Grok 4 Fast vs Qwen3 30B A3B Thinking

Detailed comparison between Grok 4 Fast and Qwen3 30B A3B Thinking 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

Grok 4 Fast takes the lead.

Both Grok 4 Fast and Qwen3 30B A3B Thinking are powerful language models designed for RAG applications. However, their performance characteristics differ in important ways.

Why Grok 4 Fast:

  • Grok 4 Fast has 295 higher ELO rating
  • Grok 4 Fast delivers better overall quality (4.99 vs 4.89)
  • Grok 4 Fast is 6.5s faster on average
  • Grok 4 Fast has a 26.8% higher win rate

Overview

Key metrics

ELO Rating

Overall ranking quality

Grok 4 Fast

1616

Qwen3 30B A3B Thinking

1322

Win Rate

Head-to-head performance

Grok 4 Fast

54.3%

Qwen3 30B A3B Thinking

27.6%

Quality Score

Overall quality metric

Grok 4 Fast

4.99

Qwen3 30B A3B Thinking

4.89

Average Latency

Response time

Grok 4 Fast

5851ms

Qwen3 30B A3B Thinking

12312ms

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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

MetricGrok 4 FastQwen3 30B A3B ThinkingDescription
Overall Performance
ELO Rating
1616
1322
Overall ranking quality based on pairwise comparisons
Win Rate
54.3%
27.6%
Percentage of comparisons won against other models
Quality Score
4.99
4.89
Average quality across all RAG metrics
Pricing & Context
Input Price per 1M
$0.20
$0.05
Cost per million input tokens
Output Price per 1M
$0.50
$0.34
Cost per million output tokens
Context Window
2000K
33K
Maximum context window size
Release Date
2025-09-19
2025-08-28
Model release date
Performance Metrics
Avg Latency
5.9s
12.3s
Average response time across all datasets

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}

Dataset Performance

By benchmark

Comprehensive comparison of RAG quality metrics (correctness, faithfulness, grounding, relevance, completeness) and latency for each benchmark dataset.

MSMARCO

MetricGrok 4 FastQwen3 30B A3B ThinkingDescription
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
5.00
Query alignment and focus
Completeness
5.00
5.00
Coverage of all aspects
Overall
5.00
5.00
Average across all metrics
Latency Metrics
Mean
3894ms
12522ms
Average response time
Min1742ms1541msFastest response time
Max6649ms49799msSlowest response time

PG

MetricGrok 4 FastQwen3 30B A3B ThinkingDescription
Quality Metrics
Correctness
5.00
4.78
Factual accuracy of responses
Faithfulness
5.00
4.78
Adherence to source material
Grounding
5.00
4.78
Citations and context usage
Relevance
5.00
4.89
Query alignment and focus
Completeness
4.94
4.61
Coverage of all aspects
Overall
4.99
4.77
Average across all metrics
Latency Metrics
Mean
9142ms
16030ms
Average response time
Min4767ms3483msFastest response time
Max17055ms44237msSlowest response time

SciFact

MetricGrok 4 FastQwen3 30B A3B ThinkingDescription
Quality Metrics
Correctness
5.00
4.91
Factual accuracy of responses
Faithfulness
5.00
4.91
Adherence to source material
Grounding
5.00
4.91
Citations and context usage
Relevance
5.00
5.00
Query alignment and focus
Completeness
4.91
4.82
Coverage of all aspects
Overall
4.98
4.91
Average across all metrics
Latency Metrics
Mean
4516ms
8384ms
Average response time
Min2358ms2185msFastest response time
Max14942ms19414msSlowest response time

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