Model comparison
Gemma 2 27B vs GPT-5.6 Sol
GPT-5.6 Sol is the stronger model overall, scoring 65.0 to 29.4 on the Noometry Index. Gemma 2 27B costs 12× less per token, which makes it the better buy when GPT-5.6 Sol's lead doesn't matter for your workload.
Last verified . 22 shared benchmarks.
GoogleSummary
- They share 22 benchmarks with published results for both. Gemma 2 27B scores higher in 0 categories and GPT-5.6 Sol in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.6 Sol leads 85.6 to 10.7.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 1.4% for Gemma 2 27B and 100% for GPT-5.6 Sol.
- Gemma 2 27B is cheaper at $0.65 / $0.65 per million input/output tokens, against $4 / $20 for GPT-5.6 Sol.
- GPT-5.6 Sol accepts more context: 1.05M tokens versus 8K.
- Gemma 2 27B has downloadable open weights; the other is API-only.
Side by side
| Gemma 2 27B | GPT-5.6 Sol | |
|---|---|---|
| Provider | OpenAI | |
| Noometry Index | 29.4 | 65.0 |
| Released | 2024-06-24 | 2026-07-09 |
| Weights | Open | Proprietary |
| Context window | 8K | 1.05M |
| Max output | 2K | 128K |
| Input $ / M tokens | $0.65 | $4 |
| Output $ / M tokens | $0.65 | $20 |
| Results tracked | 34 | 65 |
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Category by category
Coding GPT-5.6 Sol leads
Gemma 2 27B: 34.1 (#246), GPT-5.6 Sol: 65.1 (#7)
| Benchmark | Gemma 2 27B | GPT-5.6 Sol |
|---|---|---|
| LMArena Coding | 1211 | 1498 |
| DeepSWE | — | 72.7% |
| FrontierCode | — | 47.5% |
| CursorBench | — | 41.7% |
| LMArena WebDev | — | 1618 |
| FrontierSWE | — | 32.2% |
| SciCode | — | 57.1% |
| GSO | — | 76.5% |
| WeirdML | — | 89.4% |
| BigCodeBench Instruct | 42.8% | — |
| LiveBench Coding | 36% | — |
| MirrorCode | — | 20% |
| BigCodeBench Complete | 52.5% | — |
| ALE-Bench | — | 2,177 |
Agentic & Tool Use Not comparable
Gemma 2 27B: —, GPT-5.6 Sol: 50.3 (#7)
| Benchmark | Gemma 2 27B | GPT-5.6 Sol |
|---|---|---|
| APEX-Agents | — | 51.4% |
| OSWorld 2.0 | — | 27.3% |
| τ²-bench Banking | — | 46.9% |
| PostTrainBench | — | 36.2% |
| BALROG | — | 60% |
| GBAEval | — | 52.6% |
| GDP.pdf | — | 30.7% |
| LMArena Search | — | 1257 |
| Vending-Bench 2 | — | 9,619 |
Reasoning GPT-5.6 Sol leads
Gemma 2 27B: 15.3 (#315), GPT-5.6 Sol: 74.8 (#8)
| Benchmark | Gemma 2 27B | GPT-5.6 Sol |
|---|---|---|
| LMArena Hard Prompts | 1198 | 1484 |
| DTBench | 48% | 96% |
| LMCA | 7.1% | 59.2% |
| Epoch Capabilities Index | 122.08 | 161.66 |
| ARC-AGI-2 | — | 92.5% |
| SimpleBench | — | 71.7% |
| Kagi LLM Benchmark | — | 67% |
| NYT Connections (extended) | — | 93.8% |
| ARC-AGI-1 | — | 97.5% |
| CritPt | — | 32.3% |
| Chess Puzzles | — | 64% |
| EnigmaEval | — | 37.1% |
| EBR-Bench | — | 44.8% |
| LiveBench Reasoning | 28.1% | — |
| Mystery Game Puzzles | — | 58% |
| LiveBench Data Analysis | 47.9% | — |
| Surface Evolver Bench | — | 93.1% |
| Bench to the Future 3 | — | 0.14 |
| LiveBench | 38.2% | — |
Math GPT-5.6 Sol leads
Gemma 2 27B: 10.7 (#311), GPT-5.6 Sol: 85.6 (#9)
