Grok 4 Fast vs GPT-5.2
Detailed comparison between Grok 4 Fast and GPT-5.2 for RAG applications. See which LLM best meets your accuracy, performance, and cost needs. If you want to compare these models on your data, try Agentset.
Model Comparison
Two competitive LLMs, closely matched.
Both Grok 4 Fast and GPT-5.2 are powerful language models designed for RAG applications. They show comparable performance across key metrics.
Key similarities:
- Similar ELO ratings (1492 vs 1491)
- Comparable quality metrics
- Similar latency characteristics
Overview
Key metrics
ELO Rating
Overall ranking quality
Grok 4 Fast
GPT-5.2
Win Rate
Head-to-head performance
Grok 4 Fast
GPT-5.2
Quality Score
Overall quality metric
Grok 4 Fast
GPT-5.2
Average Latency
Response time
Grok 4 Fast
GPT-5.2
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Visual Performance Analysis
Performance
ELO Rating Comparison
Win/Loss/Tie Breakdown
Quality Across Datasets (Overall Score)
Latency Distribution (ms)
Breakdown
How the models stack up
| Metric | Grok 4 Fast | GPT-5.2 | Description |
|---|---|---|---|
| Overall Performance | |||
| ELO Rating | 1492 | 1491 | Overall ranking quality based on pairwise comparisons |
| Win Rate | 44.9% | 40.1% | Percentage of comparisons won against other models |
| Quality Score | 4.96 | 4.97 | Average quality across all RAG metrics |
| Pricing & Context | |||
| Input Price per 1M | $0.20 | $1.75 | Cost per million input tokens |
| Output Price per 1M | $0.50 | $14.00 | Cost per million output tokens |
| Context Window | 2000K | 400K | Maximum context window size |
| Release Date | 2025-09-19 | 2025-12-11 | Model release date |
| Performance Metrics | |||
| Avg Latency | 5.9s | 5.4s | Average response time across all datasets |
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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);
}Dataset Performance
By benchmark
Comprehensive comparison of RAG quality metrics (correctness, faithfulness, grounding, relevance, completeness) and latency for each benchmark dataset.
MSMARCO
| Metric | Grok 4 Fast | GPT-5.2 | Description |
|---|---|---|---|
| Quality Metrics | |||
| Correctness | 4.90 | 5.00 | Factual accuracy of responses |
| Faithfulness | 4.90 | 5.00 | Adherence to source material |
| Grounding | 4.90 | 5.00 | Citations and context usage |
| Relevance | 5.00 | 4.97 | Query alignment and focus |
| Completeness | 4.83 | 4.83 | Coverage of all aspects |
| Overall | 4.91 | 4.96 | Average across all metrics |
| Latency Metrics | |||
| Mean | 3894ms | 2652ms | Average response time |
| Min | 1742ms | 796ms | Fastest response time |
| Max | 6649ms | 5810ms | Slowest response time |
PG
| Metric | Grok 4 Fast | GPT-5.2 | Description |
|---|---|---|---|
| Quality Metrics | |||
| Correctness | 5.00 | 5.00 | Factual accuracy of responses |
| Faithfulness | 5.00 | 5.00 | Adherence to source material |
| Grounding | 5.00 | 5.00 | Citations and context usage |
| Relevance | 5.00 | 4.97 | Query alignment and focus |
| Completeness | 4.97 | 4.97 | Coverage of all aspects |
| Overall | 4.99 | 4.99 | Average across all metrics |
| Latency Metrics | |||
| Mean | 9142ms | 8702ms | Average response time |
| Min | 4767ms | 2755ms | Fastest response time |
| Max | 17055ms | 14361ms | Slowest response time |
SciFact
| Metric | Grok 4 Fast | GPT-5.2 | Description |
|---|---|---|---|
| Quality Metrics | |||
| Correctness | 5.00 | 4.93 | Factual accuracy of responses |
| Faithfulness | 5.00 | 5.00 | Adherence to source material |
| Grounding | 5.00 | 5.00 | Citations and context usage |
| Relevance | 4.97 | 5.00 | Query alignment and focus |
| Completeness | 4.90 | 4.80 | Coverage of all aspects |
| Overall | 4.97 | 4.95 | Average across all metrics |
| Latency Metrics | |||
| Mean | 4516ms | 4785ms | Average response time |
| Min | 2358ms | 1318ms | Fastest response time |
| Max | 14942ms | 10172ms | Slowest response time |
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