Model comparison
GPT-5.2 vs Grok 4.7
GPT-5.2 and Grok 4.7 score almost the same on the Noometry Index (54.1 vs 53.1), so choose on price, context window or the category you care about most.
Last verified . 31 shared benchmarks.
Summary
- They share 31 benchmarks with published results for both. GPT-5.2 scores higher in 7 categories and Grok 4.7 in 3 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in multimodal, where GPT-5.2 leads 51.3 to 35.5.
- The biggest single-benchmark swing is ProofBench: 15% for GPT-5.2 and 34% for Grok 4.7.
- Grok 4.7 is cheaper at $2 / $6 per million input/output tokens, against $1.75 / $14 for GPT-5.2.
- Grok 4.7 accepts more context: 500K tokens versus 400K.
Side by side
| GPT-5.2 | Grok 4.7 | |
|---|---|---|
| Provider | OpenAI | xAI |
| Noometry Index | 54.1 | 53.1 |
| Released | 2025-12-11 | 2026-09-21 |
| Weights | Proprietary | Proprietary |
| Context window | 400K | 500K |
| Max output | 128K | 500K |
| Input $ / M tokens | $1.75 | $2 |
| Output $ / M tokens | $14 | $6 |
| Results tracked | 67 | 39 |
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Category by category
Coding Grok 4.7 leads
GPT-5.2: 51.6 (#37), Grok 4.7: 58.0 (#18)
| Benchmark | GPT-5.2 | Grok 4.7 |
|---|---|---|
| LMArena WebDev | 1416 | 1639 |
| LMArena Coding | 1447 | 1427 |
| SWE-bench Verified | 73.8% | — |
| FrontierCode | — | 47.6% |
| SWE-bench Verified (bash only) | 72.8% | — |
| CursorBench | — | 46.3% |
| SWE-bench Multilingual | 66.7% | — |
| FrontierSWE | — | 29.5% |
| SciCode | — | 57.8% |
| GSO | 27.4% | — |
| WeirdML | 72.2% | — |
| ALE-Bench | 1,294 | — |
| AlgoTune | 2.05 | — |
Agentic & Tool Use GPT-5.2 leads
GPT-5.2: 40.2 (#24), Grok 4.7: 36.7 (#37)
| Benchmark | GPT-5.2 | Grok 4.7 |
|---|---|---|
| Vending-Bench 2 | 3,591 | 10,537 |
| Terminal-Bench | 64.9% | — |
| APEX-Agents | — | 54.6% |
| Berkeley Function Calling Leaderboard | 55.9% | — |
| GDPval | 49.7% | — |
| Remote Labor Index | 2.5% | — |
| τ²-bench Airline | 83% | — |
| τ²-bench Banking | 32.2% | — |
| τ²-bench Retail | 81.6% | — |
| τ²-bench Telecom | 89.7% | — |
| DeepResearch Bench | 41.1% | — |
| GDP.pdf | — | 22.8% |
| LMArena Search | 1207 | — |
| METR Time Horizons | 75.3% | — |
Reasoning GPT-5.2 leads
GPT-5.2: 50.2 (#35), Grok 4.7: 49.1 (#40)
| Benchmark | GPT-5.2 | Grok 4.7 |
|---|---|---|
| NYT Connections (extended) | 83.6% | 76.8% |
| Chess Puzzles | 49% | 38% |
| LMArena Hard Prompts | 1445 | 1413 |
| Mystery Game Puzzles | 23% | 29% |
| DTBench | 90.9% | 96% |
| LMCA | 43.9% | 49.4% |
| Epoch Capabilities Index | 153.45 | 153.53 |
| ARC-AGI-2 | 52.9% | — |
| SimpleBench | 45.8% | — |
| Kagi LLM Benchmark | 73.3% | — |
| ARC-AGI-1 | 86.2% | — |
| CritPt | — | 18% |
| EnigmaEval | 10.4% | — |
| EBR-Bench | 23% | — |
| ForecastBench | 60.1 | — |
