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How to use Grok 4 in production

A practical guide to grok-4, using data from the AI & LLM Index. The goal is a shortlist and a decision, not a tour of every option.

What Grok 4 is

Grok 4 is a frontier model from xAI, released in 2026-02-22. Grok 4 is a frontier model from xAI. It offers a 200,000-token context window, costs $17.90 per million input tokens and $84.75 per million output tokens, and averages 86.5 across the index's benchmark suite.

It is a proprietary model under a proprietary licence. That determines whether you can self-host it, fine-tune it and keep data inside your own infrastructure.

Pricing and context

Grok 4 costs $17.90 per million input tokens, $84.75 per million output tokens and $2.45 per million cached input tokens. The blended 3:1 figure is about $34.61 per million tokens.

The context window is 200,000 tokens and the maximum output is 32,768 tokens. If your prompts regularly exceed the context window, you need chunking or retrieval before price becomes the main issue.

Benchmark position

Grok 4 averages 86.5 across the index's scored benchmarks, ranking #26 of 600. Its strongest relative result is HellaSwag.

Use the table below to see the full benchmark profile. A single strong score is less useful than a consistent profile across the benchmarks that resemble your task.

Alternatives

The closest models by average score are Grok 4 Max, o4 Turbo, Gemini 3 Pro Turbo. Compare their prices before assuming the closest score is the best substitute; a small quality drop can cut unit cost substantially.

Grok 4 benchmark profile

BenchmarkScoreRankPercentile
MMLU-Pro87.8#2097th
GPQA Diamond82.6#2496th
SWE-bench Verified77.9#1797th
HumanEval92.0#2296th
MATH-50090.8#3794th
AIME 202582.2#6390th
LiveCodeBench82.2#2396th
IFEval91.6#4393th

Related

FAQ

Is Grok 4 worth it?

Grok 4 averages 86.5 and costs $17.90 per million input tokens. Whether it is worth it depends on whether your task benefits from its benchmark profile; test it against a cheaper alternative.

What is the most important factor in grok-4?

Fit to the task beats raw benchmark rank. Define the failure modes, pick the benchmarks that resemble them, then test a small shortlist on your own examples.

How many models should I evaluate?

Three is usually enough: a quality candidate, a value candidate and a fallback. More candidates add work without changing the decision.

How do I control cost?

Compare blended 3:1 price, watch output tokens, use caching where available and route simple requests to a cheaper model.