AI Common Sense

Use when user mentions model names, versions, pricing, API IDs, "which model should I use", "what's the latest model", "model comparison", "API pricing", "wh...

Security Scan

The skill is largely coherent and appears to do what it promises (a verified model/pricing reference and self‑verification guidance), but the runtime instructions include example commands that reference local API keys and shell actions while the skill declares no required credentials — this mismatch could let an agent unexpectedly access local secrets if allowed to run Bash/Read.

Purpose & Capability

Name/description match the contents: the files are a curated reference of model names, IDs, pricing, deprecations and verification guidance. No unexpected cloud or system-level capabilities are requested.

Instruction Scope

SKILL.md instructs agents to use WebSearch/WebFetch to verify stale entries and provides concrete verification commands (curl, npm/pip checks). That is in-scope for verifying model IDs and pricing. However the instructions also include shell examples that use environment-variable placeholders (e.g., $OPENAI_API_KEY) and allow tools including Bash and Read, which expands the agent's ability to run commands or access files if enabled — the guidance itself does not explicitly limit those actions.

Install Mechanism

Instruction-only skill with no install spec and no code files. No archives or downloads, so nothing is written to disk by an installer — low install risk.

Credentials

The skill declares no required environment variables or credentials, yet the documentation and curl/sdk examples reference many provider API key environment variables (OPENAI_API_KEY, ANTHROPIC_API_KEY, MISTRAL_API_KEY, COHERE_API_KEY, GOOGLE_API_KEY, etc.). This is an incoherence: either the skill should declare and justify required secrets or the instructions should avoid examples that could cause an agent to read local secrets. If the agent is permitted to run Bash/Read, those example commands could lead to use or exposure of local API keys.

Persistence & Privilege

always:false and no install hooks; skill is user-invocable only and does not request persistent/system-wide configuration or modifications. Autonomous invocation is allowed by default but is not combined with other broad privileges here.

What to consider before installing

This skill appears to be a useful, instruction-only reference for model IDs and pricing and includes sensible advice to verify stale data via web search. Before enabling it: 1) Be cautious about permitting Bash/Read for the agent — if you allow those tools the agent could run the curl/npm commands shown and may read environment variables or files containing API keys. 2) If you plan to let the skill verify provider APIs, only supply explicit API credentials you trust and expect the skill to use; otherwise avoid giving any provider keys. 3) Prefer letting the skill use WebSearch/WebFetch to check public docs (no credentials required) rather than running authenticated API calls. 4) If you need a stricter guarantee, ask the skill author to remove embedded curl examples that reference $ENV secrets or to declare required env vars explicitly. Overall: coherent and probably benign in intent, but the mismatch between examples that use API keys and the declared lack of required credentials is a real risk if the agent is allowed to execute shell commands or read environment variables.

AI Common Sense: Stop Hallucinating Model Names

LLMs frequently hallucinate model names, versions, pricing, and API identifiers because their training data has a cutoff date. This skill provides a verified quick reference and teaches AI agents how to self-verify when the reference may be stale.

Why This Exists

What LLMs Commonly Get Wrong	Example
Outdated flagship models	Saying "GPT-4o" when GPT-5.4 is current
Deprecated model IDs	Using claude-3-5-sonnet-20241022 (deprecated Jan 2026)
Wrong pricing	Quoting old rates that changed months ago
Phantom models	Referencing "GPT-4-turbo" or "Gemini Ultra" (deprecated/renamed)
Wrong API format	Using Authorization: Bearer for Anthropic (should be x-api-key)
Stale deprecation status	Not knowing DALL-E 3 is shutting down

Quick Reference (Last verified: 2026-04-12)

Current Flagship Models

Provider	Flagship	API ID	Input $/MTok	Output $/MTok	Released
OpenAI	GPT-5.4	gpt-5.4	$2.50	$15.00	2026-03-17
OpenAI	GPT-5.4 Mini	gpt-5.4-mini	$0.75	$4.50	2026-03-17
OpenAI	GPT-5.4 Nano	gpt-5.4-nano	$0.20	$1.25	2026-03-17
OpenAI	o3 (reasoning)	o3	$2.00	$8.00	2025-04
Anthropic	Claude Opus 4.6	claude-opus-4-6	$5.00	$25.00	2026-02-05
Anthropic	Claude Sonnet 4.6	claude-sonnet-4-6	$3.00	$15.00	2026-02-17
Anthropic	Claude Haiku 4.5	claude-haiku-4-5-20251001	$1.00	$5.00	2025-10
Google	Gemini 3.1 Pro	gemini-3-1-pro-latest	$2.00	$12.00	2026-02-19
Google	Gemini 3 Flash	gemini-3-flash-latest	$0.50	$3.00	2026-03
Google	Gemini 2.5 Flash-Lite	gemini-2.5-flash-lite	$0.10	$0.40	2025
Meta	Llama 4 Maverick	meta-llama/llama-4-maverick	varies	varies	2026-04-05
Mistral	Small 4	mistral-small-latest	$0.15	$0.60	2026-03-16
Mistral	Large 3	mistral-large-latest	$2.00	$6.00	2025-12
DeepSeek	V3	deepseek-chat	$0.32	$0.89	2025
DeepSeek	R1 (reasoning)	deepseek-reasoner	—	—	2025
Cohere	Command A Reasoning	command-a-reasoning	~$6.25	—	2026

Recently Deprecated (Do NOT Use)

Model	Provider	Deprecated	Replacement
GPT-4o	OpenAI	2026-02-13	GPT-5.4
GPT-4.1	OpenAI	2026-02-16	GPT-5.4
DALL-E 2 & 3	OpenAI	2026-05-12	gpt-image-1.5
Claude 3.5 Sonnet	Anthropic	2026-01-05	Sonnet 4.6
Claude Haiku 3.5	Anthropic	2026-02-19	Haiku 4.5
Claude Haiku 3	Anthropic	2026-04-20	Haiku 4.5

Upcoming / Announced (NOT Yet Available)

Model	Provider	Expected	Notes
GPT-5.5 "Spud"	OpenAI	2026 Q2	Pretraining done 2026-03-24.
Claude Mythos	Anthropic	Preview only	Invitation-only. Cybersecurity.
DeepSeek V4	DeepSeek	2026	~1T params, 1M context.
Grok 5	xAI	2026 Q2	~6T params.
Muse Spark	Meta	2026-04	Meta's first proprietary/closed-source model.

