Recommended Configurations

Recommended ladders, matching the dashboard's Auto Router templates. Every tier must exist as a model_name in the same file; swap the provider prefix or credentials for your own deployments and the router entry stays the same.

Ladder	SIMPLE	MEDIUM	COMPLEX	REASONING	Classifier
1M Context	gpt-5.6-luna	gpt-5.6-terra	gpt-5.6-sol	claude-opus-5, high effort	heuristic v2
Anthropic Family	claude-haiku-4-5	claude-sonnet-5	claude-opus-5	claude-opus-5, high effort	heuristic
OpenAI Family	gpt-5.6-luna	gpt-5.6-terra	gpt-5.6-sol	gpt-5.6-sol, xhigh effort	heuristic
Gemini Family	gemini-2.5-flash-lite	gemini-3.1-flash-lite	gemini-3.7-flash	gemini-3.1-pro-preview	heuristic
Lite	deepseek-v4-flash	muse-spark-1.2, xhigh	kimi-k3, max	claude-opus-5	LLM, agentic rubric
Benchmark config	claude-haiku-4-5	claude-sonnet-5	claude-opus-5	claude-opus-5	LLM, gpt-5.4-mini
Production config	claude-haiku-4-5	claude-haiku-4-5	claude-sonnet-5	claude-opus-5	heuristic

Choosing a ladder

One family when clients depend on provider-specific behavior (Anthropic cache control, OpenAI reasoning params). Every tier stays on one API surface.
Lite when cost beats provider consistency and traffic is agentic. Mixed providers, LLM classifier with the agentic rubric.
1M Context for long prompts. Luna, Terra, Sol, then Opus 5 at high effort, with the heuristic v2 classifier.
Same model, more effort for the top rung. Costs more output tokens, not a higher per-token rate. Pattern: effort ladders.
All ladders leave session_affinity off (the default; see prompt caching) and set escalation_keywords: ["LITELLM ESCALATE"], which bumps a request one tier when the exact phrase appears. Matching is case-sensitive.

Anthropic Family

Haiku, Sonnet, Opus, then Opus at high reasoning effort.

config.yaml
model_list:
  - model_name: claude-haiku-4-5
    litellm_params:
      model: anthropic/claude-haiku-4-5
      api_key: os.environ/ANTHROPIC_API_KEY
  - model_name: claude-sonnet-5
    litellm_params:
      model: anthropic/claude-sonnet-5
      api_key: os.environ/ANTHROPIC_API_KEY
  - model_name: claude-opus-5
    litellm_params:
      model: anthropic/claude-opus-5
      api_key: os.environ/ANTHROPIC_API_KEY
  - model_name: claude-opus-5-high
    litellm_params:
      model: anthropic/claude-opus-5
      api_key: os.environ/ANTHROPIC_API_KEY
      reasoning_effort: high

  - model_name: claude-auto
    litellm_params:
      model: auto_router/complexity_router
      complexity_router_config:
        tiers:
          SIMPLE:    claude-haiku-4-5
          MEDIUM:    claude-sonnet-5
          COMPLEX:   claude-opus-5
          REASONING: claude-opus-5-high
        classifier_type: heuristic
        escalation_keywords: ["LITELLM ESCALATE"]
        session_affinity: false
      complexity_router_default_model: claude-sonnet-5

Keep claude in the router name if Claude Code or Claude Desktop needs to discover it. See Admin Setup.

OpenAI Family

Luna, Terra, Sol, then Sol at xhigh reasoning effort.

config.yaml
model_list:
  - model_name: gpt-5.6-luna
    litellm_params:
      model: openai/gpt-5.6-luna
      api_key: os.environ/OPENAI_API_KEY
  - model_name: gpt-5.6-terra
    litellm_params:
      model: openai/gpt-5.6-terra
      api_key: os.environ/OPENAI_API_KEY
  - model_name: gpt-5.6-sol
    litellm_params:
      model: openai/gpt-5.6-sol
      api_key: os.environ/OPENAI_API_KEY
  - model_name: gpt-5.6-sol-xhigh
    litellm_params:
      model: openai/gpt-5.6-sol
      api_key: os.environ/OPENAI_API_KEY
      reasoning_effort: xhigh

  - model_name: gpt-auto
    litellm_params:
      model: auto_router/complexity_router
      complexity_router_config:
        tiers:
          SIMPLE:    gpt-5.6-luna
          MEDIUM:    gpt-5.6-terra
          COMPLEX:   gpt-5.6-sol
          REASONING: gpt-5.6-sol-xhigh
        classifier_type: heuristic
        escalation_keywords: ["LITELLM ESCALATE"]
        session_affinity: false
      complexity_router_default_model: gpt-5.6-terra

