Claude Opus 4.5 vs Qwen3 30B A3B Thinking
Detailed comparison between Claude Opus 4.5 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 Opus 4.5 takes the lead.
Both Claude Opus 4.5 and Qwen3 30B A3B Thinking are powerful language models designed for RAG applications. However, their performance characteristics differ in important ways.
Why Claude Opus 4.5:
- Claude Opus 4.5 has 227 higher ELO rating
- Claude Opus 4.5 is 4.1s faster on average
- Claude Opus 4.5 has a 20.9% higher win rate
Overview
Key metrics
ELO Rating
Overall ranking quality
Claude Opus 4.5
Qwen3 30B A3B Thinking
Win Rate
Head-to-head performance
Claude Opus 4.5
Qwen3 30B A3B Thinking
Quality Score
Overall quality metric
Claude Opus 4.5
Qwen3 30B A3B Thinking
Average Latency
Response time
Claude Opus 4.5
Qwen3 30B A3B Thinking
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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 | Claude Opus 4.5 | Qwen3 30B A3B Thinking | Description |
|---|---|---|---|
| Overall Performance | |||
| ELO Rating | 1549 | 1322 | Overall ranking quality based on pairwise comparisons |
| Win Rate | 48.5% | 27.6% | Percentage of comparisons won against other models |
| Quality Score | 4.88 | 4.89 | Average quality across all RAG metrics |
| Pricing & Context | |||
| Input Price per 1M | $5.00 | $0.05 | Cost per million input tokens |
| Output Price per 1M | $25.00 | $0.34 | Cost per million output tokens |
| Context Window | 200K | 33K | Maximum context window size |
| Release Date | 2025-11-24 | 2025-08-28 | Model release date |
| Performance Metrics | |||
| Avg Latency | 8.3s | 12.3s | Average response time across all datasets |
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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);
}Dataset Performance
By benchmark
Comprehensive comparison of RAG quality metrics (correctness, faithfulness, grounding, relevance, completeness) and latency for each benchmark dataset.
MSMARCO
| Metric | Claude Opus 4.5 | Qwen3 30B A3B Thinking | 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 | 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 | 5992ms | 12522ms | Average response time |
| Min | 2590ms | 1541ms | Fastest response time |
| Max | 8072ms | 49799ms | Slowest response time |
PG
| Metric | Claude Opus 4.5 | Qwen3 30B A3B Thinking | Description |
|---|---|---|---|
| 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 | 4.89 | 4.61 | Coverage of all aspects |
| Overall | 4.98 | 4.77 | Average across all metrics |
| Latency Metrics | |||
| Mean | 11489ms | 16030ms | Average response time |
| Min | 7945ms | 3483ms | Fastest response time |
| Max | 15934ms | 44237ms | Slowest response time |
SciFact
| Metric | Claude Opus 4.5 | Qwen3 30B A3B Thinking | Description |
|---|---|---|---|
| Quality Metrics | |||
| Correctness | 4.55 | 4.91 | Factual accuracy of responses |
| Faithfulness | 4.64 | 4.91 | Adherence to source material |
| Grounding | 4.64 | 4.91 | Citations and context usage |
| Relevance | 5.00 | 5.00 | Query alignment and focus |
| Completeness | 4.55 | 4.82 | Coverage of all aspects |
| Overall | 4.67 | 4.91 | Average across all metrics |
| Latency Metrics | |||
| Mean | 7276ms | 8384ms | Average response time |
| Min | 4210ms | 2185ms | Fastest response time |
| Max | 10496ms | 19414ms | Slowest response time |
Explore More
Compare more LLMs
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