Model comparison
Dolly 2.0-12b vs GPT-5.5
GPT-5.5 is the stronger model overall, scoring 63.4 to 24.9 on the Noometry Index.
Last verified . 10 shared benchmarks.
DatabricksSummary
- They share 10 benchmarks with published results for both. Dolly 2.0-12b scores higher in 0 categories and GPT-5.5 in 6 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-5.5 leads 72.7 to 14.6.
- Dolly 2.0-12b has downloadable open weights; the other is API-only.
Side by side
| Dolly 2.0-12b | GPT-5.5 | |
|---|---|---|
| Provider | Databricks | OpenAI |
| Noometry Index | 24.9 | 63.4 |
| Released | 2023-04-11 | 2026-04-23 |
| Weights | Open | Proprietary |
| Context window | — | 1.05M |
| Max output | — | 128K |
| Input $ / M tokens | — | $5 |
| Output $ / M tokens | — | $30 |
| Results tracked | 17 | 71 |
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Category by category
Coding GPT-5.5 leads
Dolly 2.0-12b: 23.0 (#327), GPT-5.5: 58.3 (#13)
| Benchmark | Dolly 2.0-12b | GPT-5.5 |
|---|---|---|
| LMArena Coding | 776 | 1494 |
| SWE-bench Verified | — | 80.6% |
| DeepSWE | — | 67% |
| FrontierCode | — | 43% |
| LMArena WebDev | — | 1512 |
| SciCode | — | 56.1% |
| GSO | — | 40.2% |
| WeirdML | — | 84.9% |
| MirrorCode | — | 10% |
| ALE-Bench | — | 1,943 |
Agentic & Tool Use Not comparable
Dolly 2.0-12b: —, GPT-5.5: 50.7 (#6)
| Benchmark | Dolly 2.0-12b | GPT-5.5 |
|---|---|---|
| Terminal-Bench | — | 84.7% |
| APEX-Agents | — | 55.1% |
| OSWorld 2.0 | — | 13% |
| Remote Labor Index | — | 6.3% |
| τ²-bench Banking | — | 44.6% |
| DeepResearch Bench | — | 54% |
| PostTrainBench | — | 27.2% |
| ExploitBench | — | 47.4% |
| GBAEval | — | 53.2% |
| GDP.pdf | — | 26% |
| LMArena Search | — | 1242 |
| Vending-Bench 2 | — | 7,524 |
Reasoning GPT-5.5 leads
Dolly 2.0-12b: 14.6 (#315), GPT-5.5: 72.7 (#8)
| Benchmark | Dolly 2.0-12b | GPT-5.5 |
|---|---|---|
| LMArena Hard Prompts | 804 | 1489 |
| Epoch Capabilities Index | 88.94 | 159.25 |
| ARC-AGI-2 | — | 85% |
| SimpleBench | — | 69% |
| Kagi LLM Benchmark | — | 88.8% |
| NYT Connections (extended) | — | 96.2% |
| ARC-AGI-1 | — | 95% |
| CritPt | — | 27.1% |
| Chess Puzzles | — | 54% |
| EBR-Bench | — | 34.3% |
| Mystery Game Puzzles | — | 56% |
| DTBench | — | 96% |
| LMCA | — | 54.3% |
| Surface Evolver Bench | — | 88.1% |
| Bench to the Future 3 | — | 0.14 |
| ForecastBench | — | 60.6 |
| HellaSwag | 70.8% | — |
| PIQA | 75.4% | — |
| WinoGrande | 61.8% | — |
Math GPT-5.5 leads
Dolly 2.0-12b: 25.0 (#250), GPT-5.5: 81.5 (#9)
| Benchmark | Dolly 2.0-12b | GPT-5.5 |
|---|---|---|
| LMArena Math | 871 | 1486 |
| FrontierMath (Tiers 1-3) | — | 85.3% |
| FrontierMath Tier 4 | — | 72.5% |
| MathArena Final-Answer Competitions | — | 94.3% |
| OTIS Mock AIME 2024-2025 | — | 100% |
| ProofBench | — | 50% |
| FrontierMath (Feb 2025 set) | — | 51.7% |
| FrontierMath Erdős | — | 0% |
| FrontierMath Tier 4 (v1) | — | 35.4% |
Knowledge Not comparable
Dolly 2.0-12b: —, GPT-5.5: 64.4 (#14)
| Benchmark | Dolly 2.0-12b | GPT-5.5 |
|---|---|---|
| GPQA Diamond | — | 94% |
| SimpleQA Verified | — | 63% |
| Vectara Hallucination Rate | — | 9.3% |
| LMArena Expert | — | 1508 |
| ARC (AI2) Challenge | 39.6% | — |
| BoolQ | 56.3% | — |
| MMLU | 26.2% | — |
| OpenBookQA | 39.2% | — |
Multimodal Not comparable
Dolly 2.0-12b: —, GPT-5.5: 47.1 (#11)
| Benchmark | Dolly 2.0-12b | GPT-5.5 |
|---|---|---|
| LMArena Vision | — | 1297 |
| Blueprint-Bench 2 | — | 36.2% |
| Furniture Assembly | — | 44.2% |
| LMArena Document | — | 1486 |
Multilingual GPT-5.5 leads
Dolly 2.0-12b: 17.4 (#291), GPT-5.5: 56.5 (#19)
| Benchmark | Dolly 2.0-12b | GPT-5.5 |
|---|---|---|
| LMArena Non-English | 836 | 1467 |
| LMArena Chinese | 836 | 1535 |
| LMArena French | — | 1486 |
| LMArena German | — | 1478 |
| LMArena Japanese | — | 1498 |
| LMArena Korean | — | 1461 |
| LMArena Russian | — | 1473 |
| LMArena Spanish | — | 1469 |
Instruction Following GPT-5.5 leads
Dolly 2.0-12b: 38.9 (#299), GPT-5.5: 77.7 (#18)
| Benchmark | Dolly 2.0-12b | GPT-5.5 |
|---|---|---|
| LMArena Instruction Following | 814 | 1479 |
Long Context Not comparable
Dolly 2.0-12b: —, GPT-5.5: 48.3 (#12)
| Benchmark | Dolly 2.0-12b | GPT-5.5 |
|---|---|---|
| CL-bench Life | — | 22.2% |
| LMArena Longer Query | — | 1484 |
Writing & Preference GPT-5.5 leads
Dolly 2.0-12b: 15.3 (#306), GPT-5.5: 72.8 (#13)
| Benchmark | Dolly 2.0-12b | GPT-5.5 |
|---|---|---|
| LMArena Text | 851 | 1471 |
| LMArena Creative Writing | 864 | 1455 |
| LMArena Multi-Turn | 740 | 1477 |
| EQ-Bench Creative Writing | — | 1844 |
| EQ-Bench 4 | — | 1315 |
Frequently asked questions
Is Dolly 2.0-12b better than GPT-5.5?
GPT-5.5 is the stronger model overall, scoring 63.4 to 24.9 on the Noometry Index.
Is Dolly 2.0-12b or GPT-5.5 better for coding?
GPT-5.5 scores higher on coding benchmarks: 58.3 versus 23.0 in the Noometry coding category.
How many benchmarks do Dolly 2.0-12b and GPT-5.5 share?
10 benchmarks have published results for both models. Dolly 2.0-12b has 17 scored results on Noometry and GPT-5.5 has 71.