Claude Sonnet 4.6 vs Qwen3 30B A3B Thinking

Detailed comparison between Claude Sonnet 4.6 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

Claude Sonnet 4.6 takes the lead.

Both Claude Sonnet 4.6 and Qwen3 30B A3B Thinking are powerful language models designed for RAG applications. However, their performance characteristics differ in important ways.

Why Claude Sonnet 4.6:

  • Claude Sonnet 4.6 has 327 higher ELO rating
  • Claude Sonnet 4.6 delivers better overall quality (4.95 vs 4.89)
  • Claude Sonnet 4.6 is 2.8s faster on average
  • Claude Sonnet 4.6 has a 30.6% higher win rate

Overview

Key metrics

ELO Rating

Overall ranking quality

Claude Sonnet 4.6

1649

Qwen3 30B A3B Thinking

1322

Win Rate

Head-to-head performance

Claude Sonnet 4.6

58.2%

Qwen3 30B A3B Thinking

27.6%

Quality Score

Overall quality metric

Claude Sonnet 4.6

4.95

Qwen3 30B A3B Thinking

4.89

Average Latency

Response time

Claude Sonnet 4.6

9498ms

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

MetricClaude Sonnet 4.6Qwen3 30B A3B ThinkingDescription
Overall Performance
ELO Rating
1649
1322
Overall ranking quality based on pairwise comparisons
Win Rate
58.2%
27.6%
Percentage of comparisons won against other models
Quality Score
4.95
4.89
Average quality across all RAG metrics
Pricing & Context
Input Price per 1M
$3.00
$0.05
Cost per million input tokens
Output Price per 1M
$15.00
$0.34
Cost per million output tokens
Context Window
200K
33K
Maximum context window size
Release Date
2026-02-17
2025-08-28
Model release date
Performance Metrics
Avg Latency
9.5s
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

MetricClaude Sonnet 4.6Qwen3 30B A3B ThinkingDescription
Quality Metrics
Correctness
4.97
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
4.93
5.00
Coverage of all aspects
Overall
4.98
5.00
Average across all metrics
Latency Metrics
Mean
5785ms
12522ms
Average response time
Min2066ms1541msFastest response time
Max8195ms49799msSlowest response time

PG

MetricClaude Sonnet 4.6Qwen3 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
5.00
4.61
Coverage of all aspects
Overall
5.00
4.77
Average across all metrics
Latency Metrics
Mean
12740ms
16030ms
Average response time
Min8720ms3483msFastest response time
Max20930ms44237msSlowest response time

SciFact

MetricClaude Sonnet 4.6Qwen3 30B A3B ThinkingDescription
Quality Metrics
Correctness
4.83
4.91
Factual accuracy of responses
Faithfulness
4.87
4.91
Adherence to source material
Grounding
4.87
4.91
Citations and context usage
Relevance
5.00
5.00
Query alignment and focus
Completeness
4.77
4.82
Coverage of all aspects
Overall
4.87
4.91
Average across all metrics
Latency Metrics
Mean
9969ms
8384ms
Average response time
Min2886ms2185msFastest response time
Max19276ms19414msSlowest response time

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