Grok 4.5 pricing for rag pipeline

Quick start

Call Grok 4.5 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-4.5",
    max_tokens=1500,
    messages=[{"role": "user", "content": "Your prompt here"}]
)
# Cost: ~$0.0210 per call at standard rates
# Input:  $2.00/MTok
# Output: $6.00/MTok

How Grok 4.5 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 (86% 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 4.5 specifically, that means output tokens cost 3.0x more than input, at $6.00 vs $2.00 per MTok. At this use case’s 6,000/1,500 token split, output accounts for 43% of the $0.0210 per-call cost.

Grok 4.5 quality benchmarks for rag pipeline

Benchmark data for rag pipeline is pending for Grok 4.5. Test against your own evaluation suite before moving to production.

Performance across all task types

Grok 4.5 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 4.5

Same list price as Grok 4.6 but a cheaper cached rate ($0.30/MTok vs $0.50), so cache-heavy workloads are cheaper here than on 4.6. xAI publishes release notes at month granularity only; dated to the start of the month.

Other models from Grok

You can compare Grok 4.5 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