Grok 4.7 vs o1
Grok 4.7 leads o1 on the Artificial Analysis Intelligence Index, 46.4 to 15.2, and Grok 4.7 costs 10.0x less per output token ($6.00 vs $60.00 per million). Updated October 22, 2026; 43 benchmarks have a result for both.
Grok 4.7 or o1: which to pick
Only Grok 4.7 has a coding score, 66.7 on the Vibe Coding Index; o1 has none, so the two cannot be ranked on coding.
Pick o1 for work measured by LMArena Text: Korean, where it leads.
- Shared results
- 43
- Grok 4.7 only
- 56
- o1 only
- 26
- Like-for-like categories
- 6 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Only Grok 4.7 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Grok 4.7
Grok 4.7 leads, 46.4 against 15.2.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only o1 has a GPQA Diamond result, 73.2%.
Directional only
Math
AIME
No call
Only o1 has a AIME result, 71.5%.
Directional only
Knowledge
MMLU Pro
No call
Only o1 has a MMLU Pro result, 83.5%.
Directional only
Web design
Design Arena: websites
No call
Only Grok 4.7 has a Design Arena: websites result, 1255.
Directional only
General
Vals Index
No call
Only Grok 4.7 has a Vals Index result, 58.6%.
Directional only
Long documents
Listed context window
Grok 4.7
Grok 4.7 takes 500K tokens against 200K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Grok 4.7
Grok 4.7 $0.0050, o1 $0.045 per turn.
List prices
At a glance
- Coding index
- Grok 4.7 66.7 from 5 benchmarks, o1 not benchmarked.
- Widest category
- Reasoning, 24.5 points apart on average over 7 shared benchmarks.
- Widest single gap
- ARC-AGI: Grok 4.7 90.2% against o1 30.7%, 59.5 points apart on a 0 to 100 scale.
- Output price
- Grok 4.7 $6.00 against o1 $60.00 per million output tokens, 10.0x apart.
- Context window
- Grok 4.7 takes 500K tokens, o1 200K.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | Grok 4.7 | Bars | o1 | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| Intelligence | 2 | 0 | ||
| Reasoning | 7 | 0 | ||
| Math | 4 | 0 | ||
| Knowledge | 6 | 0 | ||
| General | 21 | 1 | ||
| All shared boards | 42 | 1 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
| Benchmark | Grok 4.7 | o1 | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 61.7% | Only o1 measured |
| LiveBench coding | 84.5% | Not measured | Only Grok 4.7 measured |
| LMArena WebDev | 1637 | Not measured | Only Grok 4.7 measured |
| AA Coding Index | Not measured | 39.7 | Only o1 measured |
| LiveCodeBench (Vals AI) | Not measured | 50.3% | Only o1 measured |
| Terminal-Bench 4 (Vals AI) | 28.8% | Not measured | Only Grok 4.7 measured |
| Vibe Code Bench (Vals AI) | 86.2% | Not measured | Only Grok 4.7 measured |
| Code migration (Vals AI) | 44.8% | Not measured | Only Grok 4.7 measured |
| ProgramBench (Vals AI) | 0.5% | Not measured | Only Grok 4.7 measured |
| IOI (Vals AI) | 57.7% | Not measured | Only Grok 4.7 measured |
| Terminal-Bench 2.1 (Vals AI) | 73.4% | Not measured | Only Grok 4.7 measured |
| SWE-bench Multimodal (Vals AI) | 42.9% | Not measured | Only Grok 4.7 measured |
| CyberBench (Vals AI) | 69.5% | Not measured | Only Grok 4.7 measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 67.9% | Only o1 measured |
| SciCode | 57.8% | Not measured | Only Grok 4.7 measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 12.9% | Only o1 measured |
| FrontierCode | 47.6% | Not measured | Only Grok 4.7 measured |
| FrontierSWE | 29.5% | Not measured | Only Grok 4.7 measured |
| WeirdML | Not measured | 46.1% | Only o1 measured |
| LMArena Text: coding | 1432 | 1367 | Grok 4.7 |
| LMArena Text: software and IT | 1421 | 1366 | Grok 4.7 |
| LMArena WebDev: React | 1663 | Not measured | Only Grok 4.7 measured |
| LMArena WebDev: HTML | 1627 | Not measured | Only Grok 4.7 measured |
| LMArena WebDev: from an image | 1586 | Not measured | Only Grok 4.7 measured |
| SciCode | Not measured | 4.6% | Only o1 measured |
| Neither model has a result on the other 14 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Grok 4.7 | o1 | Leader |
|---|---|---|---|
| AA Intelligence Index | 46.4 | 15.2 | Grok 4.7 |
| Epoch Capabilities Index | 154.4 | 141.7 | Grok 4.7 |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index17 boards, none shared
| Benchmark | Grok 4.7 | o1 | Leader |
|---|---|---|---|
| LiveBench research | 86.4% | Not measured | Only Grok 4.7 measured |
| LiveBench browser | 60.0% | Not measured | Only Grok 4.7 measured |
| LiveBench long agentic | 50.1% | Not measured | Only Grok 4.7 measured |
| SREBench (Vals AI) | 3.8% | Not measured | Only Grok 4.7 measured |
| Finance Agent (Vals AI) | 52.3% | Not measured | Only Grok 4.7 measured |
| Tax Agent Bench (Vals AI) | 34.2% | Not measured | Only Grok 4.7 measured |
| Legal Research Bench (Vals AI) | 47.1% | Not measured | Only Grok 4.7 measured |
| Harvey's Legal Agent Benchmark (Vals AI) | 18.3% | Not measured | Only Grok 4.7 measured |
| Excel Modeling Benchmark (Vals AI) | 67.0% | Not measured | Only Grok 4.7 measured |
| BioMysteryBench (Vals AI) | 69.3% | Not measured | Only Grok 4.7 measured |
| Terminal-Bench-Science (Vals AI) | 10.0% | Not measured | Only Grok 4.7 measured |
| Vals Science Index | 34.8% | Not measured | Only Grok 4.7 measured |
| Robo-bench (Vals AI) | 0.2% | Not measured | Only Grok 4.7 measured |
| τ²-Bench Telecom | Not measured | 62.6% | Only o1 measured |
| APEX-Agents | 54.6% | Not measured | Only Grok 4.7 measured |
| METR time horizons | Not measured | 51.1% | Only o1 measured |
| Vending-Bench 2 | $10,537 | Not measured | Only Grok 4.7 measured |
| Neither model has a result on the other 24 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index70
| Benchmark | Grok 4.7 | o1 | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 73.2% | Only o1 measured |
| LiveBench reasoning | 82.7% | Not measured | Only Grok 4.7 measured |
| MysteryMechanism (Vals AI) | 25.2% | Not measured | Only Grok 4.7 measured |
| MMMU Pro (Vals AI) | Not measured | 77.4% | Only o1 measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 74.7% | Only o1 measured |
| Humanity's Last Exam (Artificial Analysis run) | 43.1% | 7.0% | Grok 4.7 |
| GPQA Diamond (Epoch AI run) | 92.7% | 76.8% | Grok 4.7 |
| ARC-AGI | 90.2% | 30.7% | Grok 4.7 |
| ARC-AGI-2 | 61.4% | