Grok 4.5 pricing for financial analysis

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=4000,
    messages=[{"role": "user", "content": "Your prompt here"}]
)
# Cost: ~$0.0400 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 financial analysis benchmarks. Hover for details.

Cheapest viable option: Claude Sonnet 5 at $0.0560/call (-40% less, 90/100 quality).

What drives financial analysis cost

Financial analysis carries large structured inputs (statements, tables, time series) and produces detailed reasoning as output, so it is expensive on both sides. Tabular data is token-dense, and formatting choices in how you serialize a table can change input cost by a wide margin before you have changed anything about the model.

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 8,000/4,000 token split, output accounts for 60% of the $0.0400 per-call cost.

Grok 4.5 quality benchmarks for financial analysis

Benchmark data for financial analysis 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. Financial Analysis 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