Back to all LLMs

Grok 4 Fast

2M token context window enables retrieval of entire books or massive document collections without chunking. Built-in web and X search capabilities with togglable reasoning mode for flexible RAG workflows. If you want to compare the best LLMs for your data, try Agentset.

Leaderboard Rank
#8
of 16
ELO Rating
1492
#8
Win Rate
44.9%
#7
Latency
5851ms
#3

Model Information

Provider
xAI
License
Proprietary
Input Price per 1M
$0.20
Output Price per 1M
$0.50
Context Window
2000K
Release Date
2025-09-19
Model Name
grok-4-fast
Total Evaluations
1350

Performance Record

Wins606 (44.9%)
Losses557 (41.3%)
Ties187 (13.9%)
Wins
Losses
Ties

LLMs Are Just One Piece of RAG

Agentset gives you a managed RAG pipeline with the top-ranked models and best practices baked in. No infrastructure to maintain, no LLM orchestration to manage.

Trusted by teams building production RAG applications

5M+
Documents
1,500+
Teams
99.9%
Uptime

Performance Overview

ELO ratings by dataset

Grok 4 Fast's ELO performance varies across different benchmark datasets, showing its strengths in specific domains.

Grok 4 Fast - ELO by Dataset

Detailed Metrics

Dataset breakdown

Performance metrics across different benchmark datasets, including accuracy and latency percentiles.

PG

ELO 168864.0% WR288W-133L-29T

Quality Metrics

Correctness
5.00
Faithfulness
5.00
Grounding
5.00
Relevance
5.00
Completeness
4.97
Overall
4.99

Latency Distribution

Mean
9142ms
Min
4767ms
Max
17055ms

MSMARCO

ELO 148746.2% WR208W-171L-71T

Quality Metrics

Correctness
4.90
Faithfulness
4.90
Grounding
4.90
Relevance
5.00
Completeness
4.83
Overall
4.91

Latency Distribution

Mean
3894ms
Min
1742ms
Max
6649ms

SciFact

ELO 130224.4% WR110W-253L-87T

Quality Metrics

Correctness
5.00
Faithfulness
5.00
Grounding
5.00
Relevance
4.97
Completeness
4.90
Overall
4.97

Latency Distribution

Mean
4516ms
Min
2358ms
Max
14942ms

Build RAG in Minutes, Not Months

Agentset gives you a complete RAG API with top-ranked LLMs and smart retrieval built in. Upload your data, call the API, and get grounded answers from day one.

import { Agentset } from "agentset";

const agentset = new Agentset();
const ns = agentset.namespace("ns_1234");

const results = await ns.search(
  "What is multi-head attention?"
);

for (const result of results) {
  console.log(result.text);
}

Compare Models

See how it stacks up

Compare Grok 4 Fast with other top llms to understand the differences in performance, accuracy, and latency.