Gemini Family

Flash Lite 2.5, Flash Lite 3.1, Flash 3.7, then Pro 3.1.

config.yaml
model_list:
  - model_name: gemini-2.5-flash-lite
    litellm_params:
      model: gemini/gemini-2.5-flash-lite
      api_key: os.environ/GEMINI_API_KEY
  - model_name: gemini-3.1-flash-lite
    litellm_params:
      model: gemini/gemini-3.1-flash-lite
      api_key: os.environ/GEMINI_API_KEY
  - model_name: gemini-3.7-flash
    litellm_params:
      model: gemini/gemini-3.7-flash
      api_key: os.environ/GEMINI_API_KEY
  - model_name: gemini-3.1-pro-preview
    litellm_params:
      model: gemini/gemini-3.1-pro-preview
      api_key: os.environ/GEMINI_API_KEY

  - model_name: gemini-auto
    litellm_params:
      model: auto_router/complexity_router
      complexity_router_config:
        tiers:
          SIMPLE:    gemini-2.5-flash-lite
          MEDIUM:    gemini-3.1-flash-lite
          COMPLEX:   gemini-3.7-flash
          REASONING: gemini-3.1-pro-preview
        classifier_type: heuristic
        escalation_keywords: ["LITELLM ESCALATE"]
        session_affinity: false
      complexity_router_default_model: gemini-3.1-flash-lite

Lite

Cross-provider, built for cost. DeepSeek V4 Flash also serves as the LLM classifier, with the agentic rubric and a zero-turn context window.

config.yaml
model_list:
  - model_name: deepseek-v4-flash
    litellm_params:
      model: deepseek/deepseek-v4-flash
      api_key: os.environ/DEEPSEEK_API_KEY
  - model_name: muse-spark-1.2-xhigh
    litellm_params:
      model: meta/muse-spark-1.2
      api_key: os.environ/META_API_KEY
      reasoning_effort: xhigh
  - model_name: kimi-k3-max
    litellm_params:
      model: moonshot/kimi-k3
      api_key: os.environ/MOONSHOT_API_KEY
      reasoning_effort: max
  - model_name: claude-opus-5
    litellm_params:
      model: anthropic/claude-opus-5
      api_key: os.environ/ANTHROPIC_API_KEY

  - model_name: lite-router
    litellm_params:
      model: auto_router/complexity_router
      complexity_router_config:
        tiers:
          SIMPLE:    deepseek-v4-flash
          MEDIUM:    muse-spark-1.2-xhigh
          COMPLEX:   kimi-k3-max
          REASONING: claude-opus-5
        classifier_type: llm
        classifier_llm_config:
          model: deepseek-v4-flash
          timeout_ms: 3000
          classification_rubric: agentic
        classifier_context_window_size: 0
        escalation_keywords: ["LITELLM ESCALATE"]
        session_affinity: false
      complexity_router_default_model: muse-spark-1.2-xhigh

1M Context

Luna, Terra, Sol, then Opus 5 at high reasoning effort. Uses the heuristic v2 classifier, so classification adds no LLM call.

The GPT tiers accept up to 922K input tokens and Opus 5 accepts 1M. The router moves oversized prompts to the lowest higher tier with capacity. See context-window escalation.

config.yaml
model_list:
  - model_name: gpt-5.6-luna
    litellm_params:
      model: openai/gpt-5.6-luna
      api_key: os.environ/OPENAI_API_KEY
  - model_name: gpt-5.6-terra
    litellm_params:
      model: openai/gpt-5.6-terra
      api_key: os.environ/OPENAI_API_KEY
  - model_name: gpt-5.6-sol
    litellm_params:
      model: openai/gpt-5.6-sol
      api_key: os.environ/OPENAI_API_KEY
  - model_name: claude-opus-5-high
    litellm_params:
      model: anthropic/claude-opus-5
      api_key: os.environ/ANTHROPIC_API_KEY
      reasoning_effort: high

  - model_name: 1m-auto
    litellm_params:
      model: auto_router/complexity_router
      complexity_router_config:
        tiers:
          SIMPLE:    gpt-5.6-luna
          MEDIUM:    gpt-5.6-terra
          COMPLEX:   gpt-5.6-sol
          REASONING: claude-opus-5-high
        classifier_type: heuristic_v2
        escalation_keywords: ["LITELLM ESCALATE"]
        session_affinity: false
      complexity_router_default_model: gpt-5.6-terra

