-price-intel	Input price per 1M, minimized	Intelligence, maximized
output-price-intel	Output price per 1M, minimized	Intelligence, maximized

Shared Filters

pick.py, frontier.py, and export.py share these filters:

--pattern TEXT
--creator NAME
--reasoning / --no-reasoning
--open-weights / --no-open-weights
--free
--text / --no-text
--image
--video
--audio
--modality text,image
--min-intel N
--max-run-cost N
--max-input-price N
--max-output-price N
--min-context N
--min-coding N
--max-latency N
--max-index-tokens N
--min-index-tokens N

--max-cost remains an alias for --max-run-cost, the benchmark-run cost. Use --max-input-price or --max-output-price for API price per 1M tokens. A benchmark frontier must pair each score with that same benchmark's cost per task. The commands reject known cross-benchmark cost and score pairs.

pick.py and export.py also accept --sort with intel, cost, ctx, tokens, speed, coding, agentic, input-price, or output-price. They also accept --top N. pick.py shows clearly labeled one-filter relaxations when no rows match. Use --if-empty error for strict empty-result failure. export.py accepts --if-empty nearest for the same labeled recovery without writing a relaxed data file.

compare.py resolves strictly by default. It accepts --resolve auto when selecting the strongest ambiguous match and listing alternates is acceptable.

export.py accepts --fields core, pricing, context, benchmarks, coding, slugs, or full. Field groups can be combined with commas, such as --fields pricing,context. Exact columns can be selected with --columns name,openrouter_slug,coding_index.

Useful Argument Compositions

These are ordinary command shapes built from presets, filters, and sorts. Replace N with a price ceiling in USD per million input tokens.

Behavior	Command shape
Intelligence-task cost efficiency	python pick.py best --min-intel 50 --sort cost --top 5
Fast quality shortlist	python pick.py best --min-intel 30 --sort speed --top 5
Low input-price shortlist	python pick.py best --min-intel 40 --sort input-price --top 5
Low-price image-capable shortlist	python pick.py vision --min-intel 40 --sort input-price --top 5
Price-aware coding shortlist	python pick.py coding --min-coding 45 --sort input-price --top 5
Long-context ranked by input price	python pick.py long-context --min-intel 40 --sort input-price --top 5
Long-context under input-price budget	python pick.py long-context --min-intel 40 --max-input-price N --sort input-price --top 5
Low output-price shortlist	python pick.py best --max-output-price 5 --sort output-price --top 5
Strict no-match behavior	python pick.py vision --free --min-intel 70 --if-empty error
Nearest no-match evidence	python pick.py vision --free --min-intel 70 --top 5

Export Field Groups

Field group	Contains
core	Main quality, cost, context, speed, and OpenRouter columns.
pricing	Benchmark-run cost, token use, API prices, cache price, and slugs.
context	Context window, modalities, reasoning, open weights, and slug.
coding	API prices, context, OpenRouter slugs, coding scores, and coding benchmarks.
benchmarks	Intelligence, coding, agentic, math, and benchmark scores.
slugs	Internal slug, OpenRouter production slug, and free slug.
full	All tracked columns.

Output Notes

• Every command emits a compact cost-scope reminder for the calling agent.
• Default profiles omit legacy blended-rate fields. Explicit full exports retain the source columns for inspection.
• idx-run$ is the estimated cost to run the Artificial Analysis benchmark suite. It is not a per-call API price.
• intel-task$ and agent-task$ are the matching weighted benchmark costs per task. --sort cost sorts by intel-task$.
• intelligence_index_cost_per_task_usd is the weighted Intelligence Index cost per task.
• agentic_index_cost_per_task_usd is the weighted Agentic Index cost per task.
• idx-tok is total benchmark-run token use.
• in$/1m and out$/1m are API prices per million tokens.
• openrouter_slug is the production endpoint name.
• openrouter_free_slug is a prototype option. Free endpoints can be rate-limited or served differently from paid listings.

Examples

python pick.py best --min-intel 50 --sort cost --image --top 8
python pick.py vision --min-intel 40 --sort input-price --top 5
python pick.py coding --min-coding 45 --sort input-price --top 5
python pick.py long-context --min-intel 40 --sort input-price --top 5
python compare.py gpt-5-5-medium glm-5-2
python resolve.py "gemini flash" "gpt nano"
python profile.py glm-5-2
python slug.py glm-5-2
python frontier.py cost-intel --max-x 1200 --out-dir artifacts
python frontier.py agentic-cost --image --out-dir artifacts
python export.py open-weights --fields pricing,context --format csv
python export.py open-weights --reasoning --fields coding --format csv

Do Not Use For

• Domain evals or private benchmarks that Artificial Analysis does not track.
• Models so new that Artificial Analysis has not indexed them yet.
• Authoritative non-OpenRouter provider pricing. Verify those prices with the provider.