Model comparison
DeepSeek-R1 vs ERNIE 5.0 0110
DeepSeek-R1 and ERNIE 5.0 0110 score almost the same on the Noometry Index (42.3 vs 41.8), so choose on price, context window or the category you care about most.
Last verified . 17 shared benchmarks.
DeepSeek
BaiduSummary
- They share 17 benchmarks with published results for both. DeepSeek-R1 scores higher in 5 categories and ERNIE 5.0 0110 in 3 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where DeepSeek-R1 leads 44.5 to 39.8.
Side by side
| DeepSeek-R1 | ERNIE 5.0 0110 | |
|---|---|---|
| Provider | DeepSeek | Baidu |
| Noometry Index | 42.3 | 41.8 |
| Released | 2025-01-20 | — |
| Weights | Proprietary | Proprietary |
| Context window | 164K | — |
| Max output | 64K | — |
| Input $ / M tokens | $0.50 | — |
| Output $ / M tokens | $2.15 | — |
| Results tracked | 52 | 20 |
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Category by category
Coding DeepSeek-R1 leads
DeepSeek-R1: 46.3 (#68), ERNIE 5.0 0110: 43.0 (#94)
| Benchmark | DeepSeek-R1 | ERNIE 5.0 0110 |
|---|---|---|
| LMArena Coding | 1427 | 1455 |
| Aider Polyglot | 71.4% | — |
| SciCode | 35.7% | — |
| WeirdML | 41.6% | — |
| LiveBench Coding | 66.7% | — |
| ALE-Bench | 804.12 | — |
| AlgoTune | 1.7 | — |
Agentic & Tool Use Not comparable
DeepSeek-R1: 30.7 (#75), ERNIE 5.0 0110: —
| Benchmark | DeepSeek-R1 | ERNIE 5.0 0110 |
|---|---|---|
| DeepResearch Bench | 35.1% | — |
| BALROG | 34.9% | — |
| METR Time Horizons | 53.8% | — |
Reasoning DeepSeek-R1 leads
DeepSeek-R1: 18.6 (#278), ERNIE 5.0 0110: 17.0 (#297)
| Benchmark | DeepSeek-R1 | ERNIE 5.0 0110 |
|---|---|---|
| LMArena Hard Prompts | 1416 | 1445 |
| ARC-AGI-2 | 1.3% | — |
| SimpleBench | 40.8% | — |
| Kagi LLM Benchmark | 69.4% | — |
| NYT Connections (extended) | — | 10.3% |
| ARC-AGI-1 | 21.2% | — |
| CritPt | 1.1% | — |
| Thematic Generalization | — | 41.7% |
| LiveBench Reasoning | 83.2% | — |
| LiveBench Data Analysis | 69.8% | — |
| Epoch Capabilities Index | 141.29 | — |
| ForecastBench | 60 | — |
| LiveBench | 71.6% | — |
Math DeepSeek-R1 leads
DeepSeek-R1: 43.8 (#79), ERNIE 5.0 0110: 39.3 (#110)
| Benchmark | DeepSeek-R1 | ERNIE 5.0 0110 |
|---|---|---|
| LMArena Math | 1400 | 1437 |
| OTIS Mock AIME 2024-2025 | 66.4% | — |
| Omni-MATH | 42.4% | — |
| LiveBench Math | 80.7% | — |
| MATH Level 5 | 96.6% | — |
Knowledge DeepSeek-R1 leads
DeepSeek-R1: 44.5 (#87), ERNIE 5.0 0110: 39.8 (#128)
| Benchmark | DeepSeek-R1 | ERNIE 5.0 0110 |
|---|---|---|
| LMArena Expert | 1394 | 1428 |
| GPQA Diamond | 76.3% | — |
| MMLU-Pro | 79.3% | — |
| Confabulations | 12.7% | — |
| Vectara Hallucination Rate | 11.3% | — |
| GPQA (HELM) | 66.6% | — |
Multimodal Not comparable
DeepSeek-R1: —, ERNIE 5.0 0110: 39.9 (#53)
| Benchmark | DeepSeek-R1 | ERNIE 5.0 0110 |
|---|---|---|
| LMArena Vision | — | 1249 |
Multilingual ERNIE 5.0 0110 leads
DeepSeek-R1: 52.4 (#85), ERNIE 5.0 0110: 54.1 (#49)
| Benchmark | DeepSeek-R1 | ERNIE 5.0 0110 |
|---|---|---|
| LMArena Non-English | 1412 | 1436 |
| LMArena Chinese | 1442 | 1512 |
| LMArena French | 1417 | 1467 |
| LMArena German | 1404 | 1460 |
| LMArena Japanese | 1391 | 1382 |
| LMArena Korean | 1360 | 1406 |
| LMArena Russian | 1423 | 1446 |
| LMArena Spanish | 1411 | 1473 |
Instruction Following ERNIE 5.0 0110 leads
DeepSeek-R1: 72.0 (#143), ERNIE 5.0 0110: 74.5 (#92)
| Benchmark | DeepSeek-R1 | ERNIE 5.0 0110 |
|---|---|---|
| LMArena Instruction Following | 1382 | 1413 |
| LiveBench Instruction Following | 80.5% | — |
| IFEval | 78.4% | — |
Long Context DeepSeek-R1 leads
DeepSeek-R1: 45.4 (#36), ERNIE 5.0 0110: 43.4 (#95)
| Benchmark | DeepSeek-R1 | ERNIE 5.0 0110 |
|---|---|---|
| LMArena Longer Query | 1391 | 1422 |
| Fiction.LiveBench | 75% | — |
Writing & Preference ERNIE 5.0 0110 leads
DeepSeek-R1: 61.4 (#88), ERNIE 5.0 0110: 63.1 (#66)
| Benchmark | DeepSeek-R1 | ERNIE 5.0 0110 |
|---|---|---|
| LMArena Text | 1428 | 1445 |
| LMArena Creative Writing | 1405 | 1426 |
| LMArena Multi-Turn | 1405 | 1434 |
| Short-Story Creative Writing | 83% | — |
| EQ-Bench Creative Writing | 1500 | — |
| WildBench | 82.8% | — |
| LiveBench Language | 48.5% | — |
Frequently asked questions
Is DeepSeek-R1 better than ERNIE 5.0 0110?
DeepSeek-R1 and ERNIE 5.0 0110 score almost the same on the Noometry Index (42.3 vs 41.8), so choose on price, context window or the category you care about most.
Is DeepSeek-R1 or ERNIE 5.0 0110 better for coding?
DeepSeek-R1 scores higher on coding benchmarks: 46.3 versus 43.0 in the Noometry coding category.
How many benchmarks do DeepSeek-R1 and ERNIE 5.0 0110 share?
17 benchmarks have published results for both models. DeepSeek-R1 has 52 scored results on Noometry and ERNIE 5.0 0110 has 20.