How to Use This Reference

For AI Agents

1. Before generating code with model IDs: Check this reference for the correct API ID.
2. Before quoting pricing: Check this reference. Pricing changes frequently.
3. Staleness check: If the "Last verified" date is older than 30 days, use WebSearch to verify before trusting these numbers.
4. When uncertain: Say "based on my last verified reference from [date]" rather than stating as current fact.

Staleness Detection

IF (today - last_verified_date) > 30 days:
  → WebSearch "[provider] models pricing [current year]"
  → Cross-reference with this table
  → Note any discrepancies
  → Flag to user: "My reference is [N] days old, I verified via web search"

When NOT to Trust This Reference

• Model pricing can change without notice
• New models may launch between updates
• Deprecation dates may shift
• "Upcoming" models may be delayed or cancelled

Verification Commands

When you need to verify current model information, use these tools:

Web search queries (use WebSearch tool):

• OpenAI models: site:platform.openai.com models
• Anthropic models: site:docs.anthropic.com models
• Google Gemini: site:ai.google.dev models
• Pricing (any provider): [provider] API pricing [current year]
• Specific model ID: "[exact-model-id]" API
• Deprecation status: [provider] model deprecation [current year]

SDK Version Check

# OpenAI
npm info openai version
pip show openai | grep Version

# Anthropic
npm info @anthropic-ai/sdk version
pip show anthropic | grep Version

# Google
npm info @google/generative-ai version
pip show google-generativeai | grep Version

Cost Comparison (Budget → Premium)

Sorted by input cost per million tokens:

Rank	Model	Provider	Input $/MTok	Best For
1	Gemini 2.5 Flash-Lite	Google	$0.10	Cheapest multimodal
2	Mistral Small 4	Mistral	$0.15	Cheap + reasoning + vision
3	GPT-5.4 Nano	OpenAI	$0.20	Classification, extraction
4	DeepSeek V3	DeepSeek	$0.32	Coding, long context
5	Gemini 3 Flash	Google	$0.50	Balanced Google option
6	GPT-5.4 Mini	OpenAI	$0.75	OpenAI balanced
7	Claude Haiku 4.5	Anthropic	$1.00	Fast Anthropic option
8	Gemini 2.5 Pro	Google	$1.25	Advanced Google
9	Gemini 3.1 Pro	Google	$2.00	Frontier reasoning
10	Mistral Large 3	Mistral	$2.00	675B MoE
11	o3	OpenAI	$2.00	Complex reasoning
12	GPT-5.4	OpenAI	$2.50	OpenAI flagship
13	Claude Sonnet 4.6	Anthropic	$3.00	Anthropic balanced
14	Claude Opus 4.6	Anthropic	$5.00	Most capable coding/agents

Architecture Quick Facts

Architecture	Models Using It	Why It Matters
MoE (Mixture of Experts)	Mistral Large 3 (675B/41B), DeepSeek V3 (671B/37B), Llama 4 Maverick (17B/128 experts)	Massive total params but only a fraction active per token → cheaper inference.
Dense Transformer	GPT-5.4, Claude Opus 4.6, Gemini 3.1 Pro	All params active. Higher per-token cost but potentially more consistent.

Common Discount Mechanisms

Mechanism	Discount	Available On
Prompt Caching	75-90% on cached input	OpenAI, Anthropic, Google
Batch API	50% on all tokens	OpenAI, Anthropic, Google
Committed Use	Varies	Enterprise agreements

Per-Provider Deep Dives

For detailed model specs, deprecation timelines, cross-platform IDs, and API quick-start examples, see the references/ directory in the GitHub repo:

• references/openai.md — Full OpenAI model catalog + audio/image models
• references/anthropic.md — Cross-platform IDs (Bedrock, Vertex) + cache pricing
• references/google.md — Gemini 3.x + 2.5 + specialized models
• references/meta.md — Llama 4 MoE details + access methods
• references/mistral.md — Full specialist model catalog (Devstral, Voxtral, OCR)
• references/deepseek.md — V3 MoE details + V4 roadmap
• references/xai.md — Grok versions + corporate context
• references/cohere.md — Command A + open-source models (Transcribe, Tiny Aya)

How to Update This Reference

This reference gets stale. Here's how to help:

1. Found an error? Open an Issue on GitHub with the correction and source URL.
2. New model released? Submit a PR updating the relevant references/*.md file.
3. Pricing changed? Submit a PR with the new price and a link to the official pricing page.

Every update must include:

• The source URL (official docs preferred)
• The date you verified the information
• What changed and why

Tips

• The more confident an LLM sounds about a model name, the more likely it's hallucinating from training data.
• "I'm not sure which model is current — let me check" is always better than a confident wrong answer.
• Model IDs are exact strings. gpt-5.4 works; GPT-5.4 or gpt5.4 may not.
• Always test API calls with the actual model ID before deploying.