Free
$0Free plan available.
LiteLLM is an LLM gateway and OpenAI proxy designed to manage authentication, load balancing, and spend tracking across over 100 LLMs while maintaining the OpenAI API format. It streamlines integration with providers such as OpenAI, Azure, Cohere, Anthropic, Replicate, and Google. LiteLLM ensures consistent outputs and exception handling across all APIs, while providing tools for logging, error tracking, cost management, batch processing, guardrails, model access control, budgeting, observability, rate limiting, prompt management, S3 logging, and pass-through endpoints.
Integrate LiteLLM by calling LLM APIs using the standard chatGPT format: completion(model, messages). This approach ensures consistent outputs and exception handling across all supported LLM APIs. You can deploy the open-source version of LiteLLM or opt for LiteLLM Enterprise to access additional features.
LiteLLM is a tool that simplifies model access, spend tracking, and the implementation of fallbacks across more than 100 LLMs.
LiteLLM provides features including cost tracking, budget management, rate limiting, an OpenAI-compatible API, LLM fallbacks, and more.
LiteLLM reduces operational complexity by standardizing logging, authentication, and the OpenAI API interface across all supported models.
Free plan available.
Use these comparison pages to understand the trade-offs between the models most relevant to LiteLLM.
Compare GPT 5.4 and GPT 5.4 Pro across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus long-context workloads.
Compare GPT 5.5 and GPT 5.4 across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus long-context workloads.
Compare GPT 5.5 and Claude 4.6 Opus across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus long-context workloads.
Compare GPT 5.5 and Claude 4.6 Sonnet across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus long-context workloads.
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