Model comparison
DeepSeek-R1 vs Mercury
DeepSeek-R1 is the stronger model overall, scoring 42.3 to 37.6 on the Noometry Index.
Last verified . 9 shared benchmarks.
DeepSeekSummary
- They share 9 benchmarks with published results for both. DeepSeek-R1 scores higher in 6 categories and Mercury in 0 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where DeepSeek-R1 leads 61.4 to 46.2.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 69.4% for DeepSeek-R1 and 21.6% for Mercury.
Side by side
| DeepSeek-R1 | Mercury | |
|---|---|---|
| Provider | DeepSeek | Inception |
| Noometry Index | 42.3 | 37.6 |
| 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 | 9 |
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Category by category
Coding DeepSeek-R1 leads
DeepSeek-R1: 46.3 (#68), Mercury: 38.7 (#170)
| Benchmark | DeepSeek-R1 | Mercury |
|---|---|---|
| LMArena Coding | 1427 | 1322 |
| 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), Mercury: —
| Benchmark | DeepSeek-R1 | Mercury |
|---|---|---|
| DeepResearch Bench | 35.1% | — |
| BALROG | 34.9% | — |
| METR Time Horizons | 53.8% | — |
Reasoning DeepSeek-R1 leads
DeepSeek-R1: 18.6 (#278), Mercury: 17.5 (#293)
| Benchmark | DeepSeek-R1 | Mercury |
|---|---|---|
| Kagi LLM Benchmark | 69.4% | 21.6% |
| LMArena Hard Prompts | 1416 | 1285 |
| ARC-AGI-2 | 1.3% | — |
| SimpleBench | 40.8% | — |
| ARC-AGI-1 | 21.2% | — |
| CritPt | 1.1% | — |
| LiveBench Reasoning | 83.2% | — |
| LiveBench Data Analysis | 69.8% | — |
| Epoch Capabilities Index | 141.29 | — |
| ForecastBench | 60 | — |
| LiveBench | 71.6% | — |
Math Not comparable
DeepSeek-R1: 43.8 (#79), Mercury: —
| Benchmark | DeepSeek-R1 | Mercury |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 66.4% | — |
| Omni-MATH | 42.4% | — |
| LiveBench Math | 80.7% | — |
| LMArena Math | 1400 | — |
| MATH Level 5 | 96.6% | — |
Knowledge Not comparable
DeepSeek-R1: 44.5 (#87), Mercury: —
| Benchmark | DeepSeek-R1 | Mercury |
|---|---|---|
| GPQA Diamond | 76.3% | — |
| MMLU-Pro | 79.3% | — |
| Confabulations | 12.7% | — |
| Vectara Hallucination Rate | 11.3% | — |
| GPQA (HELM) | 66.6% | — |
| LMArena Expert | 1394 | — |
Multilingual DeepSeek-R1 leads
DeepSeek-R1: 52.4 (#85), Mercury: 41.6 (#206)
| Benchmark | DeepSeek-R1 | Mercury |
|---|---|---|
| LMArena Non-English | 1412 | 1260 |
| LMArena Chinese | 1442 | — |
| LMArena French | 1417 | — |
| LMArena German | 1404 | — |
| LMArena Japanese | 1391 | — |
| LMArena Korean | 1360 | — |
| LMArena Russian | 1423 | — |
| LMArena Spanish | 1411 | — |
Instruction Following DeepSeek-R1 leads
DeepSeek-R1: 72.0 (#143), Mercury: 65.2 (#224)
| Benchmark | DeepSeek-R1 | Mercury |
|---|---|---|
| LMArena Instruction Following | 1382 | 1239 |
| LiveBench Instruction Following | 80.5% | — |
| IFEval | 78.4% | — |
Long Context DeepSeek-R1 leads
DeepSeek-R1: 45.4 (#36), Mercury: 38.4 (#198)
| Benchmark | DeepSeek-R1 | Mercury |
|---|---|---|
| LMArena Longer Query | 1391 | 1266 |
| Fiction.LiveBench | 75% | — |
Writing & Preference DeepSeek-R1 leads
DeepSeek-R1: 61.4 (#88), Mercury: 46.2 (#221)
| Benchmark | DeepSeek-R1 | Mercury |
|---|---|---|
| LMArena Text | 1428 | 1282 |
| LMArena Creative Writing | 1405 | 1191 |
| LMArena Multi-Turn | 1405 | 1282 |
| 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 Mercury?
DeepSeek-R1 is the stronger model overall, scoring 42.3 to 37.6 on the Noometry Index.
Is DeepSeek-R1 or Mercury better for coding?
DeepSeek-R1 scores higher on coding benchmarks: 46.3 versus 38.7 in the Noometry coding category.
How many benchmarks do DeepSeek-R1 and Mercury share?
9 benchmarks have published results for both models. DeepSeek-R1 has 52 scored results on Noometry and Mercury has 9.