The benchmark configuration

Terminal-Bench: Opus-5 solve rate at 27% lower cost, this config, gpt-5.4-mini classifier reading only the current message.
Cost and quality and prompt caching: same tiers, heuristic classifier.

config.yaml
model_list:
  - model_name: claude-haiku-4-5
    litellm_params:
      model: anthropic/claude-haiku-4-5
      api_key: os.environ/ANTHROPIC_API_KEY
  - model_name: claude-sonnet-5
    litellm_params:
      model: anthropic/claude-sonnet-5
      api_key: os.environ/ANTHROPIC_API_KEY
  - model_name: claude-opus-5
    litellm_params:
      model: anthropic/claude-opus-5
      api_key: os.environ/ANTHROPIC_API_KEY
  - model_name: gpt-5.4-mini
    litellm_params:
      model: openai/gpt-5.4-mini
      api_key: os.environ/OPENAI_API_KEY

  - model_name: smart-router
    litellm_params:
      model: auto_router/complexity_router
      complexity_router_config:
        tiers:
          SIMPLE:    claude-haiku-4-5
          MEDIUM:    claude-sonnet-5
          COMPLEX:   claude-opus-5
          REASONING: claude-opus-5
        classifier_type: llm
        classifier_llm_config:
          model: gpt-5.4-mini
        classifier_context_window_size: 0
      complexity_router_default_model: claude-sonnet-5

The classifier context window is the knob to revisit for your own traffic:

Terminal-Bench: last 3 user messages raised solve rate 66.7% to 76.2% and cost 44%. Assistant replies made both worse.
Chat traffic (v1.97 measurements): prior turns raised follow-up agreement 14% to 78%.
Shipped default: 3 user turns, no assistant turns.

The production configuration

The production case study that reported 51.1% savings.
Haiku serves both SIMPLE and MEDIUM; Opus is reserved for REASONING.
95% of requests never reached the flagship tier.

config.yaml
model_list:
  - model_name: claude-haiku-4-5
    litellm_params:
      model: anthropic/claude-haiku-4-5
      api_key: os.environ/ANTHROPIC_API_KEY
  - model_name: claude-sonnet-5
    litellm_params:
      model: anthropic/claude-sonnet-5
      api_key: os.environ/ANTHROPIC_API_KEY
  - model_name: claude-opus-5
    litellm_params:
      model: anthropic/claude-opus-5
      api_key: os.environ/ANTHROPIC_API_KEY

  - model_name: claude-auto-latest
    litellm_params:
      model: auto_router/complexity_router
      complexity_router_config:
        tiers:
          SIMPLE:    claude-haiku-4-5
          MEDIUM:    claude-haiku-4-5
          COMPLEX:   claude-sonnet-5
          REASONING: claude-opus-5
      complexity_router_default_model: claude-haiku-4-5

Coding agents with load balancing

For a tier with more than one deployment behind it, for example the same Claude model on Anthropic and on Bedrock.
session_affinity pins the tier for the session; deployment_affinity plus the prompt_caching pre-call check pins the deployment holding the cache.
Both matter on agent traffic. Details: Session affinity.

config.yaml
model_list:
  - model_name: claude-haiku-4-5
    litellm_params:
      model: anthropic/claude-haiku-4-5
      api_key: os.environ/ANTHROPIC_API_KEY
  - model_name: claude-sonnet-5
    litellm_params:
      model: anthropic/claude-sonnet-5
      api_key: os.environ/ANTHROPIC_API_KEY
  - model_name: claude-sonnet-5
    litellm_params:
      model: bedrock/us.anthropic.claude-sonnet-5
      aws_region_name: us-east-1
  - model_name: claude-opus-5
    litellm_params:
      model: anthropic/claude-opus-5
      api_key: os.environ/ANTHROPIC_API_KEY

  - model_name: claude-auto
    litellm_params:
      model: auto_router/complexity_router
      cache_control_injection_points:
        - location: message
          role: system
      complexity_router_config:
        tiers:
          SIMPLE:    claude-haiku-4-5
          MEDIUM:    claude-sonnet-5
          COMPLEX:   claude-sonnet-5
          REASONING: claude-opus-5
        session_affinity: true
        session_affinity_ttl_seconds: 3600
      complexity_router_default_model: claude-sonnet-5

router_settings:
  optional_pre_call_checks: ["deployment_affinity", "session_affinity", "prompt_caching"]
  deployment_affinity_ttl_seconds: 3600

Choosing a ladder​

Anthropic Family​

OpenAI Family​

Gemini Family​

Lite​

1M Context​

The benchmark configuration​

The production configuration​

Coding agents with load balancing​