Model comparison
GPT-5 Nano vs Grok 4.7
Grok 4.7 is the stronger model overall, scoring 53.1 to 33.5 on the Noometry Index. GPT-5 Nano costs 22× less per token, which makes it the better buy when Grok 4.7's lead doesn't matter for your workload.
Last verified . 26 shared benchmarks.
Summary
- They share 26 benchmarks with published results for both. GPT-5 Nano scores higher in 1 category and Grok 4.7 in 9 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Grok 4.7 leads 49.1 to 16.3.
- The biggest single-benchmark swing is SimpleQA Verified: 11.7% for GPT-5 Nano and 56% for Grok 4.7.
- GPT-5 Nano is cheaper at $0.05 / $0.40 per million input/output tokens, against $2 / $6 for Grok 4.7.
- Grok 4.7 accepts more context: 500K tokens versus 400K.
Side by side
| GPT-5 Nano | Grok 4.7 | |
|---|---|---|
| Provider | OpenAI | xAI |
| Noometry Index | 33.5 | 53.1 |
| Released | 2025-08-07 | 2026-09-21 |
| Weights | Proprietary | Proprietary |
| Context window | 400K | 500K |
| Max output | 128K | 500K |
| Input $ / M tokens | $0.05 | $2 |
| Output $ / M tokens | $0.40 | $6 |
| Results tracked | 49 | 39 |
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Category by category
Coding Grok 4.7 leads
GPT-5 Nano: 33.6 (#254), Grok 4.7: 58.0 (#18)
| Benchmark | GPT-5 Nano | Grok 4.7 |
|---|---|---|
| LMArena Coding | 1351 | 1427 |
| FrontierCode | — | 47.6% |
| SWE-bench Verified (bash only) | 34.8% | — |
| CursorBench | — | 46.3% |
| LMArena WebDev | — | 1639 |
| FrontierSWE | — | 29.5% |
| SciCode | — | 57.8% |
| WeirdML | 38.1% | — |
| ALE-Bench | 718.67 | — |
Agentic & Tool Use Grok 4.7 leads
GPT-5 Nano: 25.8 (#106), Grok 4.7: 36.7 (#37)
| Benchmark | GPT-5 Nano | Grok 4.7 |
|---|---|---|
| Terminal-Bench | 21.8% | — |
| APEX-Agents | — | 54.6% |
| Berkeley Function Calling Leaderboard | 51.5% | — |
| GDP.pdf | — | 22.8% |
| Vending-Bench 2 | — | 10,537 |
Reasoning Grok 4.7 leads
GPT-5 Nano: 16.3 (#306), Grok 4.7: 49.1 (#40)
| Benchmark | GPT-5 Nano | Grok 4.7 |
|---|---|---|
| Chess Puzzles | 27% | 38% |
| LMArena Hard Prompts | 1328 | 1413 |
| Mystery Game Puzzles | 9% | 29% |
| DTBench | 62.7% | 96% |
| LMCA | 7.9% | 49.4% |
| Epoch Capabilities Index | 139.38 | 153.53 |
| ARC-AGI-2 | 2.6% | — |
| Kagi LLM Benchmark | 62.2% | — |
| NYT Connections (extended) | — | 76.8% |
| ARC-AGI-1 | 20.7% | — |
| CritPt | — | 18% |
| ForecastBench | 59.1 | — |
Math Grok 4.7 leads
GPT-5 Nano: 29.4 (#241), Grok 4.7: 57.8 (#39)
| Benchmark | GPT-5 Nano | Grok 4.7 |
|---|---|---|
| FrontierMath (Tiers 1-3) | 20% | 53% |
| FrontierMath Tier 4 | 2.4% | 17.1% |
| OTIS Mock AIME 2024-2025 | 81.1% | 98.1% |
| ProofBench | 12% | 34% |
| LMArena Math | 1317 | 1407 |
| Omni-MATH | 54.6% | — |
| MATH Level 5 | 95.2% | — |
| FrontierMath (Feb 2025 set) | 8.3% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge Grok 4.7 leads
GPT-5 Nano: 35.9 (#178), Grok 4.7: 62.8 (#22)
| Benchmark | GPT-5 Nano | Grok 4.7 |
|---|---|---|
| GPQA Diamond | 69.4% | 92.7% |
| SimpleQA Verified | 11.7% | 56% |
| LMArena Expert | 1321 | 1422 |
| MMLU-Pro | 77.8% | — |
| Vectara Hallucination Rate | 10.5% | — |
| GPQA (HELM) | 67.9% | — |
Multimodal Grok 4.7 leads
GPT-5 Nano: 31.3 (#108), Grok 4.7: 35.5 (#87)
| Benchmark | GPT-5 Nano | Grok 4.7 |
|---|---|---|
| LMArena Vision | 1159 | 1228 |
| VPCT | 37.2% | — |
| Blueprint-Bench 2 | — | 32.5% |
| Furniture Assembly | — | 20.8% |
Multilingual Grok 4.7 leads
GPT-5 Nano: 45.3 (#172), Grok 4.7: 50.8 (#116)
| Benchmark | GPT-5 Nano | Grok 4.7 |
|---|---|---|
| LMArena Non-English | 1313 | 1389 |
| LMArena Chinese | 1356 | 1455 |
| LMArena Russian | 1296 | 1397 |
| LMArena Spanish | 1360 | 1400 |
| LMArena French | — | 1455 |
| LMArena German | 1327 | — |
| LMArena Japanese | 1226 | — |
| LMArena Korean | 1269 | — |
Instruction Following Too close to call
GPT-5 Nano: 75.0 (#79), Grok 4.7: 74.1 (#105)
| Benchmark | GPT-5 Nano | Grok 4.7 |
|---|---|---|
| LMArena Instruction Following | 1306 | 1404 |
| IFEval | 93.2% | — |
Long Context Grok 4.7 leads
GPT-5 Nano: 31.3 (#281), Grok 4.7: 43.1 (#104)
| Benchmark | GPT-5 Nano | Grok 4.7 |
|---|---|---|
| LMArena Longer Query | 1312 | 1413 |
| Fiction.LiveBench | 44.4% | — |
Writing & Preference Grok 4.7 leads
GPT-5 Nano: 39.1 (#249), Grok 4.7: 70.0 (#24)
| Benchmark | GPT-5 Nano | Grok 4.7 |
|---|---|---|
| LMArena Text | 1320 | 1399 |
| LMArena Creative Writing | 1249 | 1391 |
| EQ-Bench Creative Writing | 705 | 2007 |
| LMArena Multi-Turn | 1311 | 1393 |
| WildBench | 80.6% | — |
Frequently asked questions
Is GPT-5 Nano better than Grok 4.7?
Grok 4.7 is the stronger model overall, scoring 53.1 to 33.5 on the Noometry Index. GPT-5 Nano costs 22× less per token, which makes it the better buy when Grok 4.7's lead doesn't matter for your workload.
Which is cheaper, GPT-5 Nano or Grok 4.7?
GPT-5 Nano is cheaper. It lists at $0.05 per million input tokens and $0.40 per million output tokens; Grok 4.7 lists at $2 and $6.
Is GPT-5 Nano or Grok 4.7 better for coding?
Grok 4.7 scores higher on coding benchmarks: 58.0 versus 33.6 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 Nano and Grok 4.7 share?
26 benchmarks have published results for both models. GPT-5 Nano has 49 scored results on Noometry and Grok 4.7 has 39.