Math GPT-5.2 leads
GPT-5.2: 60.0 (#38), Grok 4.7: 57.8 (#39)
| Benchmark | GPT-5.2 | Grok 4.7 |
|---|---|---|
| FrontierMath (Tiers 1-3) | 67.4% | 53% |
| FrontierMath Tier 4 | 31.7% | 17.1% |
| OTIS Mock AIME 2024-2025 | 96.1% | 98.1% |
| ProofBench | 15% | 34% |
| LMArena Math | 1440 | 1407 |
| MathArena Final-Answer Competitions | 72% | — |
| FrontierMath (Feb 2025 set) | 40.7% | — |
| FrontierMath Tier 4 (v1) | 18.8% | — |
Knowledge Grok 4.7 leads
GPT-5.2: 59.3 (#32), Grok 4.7: 62.8 (#22)
| Benchmark | GPT-5.2 | Grok 4.7 |
|---|---|---|
| GPQA Diamond | 91.4% | 92.7% |
| SimpleQA Verified | 37.1% | 56% |
| LMArena Expert | 1445 | 1422 |
| Humanity's Last Exam | 27.8% | — |
| Vectara Hallucination Rate | 8.4% | — |
Multimodal GPT-5.2 leads
GPT-5.2: 51.3 (#7), Grok 4.7: 35.5 (#87)
| Benchmark | GPT-5.2 | Grok 4.7 |
|---|---|---|
| LMArena Vision | 1268 | 1228 |
| Furniture Assembly | 38.3% | 20.8% |
| VPCT | 84% | — |
| Blueprint-Bench 2 | — | 32.5% |
| LMArena Document | 1405 | — |
Multilingual GPT-5.2 leads
GPT-5.2: 53.4 (#67), Grok 4.7: 50.8 (#116)
| Benchmark | GPT-5.2 | Grok 4.7 |
|---|---|---|
| LMArena Non-English | 1425 | 1389 |
| LMArena Chinese | 1460 | 1455 |
| LMArena French | 1455 | 1455 |
| LMArena Russian | 1440 | 1397 |
| LMArena Spanish | 1433 | 1400 |
| LMArena German | 1448 | — |
| LMArena Japanese | 1420 | — |
| LMArena Korean | 1392 | — |
Instruction Following Too close to call
GPT-5.2: 74.7 (#89), Grok 4.7: 74.1 (#105)
| Benchmark | GPT-5.2 | Grok 4.7 |
|---|---|---|
| LMArena Instruction Following | 1417 | 1404 |
Long Context Too close to call
GPT-5.2: 44.0 (#78), Grok 4.7: 43.1 (#104)
| Benchmark | GPT-5.2 | Grok 4.7 |
|---|---|---|
| LMArena Longer Query | 1428 | 1413 |
| CL-bench | 18.2% | — |
Writing & Preference Grok 4.7 leads
GPT-5.2: 66.8 (#32), Grok 4.7: 70.0 (#24)
| Benchmark | GPT-5.2 | Grok 4.7 |
|---|---|---|
| LMArena Text | 1439 | 1399 |
| LMArena Creative Writing | 1401 | 1391 |
| EQ-Bench Creative Writing | 1703 | 2007 |
| LMArena Multi-Turn | 1458 | 1393 |
Frequently asked questions
Is GPT-5.2 better than Grok 4.7?
GPT-5.2 and Grok 4.7 score almost the same on the Noometry Index (54.1 vs 53.1), so choose on price, context window or the category you care about most.
Which is cheaper, GPT-5.2 or Grok 4.7?
Grok 4.7 is cheaper. It lists at $2 per million input tokens and $6 per million output tokens; GPT-5.2 lists at $1.75 and $14.
Is GPT-5.2 or Grok 4.7 better for coding?
Grok 4.7 scores higher on coding benchmarks: 58.0 versus 51.6 in the Noometry coding category.
Which has the bigger context window?
Grok 4.7 does, with 500K tokens against 400K.
How many benchmarks do GPT-5.2 and Grok 4.7 share?
31 benchmarks have published results for both models. GPT-5.2 has 67 scored results on Noometry and Grok 4.7 has 39.