Model comparison
GPT-5 Nano vs Grok 4.6
Grok 4.6 is the stronger model overall, scoring 56.9 to 33.5 on the Noometry Index. GPT-5 Nano costs 22× less per token, which makes it the better buy when Grok 4.6's lead doesn't matter for your workload.
Last verified . 32 shared benchmarks.
Summary
- They share 32 benchmarks with published results for both. GPT-5 Nano scores higher in 0 categories and Grok 4.6 in 10 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Grok 4.6 leads 61.4 to 16.3.
- The biggest single-benchmark swing is ARC-AGI-1: 20.7% for GPT-5 Nano and 87.5% for Grok 4.6.
- GPT-5 Nano is cheaper at $0.05 / $0.40 per million input/output tokens, against $2 / $6 for Grok 4.6.
- Grok 4.6 accepts more context: 500K tokens versus 400K.
Side by side
| GPT-5 Nano | Grok 4.6 | |
|---|---|---|
| Provider | OpenAI | xAI |
| Noometry Index | 33.5 | 56.9 |
| Released | 2025-08-07 | 2026-08-12 |
| 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 | 49 |
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Category by category
Coding Grok 4.6 leads
GPT-5 Nano: 33.6 (#254), Grok 4.6: 58.5 (#16)
| Benchmark | GPT-5 Nano | Grok 4.6 |
|---|---|---|
| WeirdML | 38.1% | 67.3% |
| LMArena Coding | 1351 | 1465 |
| ALE-Bench | 718.67 | 1,508 |
| DeepSWE | — | 67.5% |
| FrontierCode | — | 48% |
| SWE-bench Verified (bash only) | 34.8% | — |
| CursorBench | — | 41.4% |
| LMArena WebDev | — | 1617 |
| FrontierSWE | — | 25.3% |
| SciCode | — | 56.5% |
Agentic & Tool Use Grok 4.6 leads
GPT-5 Nano: 25.8 (#106), Grok 4.6: 39.4 (#27)
| Benchmark | GPT-5 Nano | Grok 4.6 |
|---|---|---|
| Terminal-Bench | 21.8% | — |
| APEX-Agents | — | 65.3% |
| Berkeley Function Calling Leaderboard | 51.5% | — |
| GDP.pdf | — | 17.2% |
| Vending-Bench 2 | — | 9,047 |
Reasoning Grok 4.6 leads
GPT-5 Nano: 16.3 (#306), Grok 4.6: 61.4 (#20)
| Benchmark | GPT-5 Nano | Grok 4.6 |
|---|---|---|
| ARC-AGI-2 | 2.6% | 67.1% |
| ARC-AGI-1 | 20.7% | 87.5% |
| Chess Puzzles | 27% | 40% |
| LMArena Hard Prompts | 1328 | 1447 |
| Mystery Game Puzzles | 9% | 34% |
| DTBench | 62.7% | 97.3% |
| LMCA | 7.9% | 48.5% |
| Epoch Capabilities Index | 139.38 | 156.44 |
| SimpleBench | — | 75.9% |
| Kagi LLM Benchmark | 62.2% | — |
| NYT Connections (extended) | — | 80% |
| CritPt | — | 19.7% |
| EBR-Bench | — | 30.5% |
| ForecastBench | 59.1 | — |
Math Grok 4.6 leads
GPT-5 Nano: 29.4 (#241), Grok 4.6: 67.0 (#24)
| Benchmark | GPT-5 Nano | Grok 4.6 |
|---|---|---|
| FrontierMath (Tiers 1-3) | 20% | 66% |
| FrontierMath Tier 4 | 2.4% | 31.7% |
| OTIS Mock AIME 2024-2025 | 81.1% | 99.2% |
| ProofBench | 12% | 51% |
| LMArena Math | 1317 | 1423 |
| Omni-MATH | 54.6% | — |
| MATH Level 5 | 95.2% | — |
| FrontierMath (Feb 2025 set) | 8.3% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge Grok 4.6 leads
GPT-5 Nano: 35.9 (#178), Grok 4.6: 63.3 (#20)
| Benchmark | GPT-5 Nano | Grok 4.6 |
|---|---|---|
| GPQA Diamond | 69.4% | 94% |
| SimpleQA Verified | 11.7% | 49.3% |
| LMArena Expert | 1321 | 1467 |
| MMLU-Pro | 77.8% | — |
| Vectara Hallucination Rate | 10.5% | — |
| GPQA (HELM) | 67.9% | — |
Multimodal Grok 4.6 leads
GPT-5 Nano: 31.3 (#108), Grok 4.6: 43.6 (#23)
| Benchmark | GPT-5 Nano | Grok 4.6 |
|---|---|---|
| LMArena Vision | 1159 | 1263 |
| VPCT | 37.2% | — |
| Blueprint-Bench 2 | — | 33.2% |
| Furniture Assembly | — | 40% |
| LMArena Document | — | 1452 |
Multilingual Grok 4.6 leads
GPT-5 Nano: 45.3 (#172), Grok 4.6: 53.0 (#74)
| Benchmark | GPT-5 Nano | Grok 4.6 |
|---|---|---|
| LMArena Non-English | 1313 | 1420 |
| LMArena Chinese | 1356 | 1480 |
| LMArena German | 1327 | 1431 |
| LMArena Japanese | 1226 | 1376 |
| LMArena Korean | 1269 | 1397 |
| LMArena Russian | 1296 | 1422 |
| LMArena Spanish | 1360 | 1404 |
| LMArena French | — | 1461 |
Instruction Following Too close to call
GPT-5 Nano: 75.0 (#79), Grok 4.6: 75.4 (#63)
| Benchmark | GPT-5 Nano | Grok 4.6 |
|---|---|---|
| LMArena Instruction Following | 1306 | 1431 |
| IFEval | 93.2% | — |
Long Context Grok 4.6 leads
GPT-5 Nano: 31.3 (#281), Grok 4.6: 44.5 (#66)
| Benchmark | GPT-5 Nano | Grok 4.6 |
|---|---|---|
| LMArena Longer Query | 1312 | 1454 |
| Fiction.LiveBench | 44.4% | — |
Writing & Preference Grok 4.6 leads
GPT-5 Nano: 39.1 (#249), Grok 4.6: 62.3 (#80)
| Benchmark | GPT-5 Nano | Grok 4.6 |
|---|---|---|
| LMArena Text | 1320 | 1428 |
| LMArena Creative Writing | 1249 | 1428 |
| LMArena Multi-Turn | 1311 | 1425 |
| EQ-Bench Creative Writing | 705 | — |
| WildBench | 80.6% | — |
Frequently asked questions
Is GPT-5 Nano better than Grok 4.6?
Grok 4.6 is the stronger model overall, scoring 56.9 to 33.5 on the Noometry Index. GPT-5 Nano costs 22× less per token, which makes it the better buy when Grok 4.6's lead doesn't matter for your workload.
Which is cheaper, GPT-5 Nano or Grok 4.6?
GPT-5 Nano is cheaper. It lists at $0.05 per million input tokens and $0.40 per million output tokens; Grok 4.6 lists at $2 and $6.
Is GPT-5 Nano or Grok 4.6 better for coding?
Grok 4.6 scores higher on coding benchmarks: 58.5 versus 33.6 in the Noometry coding category.
Which has the bigger context window?
Grok 4.6 does, with 500K tokens against 400K.
How many benchmarks do GPT-5 Nano and Grok 4.6 share?
32 benchmarks have published results for both models. GPT-5 Nano has 49 scored results on Noometry and Grok 4.6 has 49.