GLM 4.6 vs GPT-OSS 120B
Detailed comparison between GLM 4.6 and GPT-OSS 120B 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 GPT-OSS 120B are powerful language models designed for RAG applications. However, their performance characteristics differ in important ways.
Why GLM 4.6:
- GLM 4.6 has 213 higher ELO rating
- GLM 4.6 delivers better overall quality (4.93 vs 4.80)
- GLM 4.6 has a 20.4% higher win rate
Overview
Key metrics
ELO Rating
Overall ranking quality
GLM 4.6
GPT-OSS 120B
Win Rate
Head-to-head performance
GLM 4.6
GPT-OSS 120B
Quality Score
Overall quality metric
GLM 4.6
GPT-OSS 120B
Average Latency
Response time
GLM 4.6
GPT-OSS 120B
LLMs Are Just One Piece of RAG
Agentset gives you a managed RAG pipeline with the top-ranked models and best practices baked in. No infrastructure to maintain, no LLM orchestration to manage.
Trusted by teams building production RAG applications
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 | GPT-OSS 120B | Description |
|---|---|---|---|
| Overall Performance | |||
| ELO Rating | 1487 | 1274 | Overall ranking quality based on pairwise comparisons |
| Win Rate | 37.0% | 16.6% | Percentage of comparisons won against other models |
| Quality Score | 4.93 | 4.80 | Average quality across all RAG metrics |
| Pricing & Context | |||
| Input Price per 1M | $0.40 | $0.04 | Cost per million input tokens |
| Output Price per 1M | $1.75 | $0.19 | Cost per million output tokens |
| Context Window | 203K | 131K | Maximum context window size |
| Release Date | 2025-09-30 | 2025-08-05 | Model release date |
| Performance Metrics | |||
| Avg Latency | 33.1s | 11.2s | Average response time across all datasets |
Build RAG in Minutes, Not Months
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 | GPT-OSS 120B | 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 | 5616ms | Average response time |
| Min | 9198ms | 1255ms | Fastest response time |
| Max | 69527ms | 20330ms | Slowest response time |
PG
| Metric | GLM 4.6 | GPT-OSS 120B | Description |
|---|---|---|---|
| Quality Metrics | |||
| Correctness | 4.83 | 4.78 | Factual accuracy of responses |
| Faithfulness | 4.89 | 4.78 | 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 | 4.72 | Coverage of all aspects |
| Overall | 4.81 | 4.78 | Average across all metrics |
| Latency Metrics | |||
| Mean | 36774ms | 19128ms | Average response time |
| Min | 9584ms | 1317ms | Fastest response time |
| Max | 104257ms | 69491ms | Slowest response time |
SciFact
| Metric | GLM 4.6 | GPT-OSS 120B | Description |
|---|---|---|---|
| Quality Metrics | |||
| Correctness | 5.00 | 4.64 | Factual accuracy of responses |
| Faithfulness | 5.00 | 4.64 | Adherence to source material |
| Grounding | 5.00 | 4.64 | Citations and context usage |
| Relevance | 5.00 | 4.64 | Query alignment and focus |
| Completeness | 4.91 | 4.55 | Coverage of all aspects |
| Overall | 4.98 | 4.62 | Average across all metrics |
| Latency Metrics | |||
| Mean | 27880ms | 8854ms | Average response time |
| Min | 3248ms | 0ms | Fastest response time |
| Max | 68513ms | 35709ms | Slowest response time |
Explore More
Compare more LLMs
See how all LLMs stack up for RAG applications. Compare GPT-5, Claude, Gemini, and more. View comprehensive benchmarks and find the perfect LLM for your needs.