# Artificial Analysis > Pick the right LLM or media model for a task, backed by live benchmark data from artificialanalysis.ai. Use when the user asks "which model should I use for X", "what's the best/fastest/cheapest model", "compare model A vs B", "model leaderboard", or anything about model intelligence / speed / price / context / coding-or-math ability. Also for text-to-image, image-editing, text-to-speech, text-to… - **Type:** Skill - **Install:** `agentstack add skill-mrloldev-artificial-analysis-skill-artificial-analysis-skill` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [mrloldev](https://agentstack.voostack.com/s/mrloldev) - **Installs:** 0 - **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [mrloldev](https://github.com/mrloldev) - **Source:** https://github.com/mrloldev/artificial-analysis-skill ## Install ```sh agentstack add skill-mrloldev-artificial-analysis-skill-artificial-analysis-skill ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # Artificial Analysis: pick the right model Live benchmark data from https://artificialanalysis.ai — intelligence index (10 evals), coding index, math index, output speed, time-to-first-token, pricing, and context-window, for ~400+ LLMs plus media-model ELO leaderboards. **Your job: when the user needs a model recommendation, run `recommend.sh` with the right profile — do not guess from memory.** Model rankings change weekly; never answer from training data alone. ## Prerequisites 1. `AA_API_KEY` must be exported. Free key (1,000 req/day) at https://artificialanalysis.ai/documentation. If missing, scripts prompt on TTY or fail with a clear pointer. 2. `curl` and `jq` installed. Responses cached 24h at `~/.cache/artificial-analysis/` — repeated calls are free of rate-limit cost. ## Decision flow — ALWAYS follow this for "which model" questions ``` 1. Classify the user's need → pick ONE profile (see table below). 2. Extract hard constraints: budget, min context, required provider, required quality floor. 3. Run: recommend.sh [--budget $/M] [--min-context N] [--min-intel N] [--creator X] --top 5 4. Report the top pick with its trade-offs vs. #2 and #3. 5. If the user names two or more specific models, follow with: compare.sh ... ``` Do NOT skip step 3. Benchmark numbers change; the API is the source of truth. ### Profile selection | User says / implies | Profile | Optimizes for | |---|---|---| | "best / smartest / most capable model" | `smart` | intelligence_index (tie-break: coding) | | no strong steer, general production use | `balanced` | intelligence + price + speed | | "fast / low latency / streaming / realtime" | `fast` | output t/s + low TTFT | | "cheap / bulk / high-volume / budget" | `cheap` | price (with a sane quality floor) | | "coding / code-gen / codegen agent" | `coding` | coding_index | | "math / reasoning-heavy / proofs" | `math` | math_index | | "long docs / 200k+ / large context" | `long-ctx` | context window, then intelligence | | "agent / tool loop / long-running" | `agentic` | intelligence + speed + context | When in doubt, use `balanced`. If the user has two conflicting hints (e.g. "fast and smart"), pick the dominant one and say so. ### Hard constraints → flags - Budget → `--budget 5` means ≤ $5/M tokens blended (3:1 input:output). - Min context → `--min-context 200000`. - Quality floor → `--min-intel 60` (intelligence_index is ~0–100-ish). - Specific vendor → `--creator anthropic` (matches slug or name, case-insensitive substring). ## Core scripts ### `recommend.sh` — the one you'll use most ```bash # Pick the best general model "${CLAUDE_SKILL_DIR}/scripts/recommend.sh" balanced --top 5 # Cheapest decent model for batch processing "${CLAUDE_SKILL_DIR}/scripts/recommend.sh" cheap --min-intel 55 # Fast Anthropic model for streaming UI "${CLAUDE_SKILL_DIR}/scripts/recommend.sh" fast --creator anthropic # Smart model that fits a 500k-token document "${CLAUDE_SKILL_DIR}/scripts/recommend.sh" long-ctx --min-context 500000 # Best coding model under $10/M "${CLAUDE_SKILL_DIR}/scripts/recommend.sh" coding --budget 10 ``` Output: ranked table with `score`, `slug`, `creator`, intel/code/math, t/s, TTFT, $/M, ctx. ### `models.sh` — leaderboard ```bash # Top 10 by raw intelligence "${CLAUDE_SKILL_DIR}/scripts/models.sh" --top 10 --sort-by evaluations.artificial_analysis_intelligence_index # Just the slugs (for piping into compare.sh) "${CLAUDE_SKILL_DIR}/scripts/models.sh" --top 20 --slugs # Inspect available fields on the first model "${CLAUDE_SKILL_DIR}/scripts/models.sh" --path ``` Sort keys containing `latency`, `price`, `ttft`, or `time_to_first` are treated as lower-is-better automatically. ### `compare.sh` — head-to-head ```bash "${CLAUDE_SKILL_DIR}/scripts/compare.sh" claude-4-5-sonnet gpt-5-xhigh gemini-3-1-pro-preview ``` Prints rows for creator, intel, coding, math, t/s, TTFT, blended / input / output price, and context window. ### `media.sh` — image / audio / video leaderboards ```bash "${CLAUDE_SKILL_DIR}/scripts/media.sh" image --top 15 "${CLAUDE_SKILL_DIR}/scripts/media.sh" edit --top 10 "${CLAUDE_SKILL_DIR}/scripts/media.sh" tts --top 10 "${CLAUDE_SKILL_DIR}/scripts/media.sh" ttv --categories --top 10 "${CLAUDE_SKILL_DIR}/scripts/media.sh" i2v --categories --top 10 ``` Ranked by ELO rating. ### `fetch.sh` — raw access (for custom jq) ```bash "${CLAUDE_SKILL_DIR}/scripts/fetch.sh" data/llms/models --raw \ | jq '[.data[] | select(.context_window >= 1000000)] | length' ``` ## Guidance when reporting to the user 1. **Name the top pick by slug**, link to its AA page (`https://artificialanalysis.ai/models/`). 2. **Justify with the two or three metrics that drove the choice** — e.g. "intelligence 72, $3.20/M blended, 190 t/s". 3. **Mention the runner-up** in one line with the tradeoff ("If you want lower latency, try X at $/M but intel Y"). 4. **Attribute**: say "per Artificial Analysis benchmarks" — that's the data source and their ToS expects it. 5. If slugs are unfamiliar to the user, offer `compare.sh` as a follow-up. ## Failure handling - `401` → remind user about `AA_API_KEY` and link to https://artificialanalysis.ai/documentation. - `429` → the script auto-falls-back to stale cache; warn the user it's stale. - Empty filter result → loosen a constraint (usually `--budget` or `--min-intel`). - Unknown slug in `compare.sh` → it warns on stderr; list candidates with `models.sh --slugs | grep -i `. ## Notes on authentication The Artificial Analysis API requires an API key. There is no unauthenticated public endpoint — the website renders data server-side through authenticated calls. A free key (1,000 req/day) is explicitly offered for this use case; the skill caches aggressively to stay under that limit. ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [mrloldev](https://github.com/mrloldev) - **Source:** [mrloldev/artificial-analysis-skill](https://github.com/mrloldev/artificial-analysis-skill) - **License:** MIT Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** yes - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-mrloldev-artificial-analysis-skill-artificial-analysis-skill - Seller: https://agentstack.voostack.com/s/mrloldev - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.