| Benchmark | Gemma 2 27B | GPT-5.6 Sol |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 1.4% | 100% |
| LMArena Math | 1212 | 1474 |
| FrontierMath (Tiers 1-3) | — | 89.1% |
| FrontierMath Tier 4 | — | 82.9% |
| ProofBench | — | 83% |
| LiveBench Math | 26.5% | — |
| MATH Level 5 | 27.9% | — |
| FrontierMath Erdős | — | 0% |
Knowledge GPT-5.6 Sol leads
Gemma 2 27B: 19.0 (#280), GPT-5.6 Sol: 64.3 (#18)
| Benchmark | Gemma 2 27B | GPT-5.6 Sol |
|---|---|---|
| GPQA Diamond | 36.5% | 93.5% |
| LMArena Expert | 1172 | 1516 |
| SimpleQA Verified | — | 69.7% |
| Confabulations | 27.1% | — |
| Vectara Hallucination Rate | — | 12.4% |
| MMLU | 75.7% | — |
Multimodal Not comparable
Gemma 2 27B: —, GPT-5.6 Sol: 48.6 (#9)
| Benchmark | Gemma 2 27B | GPT-5.6 Sol |
|---|---|---|
| LMArena Vision | — | 1281 |
| Blueprint-Bench 2 | — | 33.6% |
| Furniture Assembly | — | 56.7% |
| LMArena Document | — | 1483 |
Multilingual GPT-5.6 Sol leads
Gemma 2 27B: 38.6 (#226), GPT-5.6 Sol: 55.3 (#32)
| Benchmark | Gemma 2 27B | GPT-5.6 Sol |
|---|---|---|
| LMArena Non-English | 1217 | 1452 |
| LMArena Chinese | 1221 | 1527 |
| LMArena French | 1247 | 1477 |
| LMArena German | 1209 | 1476 |
| LMArena Japanese | 1175 | 1471 |
| LMArena Korean | 1174 | 1442 |
| LMArena Russian | 1234 | 1468 |
| LMArena Spanish | 1228 | 1441 |
Instruction Following GPT-5.6 Sol leads
Gemma 2 27B: 60.5 (#249), GPT-5.6 Sol: 77.7 (#16)
| Benchmark | Gemma 2 27B | GPT-5.6 Sol |
|---|---|---|
| LMArena Instruction Following | 1206 | 1482 |
| LiveBench Instruction Following | 58.1% | — |
Long Context GPT-5.6 Sol leads
Gemma 2 27B: 37.3 (#218), GPT-5.6 Sol: 45.4 (#42)
| Benchmark | Gemma 2 27B | GPT-5.6 Sol |
|---|---|---|
| LMArena Longer Query | 1231 | 1480 |
Writing & Preference GPT-5.6 Sol leads
Gemma 2 27B: 44.2 (#225), GPT-5.6 Sol: 73.3 (#12)
| Benchmark | Gemma 2 27B | GPT-5.6 Sol |
|---|---|---|
| LMArena Text | 1231 | 1457 |
| LMArena Creative Writing | 1241 | 1448 |
| LMArena Multi-Turn | 1224 | 1460 |
| EQ-Bench Creative Writing | — | 1972 |
| EQ-Bench 4 | — | 1250 |
| LiveBench Language | 32.6% | — |
Frequently asked questions
Is Gemma 2 27B better than GPT-5.6 Sol?
GPT-5.6 Sol is the stronger model overall, scoring 65.0 to 29.4 on the Noometry Index. Gemma 2 27B costs 12× less per token, which makes it the better buy when GPT-5.6 Sol's lead doesn't matter for your workload.
Which is cheaper, Gemma 2 27B or GPT-5.6 Sol?
Gemma 2 27B is cheaper. It lists at $0.65 per million input tokens and $0.65 per million output tokens; GPT-5.6 Sol lists at $4 and $20.
Is Gemma 2 27B or GPT-5.6 Sol better for coding?
GPT-5.6 Sol scores higher on coding benchmarks: 65.1 versus 34.1 in the Noometry coding category.
Which has the bigger context window?
GPT-5.6 Sol does, with 1.05M tokens against 8K.
How many benchmarks do Gemma 2 27B and GPT-5.6 Sol share?
22 benchmarks have published results for both models. Gemma 2 27B has 34 scored results on Noometry and GPT-5.6 Sol has 65.