Model comparison
DeepSeek-V3.1 vs Dolly 2.0-12b
DeepSeek-V3.1 is the stronger model overall, scoring 42.2 to 24.9 on the Noometry Index.
Last verified . 10 shared benchmarks.
DeepSeek
DatabricksSummary
- They share 10 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 6 categories and Dolly 2.0-12b in 0 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where DeepSeek-V3.1 leads 60.5 to 15.3.
Side by side
| DeepSeek-V3.1 | Dolly 2.0-12b | |
|---|---|---|
| Provider | DeepSeek | Databricks |
| Noometry Index | 42.2 | 24.9 |
| Released | 2025-08-21 | 2023-04-11 |
| Weights | Open | Open |
| Context window | 164K | — |
| Max output | 8K | — |
| Input $ / M tokens | $0.25 | — |
| Output $ / M tokens | $0.95 | — |
| Results tracked | 27 | 17 |
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Category by category
Coding DeepSeek-V3.1 leads
DeepSeek-V3.1: 39.8 (#140), Dolly 2.0-12b: 23.0 (#327)
| Benchmark | DeepSeek-V3.1 | Dolly 2.0-12b |
|---|---|---|
| LMArena Coding | 1417 | 776 |
| WeirdML | 38.4% | — |
Reasoning DeepSeek-V3.1 leads
DeepSeek-V3.1: 27.3 (#109), Dolly 2.0-12b: 14.6 (#315)
| Benchmark | DeepSeek-V3.1 | Dolly 2.0-12b |
|---|---|---|
| LMArena Hard Prompts | 1417 | 804 |
| Epoch Capabilities Index | 139.92 | 88.94 |
| SimpleBench | 40% | — |
| Kagi LLM Benchmark | 53.2% | — |
| DTBench | 82.7% | — |
| LMCA | 24.3% | — |
| ForecastBench | 58 | — |
| HellaSwag | — | 70.8% |
| PIQA | — | 75.4% |
| WinoGrande | — | 61.8% |
Math DeepSeek-V3.1 leads
DeepSeek-V3.1: 36.1 (#125), Dolly 2.0-12b: 25.0 (#250)
| Benchmark | DeepSeek-V3.1 | Dolly 2.0-12b |
|---|---|---|
| LMArena Math | 1420 | 871 |
Knowledge Not comparable
DeepSeek-V3.1: 43.0 (#86), Dolly 2.0-12b: —
| Benchmark | DeepSeek-V3.1 | Dolly 2.0-12b |
|---|---|---|
| Vectara Hallucination Rate | 5.5% | — |
| LMArena Expert | 1403 | — |
| ARC (AI2) Challenge | — | 39.6% |
| BoolQ | — | 56.3% |
| MMLU | — | 26.2% |
| OpenBookQA | — | 39.2% |
Multilingual DeepSeek-V3.1 leads
DeepSeek-V3.1: 51.8 (#100), Dolly 2.0-12b: 17.4 (#291)
| Benchmark | DeepSeek-V3.1 | Dolly 2.0-12b |
|---|---|---|
| LMArena Non-English | 1400 | 836 |
| LMArena Chinese | 1470 | 836 |
| LMArena French | 1447 | — |
| LMArena German | 1412 | — |
| LMArena Japanese | 1374 | — |
| LMArena Korean | 1337 | — |
| LMArena Russian | 1405 | — |
| LMArena Spanish | 1429 | — |
Instruction Following DeepSeek-V3.1 leads
DeepSeek-V3.1: 74.1 (#105), Dolly 2.0-12b: 38.9 (#299)
| Benchmark | DeepSeek-V3.1 | Dolly 2.0-12b |
|---|---|---|
| LMArena Instruction Following | 1400 | 814 |
Long Context Not comparable
DeepSeek-V3.1: 36.3 (#227), Dolly 2.0-12b: —
| Benchmark | DeepSeek-V3.1 | Dolly 2.0-12b |
|---|---|---|
| Fiction.LiveBench | 52.8% | — |
| LMArena Longer Query | 1422 | — |
Writing & Preference DeepSeek-V3.1 leads
DeepSeek-V3.1: 60.5 (#93), Dolly 2.0-12b: 15.3 (#306)
| Benchmark | DeepSeek-V3.1 | Dolly 2.0-12b |
|---|---|---|
| LMArena Text | 1420 | 851 |
| LMArena Creative Writing | 1401 | 864 |
| LMArena Multi-Turn | 1408 | 740 |
| EQ-Bench Creative Writing | 1436 | — |
Frequently asked questions
Is DeepSeek-V3.1 better than Dolly 2.0-12b?
DeepSeek-V3.1 is the stronger model overall, scoring 42.2 to 24.9 on the Noometry Index.
Is DeepSeek-V3.1 or Dolly 2.0-12b better for coding?
DeepSeek-V3.1 scores higher on coding benchmarks: 39.8 versus 23.0 in the Noometry coding category.
How many benchmarks do DeepSeek-V3.1 and Dolly 2.0-12b share?
10 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Dolly 2.0-12b has 17.