OpenAI

Required OpenAI Attributes

The following cost calculation tags are required for traces originating from OpenAI API calls.

For a complete, runnable example, see Quill — Beakpoint's reference application for multi-model LLM cost attribution. Quill's tracing.py shows the full OTel setup, and roles.py defines the OpenAI models used.

Always Required Fields

These fields are always required for Beakpoint to calculate OpenAI costs:

Attribute Name	Example Value	Allowed Values
gen_ai.system	openai	openai (must be this exact value)
gen_ai.request.model	gpt-4.1	Any valid OpenAI model name
gen_ai.usage.input_tokens	512	Non-negative integer
gen_ai.usage.output_tokens	128	Non-negative integer

gen_ai.system requires a SpanProcessor

The OpenAI auto-instrumentor sets gen_ai.provider.name but not gen_ai.system. Beakpoint requires gen_ai.system for pricing. Use a custom SpanProcessor to copy gen_ai.provider.name → gen_ai.system on span start. See the _GenAiSystemProcessor in Quill for the pattern, or the Track LLM Costs guide for a full walkthrough.

Optional Enrichment Attributes

These fields are optional but improve cost accuracy when provided:

Attribute Name	Example Value	Description
gen_ai.response.model	gpt-4.1-2025-04-14	The exact model version returned in the response. When present, this takes precedence over gen_ai.request.model for pricing lookups.
gen_ai.usage.input_tokens.cached	256	Number of input tokens served from the prompt cache. Cached tokens are billed at a reduced rate.
gen_ai.usage.output_tokens.reasoning	64	Number of tokens used for internal reasoning (o-series models). Billed at the standard output token rate.
cloud.provider	openai	Provider identification. Set automatically by the _GenAiSystemProcessor pattern.

Supported Models

Beakpoint calculates costs for the following OpenAI models. Prices are per 1 million tokens (USD).

Model	Input ($/M)	Cached Input ($/M)	Output ($/M)
gpt-4.1	$2.00	$0.50	$8.00
gpt-4.1-mini	$0.40	$0.10	$1.60
gpt-4.1-nano	$0.10	$0.025	$0.40

note

Prices reflect OpenAI list pricing and may change. Beakpoint keeps these rates up to date, but check the OpenAI pricing page for the latest figures.

Python Example

The quickest way to emit the required attributes is with the opentelemetry-instrumentation-openai-v2 package paired with a SpanProcessor that sets gen_ai.system. This example is adapted from Quill:

pip install opentelemetry-instrumentation-openai-v2

from opentelemetry.instrumentation.openai_v2 import OpenAIInstrumentor
from openai import OpenAI

# Instrument before creating the client (see Track LLM Costs guide
# for the full TracerProvider setup including _GenAiSystemProcessor)
OpenAIInstrumentor().instrument()

client = OpenAI()

response = client.chat.completions.create(
    model="gpt-4.1-mini",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello!"},
    ],
)

The instrumentation automatically sets gen_ai.provider.name, gen_ai.request.model, gen_ai.usage.input_tokens, gen_ai.usage.output_tokens, and the optional enrichment attributes whenever they are available in the API response. The _GenAiSystemProcessor copies gen_ai.provider.name to gen_ai.system and cloud.provider.

For full setup instructions, including how to configure the OpenTelemetry exporter for Beakpoint, see the Track LLM Costs guide.