GLM 4.6 vs DeepSeek R1
Detailed comparison between GLM 4.6 and DeepSeek R1 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
GLM 4.6 takes the lead.
Both GLM 4.6 and DeepSeek R1 are powerful language models designed for RAG applications. However, their performance characteristics differ in important ways.
Why GLM 4.6:
- GLM 4.6 has 214 higher ELO rating
- GLM 4.6 has a 19.4% higher win rate
Overview
Key metrics
ELO Rating
Overall ranking quality
GLM 4.6
DeepSeek R1
Win Rate
Head-to-head performance
GLM 4.6
DeepSeek R1
Quality Score
Overall quality metric
GLM 4.6
DeepSeek R1
Average Latency
Response time
GLM 4.6
DeepSeek R1
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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 | GLM 4.6 | DeepSeek R1 | Description |
|---|---|---|---|
| Overall Performance | |||
| ELO Rating | 1487 | 1274 | Overall ranking quality based on pairwise comparisons |
| Win Rate | 37.0% | 17.6% | Percentage of comparisons won against other models |
| Quality Score | 4.93 | 4.92 | Average quality across all RAG metrics |
| Pricing & Context | |||
| Input Price per 1M | $0.40 | $0.30 | Cost per million input tokens |
| Output Price per 1M | $1.75 | $1.20 | Cost per million output tokens |
| Context Window | 203K | 164K | Maximum context window size |
| Release Date | 2025-09-30 | 2025-01-20 | Model release date |
| Performance Metrics | |||
| Avg Latency | 33.1s | 18.3s | 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 | GLM 4.6 | DeepSeek R1 | 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 | 34694ms | 16654ms | Average response time |
| Min | 9198ms | 9675ms | Fastest response time |
| Max | 69527ms | 31255ms | Slowest response time |
PG
| Metric | GLM 4.6 | DeepSeek R1 | Description |
|---|---|---|---|
| Quality Metrics | |||
| Correctness | 4.83 | 4.83 | Factual accuracy of responses |
| Faithfulness | 4.89 | 4.83 | Adherence to source material |
| Grounding | 4.83 | 4.78 | Citations and context usage |
| Relevance | 5.00 | 4.83 | Query alignment and focus |
| Completeness | 4.50 | 4.67 | Coverage of all aspects |
| Overall | 4.81 | 4.79 | Average across all metrics |
| Latency Metrics | |||
| Mean | 36774ms | 23334ms | Average response time |
| Min | 9584ms | 12280ms | Fastest response time |
| Max | 104257ms | 85633ms | Slowest response time |
SciFact
| Metric | GLM 4.6 | DeepSeek R1 | 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 | 4.91 | 4.91 | Coverage of all aspects |
| Overall | 4.98 | 4.98 | Average across all metrics |
| Latency Metrics | |||
| Mean | 27880ms | 14826ms | Average response time |
| Min | 3248ms | 7765ms | Fastest response time |
| Max | 68513ms | 33129ms | Slowest response time |
Explore More
Compare more LLMs
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