Model comparison
GPT-5.5 vs Olmo 2 0325 32b Instruct
GPT-5.5 is the stronger model overall, scoring 63.4 to 32.2 on the Noometry Index.
Last verified . 11 shared benchmarks.
Allen Institute for AI (Ai2)Summary
- They share 11 benchmarks with published results for both. GPT-5.5 scores higher in 8 categories and Olmo 2 0325 32b Instruct in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.5 leads 81.5 to 24.8.
- Olmo 2 0325 32b Instruct has downloadable open weights; the other is API-only.
Side by side
| GPT-5.5 | Olmo 2 0325 32b Instruct | |
|---|---|---|
| Provider | OpenAI | Allen Institute for AI (Ai2) |
| Noometry Index | 63.4 | 32.2 |
| Released | 2026-04-23 | — |
| Weights | Proprietary | Open |
| Context window | 1.05M | — |
| Max output | 128K | — |
| Input $ / M tokens | $5 | — |
| Output $ / M tokens | $30 | — |
| Results tracked | 71 | 16 |
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Category by category
Coding GPT-5.5 leads
GPT-5.5: 58.3 (#13), Olmo 2 0325 32b Instruct: 34.6 (#222)
| Benchmark | GPT-5.5 | Olmo 2 0325 32b Instruct |
|---|---|---|
| LMArena Coding | 1494 | 1210 |
| 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
GPT-5.5: 50.7 (#6), Olmo 2 0325 32b Instruct: —
| Benchmark | GPT-5.5 | Olmo 2 0325 32b Instruct |
|---|---|---|
| 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
GPT-5.5: 72.7 (#8), Olmo 2 0325 32b Instruct: 22.6 (#174)
| Benchmark | GPT-5.5 | Olmo 2 0325 32b Instruct |
|---|---|---|
| LMArena Hard Prompts | 1489 | 1208 |
| 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 | — |
| Epoch Capabilities Index | 159.25 | — |
| ForecastBench | 60.6 | — |
Math GPT-5.5 leads
GPT-5.5: 81.5 (#9), Olmo 2 0325 32b Instruct: 24.8 (#255)
| Benchmark | GPT-5.5 | Olmo 2 0325 32b Instruct |
|---|---|---|
| LMArena Math | 1486 | 1208 |
| 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% | — |
| Omni-MATH | — | 16.1% |
| FrontierMath (Feb 2025 set) | 51.7% | — |
| FrontierMath Erdős | 0% | — |
| FrontierMath Tier 4 (v1) | 35.4% | — |
Knowledge GPT-5.5 leads
GPT-5.5: 64.4 (#14), Olmo 2 0325 32b Instruct: 19.2 (#274)
| Benchmark | GPT-5.5 | Olmo 2 0325 32b Instruct |
|---|---|---|
| GPQA Diamond | 94% | — |
| SimpleQA Verified | 63% | — |
| MMLU-Pro | — | 41.4% |
| Vectara Hallucination Rate | 9.3% | — |
| GPQA (HELM) | — | 28.7% |
| LMArena Expert | 1508 | — |
Multimodal Not comparable
GPT-5.5: 47.1 (#11), Olmo 2 0325 32b Instruct: —
| Benchmark | GPT-5.5 | Olmo 2 0325 32b Instruct |
|---|---|---|
| LMArena Vision | 1297 | — |
| Blueprint-Bench 2 | 36.2% | — |
| Furniture Assembly | 44.2% | — |
| LMArena Document | 1486 | — |
Multilingual GPT-5.5 leads
GPT-5.5: 56.5 (#19), Olmo 2 0325 32b Instruct: 35.0 (#243)
| Benchmark | GPT-5.5 | Olmo 2 0325 32b Instruct |
|---|---|---|
| LMArena Non-English | 1467 | 1160 |
| LMArena Chinese | 1535 | 1192 |
| LMArena Russian | 1473 | 1187 |
| LMArena French | 1486 | — |
| LMArena German | 1478 | — |
| LMArena Japanese | 1498 | — |
| LMArena Korean | 1461 | — |
| LMArena Spanish | 1469 | — |
Instruction Following GPT-5.5 leads
GPT-5.5: 77.7 (#18), Olmo 2 0325 32b Instruct: 61.8 (#238)
| Benchmark | GPT-5.5 | Olmo 2 0325 32b Instruct |
|---|---|---|
| LMArena Instruction Following | 1479 | 1186 |
| IFEval | — | 78% |
Long Context GPT-5.5 leads
GPT-5.5: 48.3 (#12), Olmo 2 0325 32b Instruct: 36.2 (#229)
| Benchmark | GPT-5.5 | Olmo 2 0325 32b Instruct |
|---|---|---|
| LMArena Longer Query | 1484 | 1194 |
| CL-bench Life | 22.2% | — |
Writing & Preference GPT-5.5 leads
GPT-5.5: 72.8 (#13), Olmo 2 0325 32b Instruct: 42.3 (#231)
| Benchmark | GPT-5.5 | Olmo 2 0325 32b Instruct |
|---|---|---|
| LMArena Text | 1471 | 1218 |
| LMArena Creative Writing | 1455 | 1199 |
| LMArena Multi-Turn | 1477 | 1221 |
| EQ-Bench Creative Writing | 1844 | — |
| WildBench | — | 73.4% |
| EQ-Bench 4 | 1315 | — |
Frequently asked questions
Is GPT-5.5 better than Olmo 2 0325 32b Instruct?
GPT-5.5 is the stronger model overall, scoring 63.4 to 32.2 on the Noometry Index.
Is GPT-5.5 or Olmo 2 0325 32b Instruct better for coding?
GPT-5.5 scores higher on coding benchmarks: 58.3 versus 34.6 in the Noometry coding category.
How many benchmarks do GPT-5.5 and Olmo 2 0325 32b Instruct share?
11 benchmarks have published results for both models. GPT-5.5 has 71 scored results on Noometry and Olmo 2 0325 32b Instruct has 16.