# =============================================================================
# CEI-ToM DMLR 2026 Configuration
# =============================================================================
# IMPORTANT: DMLR baselines must use models NOT evaluated in the CogSci 2026
# companion paper. See cogsci_off_limits below for the full exclusion list.

llm_inference:
  # Model sets by execution mode
  models:
    # TEST mode: single model for quick validation
    test:
      - id: "gpt-5-mini"
        provider: "openai"

    # COMPLETE mode: DMLR-safe models (none overlap with CogSci 2026)
    complete:
      # Commercial API models
      - id: "gpt-5-mini"
        provider: "openai"
      - id: "claude-sonnet-4-5"
        provider: "anthropic"
      - id: "grok-4-1-fast-non-reasoning"
        provider: "xai"
      - id: "gemini-2.5-flash"
        provider: "google"
      # Open-source models via API providers
      - id: "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo"
        provider: "together"
      - id: "accounts/fireworks/models/deepseek-v3p1"
        provider: "fireworks"
      - id: "Qwen/Qwen2.5-7B-Instruct-Turbo"
        provider: "together"

  # Models used in CogSci 2026 — DO NOT use for DMLR baselines
  cogsci_off_limits:
    behavioral:
      - "gpt-5-mini"
      - "claude-haiku-4-5"
      - "grok-4-1-fast"
      - "gemini-3-flash-preview"
      - "kimi-k2-instruct-0905"
      - "qwen3-235b-a22b-instruct-2507"
      - "deepseek-v3p2"
      - "minimax-m2p1"
      - "Meta-Llama-3.1-8B-Instruct-Turbo"
      - "Mistral-Small-24B-Instruct-2501"
      - "gemma-3n-E4B-it"
    probing:
      - "llama-3-8b"
      - "mistral-7b"
      - "flan-t5-xxl"
      - "mixtral-8x22b"

# =============================================================================
# PRICING (USD per 1M tokens, as of 2026-02-07)
# =============================================================================
pricing_usd_per_1m_tokens:
  # --- DMLR-safe models (recommended for baselines) ---
  openai:
    gpt-5-mini:           {input: 0.25,  output:  2.00}
    _default:             {input: 0.25,  output:  2.00}
  anthropic:
    claude-sonnet-4-5:    {input: 3.00,  output: 15.00}
    _default:             {input: 3.00,  output: 15.00}
  google:
    gemini-2.0-flash:     {input: 0.10,  output:  0.40}
    gemini-2.5-flash:     {input: 0.30,  output:  2.50}
    _default:             {input: 0.30,  output:  2.50}
  xai:
    grok-4-1-fast-non-reasoning: {input: 0.20, output: 0.50}
    _default:             {input: 0.20,  output:  0.50}
  fireworks:
    deepseek-v3p1:        {input: 0.15,  output:  0.75}
    _default:             {input: 1.00,  output:  5.00}
  together:
    meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo: {input: 0.88, output: 0.88}
    Qwen/Qwen2.5-7B-Instruct-Turbo: {input: 0.30, output: 0.30}
    _default:             {input: 0.15,  output:  0.60}
  ollama:
    _default:             {input: 0.0,   output:  0.0}

  # --- CogSci models (reference only, DO NOT use for DMLR) ---
  # openai:  gpt-5-mini            {input: 0.25,  output:  2.00}
  # anthropic: claude-haiku-4-5    {input: 1.00,  output:  5.00}
  # xai:     grok-4-1-fast         {input: 0.20,  output:  0.50}
  # google:  gemini-3-flash        {input: 0.50,  output:  3.00}
  # fireworks: kimi-k2             {input: 0.60,  output:  2.50}
  # fireworks: qwen3-235b          {input: 0.22,  output:  0.88}
  # fireworks: deepseek-v3p2       {input: 0.56,  output:  1.68}
  # fireworks: minimax-m2p1        {input: 0.30,  output:  1.20}
  # together: llama-3.1-8b         {input: 0.18,  output:  0.18}
  # together: mistral-small-24b    {input: 0.10,  output:  0.30}
  # together: gemma-3n-e4b         {input: 0.03,  output:  0.03}