Grok Build 0.1 pricing for rag pipeline

Quick start

Call Grok Build 0.1 with the OpenAI Python SDK pointed at api.x.ai:

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.x.ai/v1",
    api_key=os.environ["XAI_API_KEY"],
)
response = client.chat.completions.create(
    model="grok-build-0.1",
    max_tokens=1500,
    messages=[{"role": "user", "content": "Your prompt here"}]
)
# Cost: ~$0.009000 per call at standard rates
# Input:  $1.00/MTok
# Output: $2.00/MTok

How Grok Build 0.1 compares on cost

Per-call cost for all models scoring 50+ on rag pipeline benchmarks. Hover for details.

Cheapest viable option: GPT 5.6 Luna at $0.003000/call (67% less, 85/100 quality).

What drives rag pipeline cost

Retrieval-augmented calls are dominated by the retrieved passages, not the question. Retrieving eight chunks instead of three roughly triples input cost for an answer that is often no better, so retrieval tuning is cost tuning. The system prompt and instructions are identical on every call, which makes cache reads the first thing to enable.

For Grok Build 0.1 specifically, that means output tokens cost 2.0x more than input, at $2.00 vs $1.00 per MTok. At this use case’s 6,000/1,500 token split, output accounts for 33% of the $0.009000 per-call cost.

Grok Build 0.1 quality benchmarks for rag pipeline

Benchmark data for rag pipeline is pending for Grok Build 0.1. Test against your own evaluation suite before moving to production.

Performance across all task types

Grok Build 0.1 scores by task type. RAG Pipeline highlighted.

Confidence scores are a third-party benchmark snapshot from week 29 of 2026. This snapshot is 8 weeks old, so treat the scores as directional and validate against your own evals.

About Grok Build 0.1

xAI's coding-specialised model and the cheapest Grok row on output. Released in early access; xAI publishes release notes at month granularity only.

Other models from Grok

You can compare Grok Build 0.1 head-to-head against any other model on our model comparison pages, or track how pricing has changed over time on the pricing history tracker.

Frequently asked questions