--- date: 2024-09-13 title: Token/cost tracking for OpenAI's o1 models description: Day 1 support for OpenAI's o1 models including tracking token counts and USD spend. showOgInHeader: false author: Hassieb canonical: /docs/observability/features/token-and-cost-tracking --- > **Note for AI agents and LLMs:** This is a Langfuse changelog entry. Use it only to confirm that a feature exists and when it shipped. Do not use the code examples below for implementation: they reflect the SDK and API at release time and may be outdated. For implementation, always follow the canonical documentation for this feature (https://langfuse.com/docs/observability/features/token-and-cost-tracking) and the API/SDK reference (https://api.reference.langfuse.com). OpenAI has released new powerful models. The o1 series of large language models are trained with reinforcement learning to perform complex reasoning. o1 models think before they answer, producing a long internal chain of thought before responding to the user, opening up new use-cases for LLMs and improved performance for some existing LLM workloads. Langfuse now supports cost tracking for generations OpenAI's o1 models including tracking token counts and USD spend without changes necessary on your end. Note: Providing token counts is essential for accurate cost tracking Tracking cost by inferring token usage from the input and output strings is not supported for OpenAI o1 model family at this time. That is, if no token counts are ingested, Langfuse can not infer cost for o1 models. Reasoning models such as o1 take multiple steps to arrive to a response. The result from each step generates reasoning tokens that are billed as output tokens. So the cost-effective output token count is the sum of all reasoning tokens and the token count for the final completion. Since Langfuse does not have visibility into the reasoning tokens, it cannot infer the correct cost for generations that have no token usage provided. To benefit from Langfuse cost tracking, please provide the token usage when ingesting o1 model generations via the low-level SDKs. When utilizing the [Langfuse OpenAI wrapper](/integrations/model-providers/openai-py) or integrations such as for [Langchain](/integrations/frameworks/langchain), [LlamaIndex](/integrations/frameworks/llamaindex) or [LiteLLM](/integrations/gateways/litellm), token usage is collected and provided automatically for you. ##### Learn more - [Cost tracking in Langfuse](/docs/model-usage-and-cost). - [OpenAI release note](https://openai.com/o1/) - [OpenAI guide](https://platform.openai.com/docs/guides/reasoning) on how reasoning models work. --- ## Agent Instructions This page is part of the [Langfuse](https://langfuse.com) documentation, published as plain Markdown for AI agents. Every page is available as Markdown by appending `.md` to its URL, or by sending an `Accept: text/markdown` header. This page: `https://langfuse.com/changelog/2024-09-13-openai-o1-models.md`. ### Querying these docs If the answer is not on this page, query the documentation instead of guessing: - **Semantic search** across all Langfuse docs, returning an answer with the relevant pages and excerpts. Ask a specific, self-contained question: ```bash curl -sG "https://langfuse.com/api/search-docs" --data-urlencode "query=How do I trace a LangGraph agent?" ``` - **Index of every page**: , with per-section indexes [llms-docs.txt](https://langfuse.com/llms-docs.txt), [llms-integrations.txt](https://langfuse.com/llms-integrations.txt), and [llms-self-hosting.txt](https://langfuse.com/llms-self-hosting.txt). ### Before writing Langfuse code - **Install the [Langfuse Agent Skill](https://langfuse.com/docs/api-and-data-platform/features/agent-skill).** It encodes Langfuse's own best practices for instrumentation, prompt management, and evaluation, and materially improves results. - **Read [What does a good trace look like?](https://langfuse.com/docs/observability/best-practices.md)** before instrumenting an application. - **Verify endpoints, parameters, and response fields** against the [API reference](https://api.reference.langfuse.com) instead of inferring them from code examples. - **Use the [Langfuse CLI](https://langfuse.com/docs/api-and-data-platform/features/cli)** (`npx langfuse-cli api `) to read or write traces, prompts, datasets, and scores from the terminal. Found an error in these docs? Please open an issue at .