跳至主要內容

推薦設定

建議的梯級,與儀表板的 Auto Router 範本相符。每個層級都必須在同一個檔案中存在為 model_name;將提供者前綴或憑證替換為您自己的部署,router 項目就會保持不變。

梯級SIMPLEMEDIUMCOMPLEXREASONING分類器
1M Contextgpt-5.6-lunagpt-5.6-terragpt-5.6-solclaude-opus-5, high effortheuristic v2
Anthropic Familyclaude-haiku-4-5claude-sonnet-5claude-opus-5claude-opus-5, high effortheuristic
OpenAI Familygpt-5.6-lunagpt-5.6-terragpt-5.6-solgpt-5.6-sol, xhigh effortheuristic
Gemini Familygemini-2.5-flash-litegemini-3.1-flash-litegemini-3.7-flashgemini-3.1-pro-previewheuristic
Litedeepseek-v4-flashmuse-spark-1.2, xhighkimi-k3, maxclaude-opus-5LLM, agentic rubric
Benchmark configclaude-haiku-4-5claude-sonnet-5claude-opus-5claude-opus-5LLM, gpt-5.4-mini
Production configclaude-haiku-4-5claude-haiku-4-5claude-sonnet-5claude-opus-5heuristic

選擇梯級​

  • 單一家族:當用戶端依賴提供者特定行為(Anthropic 快取控制、OpenAI reasoning 參數)時。每個層級都維持在同一個 API 表面。
  • Lite:當成本比提供者一致性更重要且流量是 agentic 時。混合提供者,使用帶有 agentic rubric 的 LLM 分類器。
  • 1M Context:適用於長提示。Luna、Terra、Sol,然後在高 effort 時用 Opus 5,搭配 heuristic v2 分類器。
  • 相同模型,更多 effort:適用於最上層。增加的是輸出 token 成本,而不是更高的每 token 費率。模式:effort 梯級。
  • 所有梯級都會關閉 session_affinity(預設;請參閱 prompt caching),並設定 escalation_keywords: ["LITELLM ESCALATE"];當完全相同的片語出現時,會將請求提升一個層級。比對區分大小寫。

Anthropic Family​

Haiku、Sonnet、Opus,然後在高 reasoning effort 時使用 Opus。

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

如果 Claude Code 或 Claude Desktop 需要發現它,請在 router 名稱中保留 claude。請參閱 管理員設定。

OpenAI Family​

Luna、Terra、Sol,然後在 xhigh reasoning effort 時使用 Sol。

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,然後是 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​

跨提供者,以成本為導向。DeepSeek V4 Flash 也可作為 LLM 分類器,採用 agentic rubric,並使用零輪次上下文視窗。

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,然後在高 reasoning effort 時使用 Opus 5。使用 heuristic v2 分類器,因此分類不會增加任何 LLM 請求。

GPT 層級最多可接受 922K 輸入 token,而 Opus 5 可接受 1M。router 會將超出大小的提示移到具備容量的下一個最低較高層級。請參閱 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

基準測試設定​

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

分類器上下文視窗是您自己流量最值得重新檢視的調整旋鈕:

  • Terminal-Bench:最近 3 則使用者訊息讓 solve rate 從 66.7% 提升到 76.2%,成本增加 44%。assistant 回覆會讓兩者都更差。
  • 聊天流量(v1.97 measurements):先前輪次讓後續回應一致率從 14% 提升到 78%。
  • 已發布的預設值:3 則使用者輪次,不含 assistant 輪次。

Production config​

  • 報告 51.1% 節省的 production case study。
  • Haiku 同時處理 SIMPLE 與 MEDIUM;Opus 保留給 REASONING。
  • 95% 的請求從未到達旗艦層級。
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

具備負載平衡的 coding agents​

  • 適用於其後方有多個部署的層級,例如 Anthropic 與 Bedrock 上的相同 Claude model。
  • session_affinity 會將該層級鎖定於該 session;deployment_affinity 加上 prompt_caching 的 pre-call 檢查會將具備快取的部署鎖定。
  • 這兩者對 agent 流量都很重要。細節: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
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