Model comparison
GPT-5.6 Sol vs Grok 3
GPT-5.6 Sol is the stronger model overall, scoring 65.0 to 39.9 on the Noometry Index.
Last verified . 28 shared benchmarks.
Summary
- They share 28 benchmarks with published results for both. GPT-5.6 Sol scores higher in 9 categories and Grok 3 in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-5.6 Sol leads 74.8 to 13.7.
- The biggest single-benchmark swing is ARC-AGI-2: 92.5% for GPT-5.6 Sol and 0% for Grok 3.
Side by side
| GPT-5.6 Sol | Grok 3 | |
|---|---|---|
| Provider | OpenAI | xAI |
| Noometry Index | 65.0 | 39.9 |
| Released | 2026-07-09 | 2025-04-09 |
| Weights | Proprietary | Proprietary |
| Context window | 1.05M | — |
| Max output | 128K | — |
| Input $ / M tokens | $4 | — |
| Output $ / M tokens | $20 | — |
| Results tracked | 65 | 40 |
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Category by category
Coding GPT-5.6 Sol leads
GPT-5.6 Sol: 65.1 (#7), Grok 3: 41.9 (#115)
| Benchmark | GPT-5.6 Sol | Grok 3 |
|---|---|---|
| WeirdML | 89.4% | 37.2% |
| LMArena Coding | 1498 | 1432 |
| DeepSWE | 72.7% | — |
| FrontierCode | 47.5% | — |
| Aider Polyglot | — | 53.3% |
| CursorBench | 41.7% | — |
| LMArena WebDev | 1618 | — |
| FrontierSWE | 32.2% | — |
| SciCode | 57.1% | — |
| GSO | 76.5% | — |
| MirrorCode | 20% | — |
| ALE-Bench | 2,177 | — |
Agentic & Tool Use GPT-5.6 Sol leads
GPT-5.6 Sol: 50.3 (#7), Grok 3: 30.5 (#76)
| Benchmark | GPT-5.6 Sol | Grok 3 |
|---|---|---|
| BALROG | 60% | 29.5% |
| APEX-Agents | 51.4% | — |
| OSWorld 2.0 | 27.3% | — |
| τ²-bench Banking | 46.9% | — |
| PostTrainBench | 36.2% | — |
| GBAEval | 52.6% | — |
| GDP.pdf | 30.7% | — |
| LMArena Search | 1257 | — |
| Vending-Bench 2 | 9,619 | — |
Reasoning GPT-5.6 Sol leads
GPT-5.6 Sol: 74.8 (#8), Grok 3: 13.7 (#333)
| Benchmark | GPT-5.6 Sol | Grok 3 |
|---|---|---|
| ARC-AGI-2 | 92.5% | 0% |
| SimpleBench | 71.7% | 36.1% |
| Kagi LLM Benchmark | 67% | 61.3% |
| ARC-AGI-1 | 97.5% | 5.5% |
| LMArena Hard Prompts | 1484 | 1434 |
| Epoch Capabilities Index | 161.66 | 138.33 |
| NYT Connections (extended) | 93.8% | — |
| CritPt | 32.3% | — |
| Chess Puzzles | 64% | — |
| EnigmaEval | 37.1% | — |
| EBR-Bench | 44.8% | — |
| Mystery Game Puzzles | 58% | — |
| DTBench | 96% | — |
| LMCA | 59.2% | — |
| Surface Evolver Bench | 93.1% | — |
| Bench to the Future 3 | 0.14 | — |
Math GPT-5.6 Sol leads
GPT-5.6 Sol: 85.6 (#9), Grok 3: 38.0 (#145)
| Benchmark | GPT-5.6 Sol | Grok 3 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 100% | 55.6% |
| LMArena Math | 1474 | 1391 |
| FrontierMath (Tiers 1-3) | 89.1% | — |
| FrontierMath Tier 4 | 82.9% | — |
| ProofBench | 83% | — |
| Omni-MATH | — | 46.4% |
| MATH Level 5 | — | 88.7% |
| FrontierMath (Feb 2025 set) | — | 3.8% |
| FrontierMath Erdős | 0% | — |
| FrontierMath Tier 4 (v1) | — | 0% |
Knowledge GPT-5.6 Sol leads
GPT-5.6 Sol: 64.3 (#18), Grok 3: 46.2 (#82)
| Benchmark | GPT-5.6 Sol | Grok 3 |
|---|---|---|
| GPQA Diamond | 93.5% | 75.8% |
| Vectara Hallucination Rate | 12.4% | 5.8% |
| LMArena Expert | 1516 | 1421 |
| SimpleQA Verified | 69.7% | — |
| MMLU-Pro | — | 78.8% |
| Confabulations | — | 14.2% |
| GPQA (HELM) | — | 65% |
Multimodal Not comparable
GPT-5.6 Sol: 48.6 (#9), Grok 3: —
| Benchmark | GPT-5.6 Sol | Grok 3 |
|---|---|---|
| LMArena Vision | 1281 | — |
| Blueprint-Bench 2 | 33.6% | — |
| Furniture Assembly | 56.7% | — |
| LMArena Document | 1483 | — |
Multilingual GPT-5.6 Sol leads
GPT-5.6 Sol: 55.3 (#32), Grok 3: 52.3 (#87)
| Benchmark | GPT-5.6 Sol | Grok 3 |
|---|---|---|
| LMArena Non-English | 1452 | 1410 |
| LMArena Chinese | 1527 | 1448 |
| LMArena French | 1477 | 1460 |
| LMArena German | 1476 | 1431 |
| LMArena Japanese | 1471 | 1387 |
| LMArena Korean | 1442 | 1373 |
| LMArena Russian | 1468 | 1416 |
| LMArena Spanish | 1441 | 1417 |
Instruction Following GPT-5.6 Sol leads
GPT-5.6 Sol: 77.7 (#16), Grok 3: 75.0 (#73)
| Benchmark | GPT-5.6 Sol | Grok 3 |
|---|---|---|
| LMArena Instruction Following | 1482 | 1409 |
| IFEval | — | 88.4% |
Long Context GPT-5.6 Sol leads
GPT-5.6 Sol: 45.4 (#42), Grok 3: 38.7 (#192)
| Benchmark | GPT-5.6 Sol | Grok 3 |
|---|---|---|
| LMArena Longer Query | 1480 | 1439 |
| Fiction.LiveBench | — | 58.3% |
Writing & Preference GPT-5.6 Sol leads
GPT-5.6 Sol: 73.3 (#12), Grok 3: 55.8 (#141)
| Benchmark | GPT-5.6 Sol | Grok 3 |
|---|---|---|
| LMArena Text | 1457 | 1426 |
| LMArena Creative Writing | 1448 | 1414 |
| EQ-Bench Creative Writing | 1972 | 1186 |
| LMArena Multi-Turn | 1460 | 1425 |
| Short-Story Creative Writing | — | 76.4% |
| WildBench | — | 84.9% |
| EQ-Bench 4 | 1250 | — |
Frequently asked questions
Is GPT-5.6 Sol better than Grok 3?
GPT-5.6 Sol is the stronger model overall, scoring 65.0 to 39.9 on the Noometry Index.
Is GPT-5.6 Sol or Grok 3 better for coding?
GPT-5.6 Sol scores higher on coding benchmarks: 65.1 versus 41.9 in the Noometry coding category.
How many benchmarks do GPT-5.6 Sol and Grok 3 share?
28 benchmarks have published results for both models. GPT-5.6 Sol has 65 scored results on Noometry and Grok 3 has 40.