Grok 4.3 pricing for resume parsing

Quick start

Call Grok 4.3 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-4.3",
    max_tokens=800,
    messages=[{"role": "user", "content": "Your prompt here"}]
)
# Cost: ~$0.005125 per call at standard rates
# Input:  $1.25/MTok
# Output: $2.50/MTok

How Grok 4.3 compares on cost

Per-call cost for all models scoring 50+ on resume parsing benchmarks. Hover for details.

Cheapest viable option: GPT 5.6 Luna at $0.001460/call (72% less, 89/100 quality).

What drives resume parsing cost

Resumes are longer than invoices and less uniformly structured, so input runs higher while output stays compact. Like invoice parsing this is a bulk, latency-tolerant workload where the batch API discount applies to the entire request. The parsing instructions are identical across every candidate, which puts them squarely in the cached prefix.

For Grok 4.3 specifically, that means output tokens cost 2.0x more than input, at $2.50 vs $1.25 per MTok. At this use case’s 2,500/800 token split, output accounts for 39% of the $0.005125 per-call cost.

Grok 4.3 quality benchmarks for resume parsing

Benchmark data for resume parsing is pending for Grok 4.3. Test against your own evaluation suite before moving to production.

Performance across all task types

Grok 4.3 scores by task type. Resume Parsing 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 4.3

xAI publishes no release date for this model; dated to the March 4.20-generation launch whose pricing and 1M context window it shares. Priced from the live models table, which is what matters for costing traffic.

Other models from Grok

You can compare Grok 4.3 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