--- title: "Recommended Configurations" url: "/docs/auto_router/recommended_configurations" canonical_url: "https://docs.litellm.ai/docs/auto_router/recommended_configurations" type: "docs" last_updated: "2026-10-12" summary: "The bundled 1M Context, Anthropic, OpenAI, Gemini, and Lite presets as config.yaml, the configs the public benchmarks were run on, and how to choose between them." related: - "/docs/auto_router/user_setup" - "/docs/auto_router/benchmarks" --- # Recommended Configurations > Index of all LiteLLM docs: https://docs.litellm.ai/llms.txt 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](#1m-context) | gpt-5.6-luna | gpt-5.6-terra | gpt-5.6-sol | claude-opus-5, high effort | heuristic v2 | | [Anthropic Family](#anthropic-family) | claude-haiku-4-5 | claude-sonnet-5 | claude-opus-5 | claude-opus-5, high effort | heuristic | | [OpenAI Family](#openai-family) | gpt-5.6-luna | gpt-5.6-terra | gpt-5.6-sol | gpt-5.6-sol, xhigh effort | heuristic | | [Gemini Family](#gemini-family) | gemini-2.5-flash-lite | gemini-3.1-flash-lite | gemini-3.7-flash | gemini-3.1-pro-preview | heuristic | | [Lite](#lite) | deepseek-v4-flash | muse-spark-1.2, xhigh | kimi-k3, max | claude-opus-5 | LLM, agentic rubric | | [Benchmark config](#the-benchmark-configuration) | claude-haiku-4-5 | claude-sonnet-5 | claude-opus-5 | claude-opus-5 | LLM, gpt-5.4-mini | | [Production config](#the-production-configuration) | 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](/docs/proxy/auto_routing#effort-ladders). - All ladders leave `session_affinity` off (the default; see [prompt caching](/docs/auto_router/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. ```yaml title="config.yaml" keep-model-ids 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](/docs/auto_router/setup#claude-code-and-claude-desktop). ## OpenAI Family Luna, Terra, Sol, then Sol at xhigh reasoning effort. ```yaml title="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. ```yaml title="config.yaml" keep-model-ids 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. ```yaml title="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](/docs/proxy/auto_routing#context-window). ```yaml title="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](/blog/auto-router-terminal-bench-benchmark): Opus-5 solve rate at 27% lower cost, this config, gpt-5.4-mini classifier reading only the current message. - [Cost and quality](/blog/auto-router-cost-quality-benchmark) and [prompt caching](/blog/auto-router-prompt-caching-benchmark): same tiers, heuristic classifier. ```yaml title="config.yaml" keep-model-ids 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](/blog/auto-router-context-and-benchmarks)): prior turns raised follow-up agreement 14% to 78%. - Shipped default: 3 user turns, no assistant turns. ## The production configuration - The [production case study](/blog/auto-router-production-savings) that reported 51.1% savings. - Haiku serves both SIMPLE and MEDIUM; Opus is reserved for REASONING. - 95% of requests never reached the flagship tier. ```yaml title="config.yaml" keep-model-ids 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](/docs/proxy/auto_routing#session-affinity). ```yaml title="config.yaml" keep-model-ids 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 ``` ## Related pages - [User Setup](https://docs.litellm.ai/docs/auto_router/user_setup.md) - [Public Benchmarks](https://docs.litellm.ai/docs/auto_router/benchmarks.md)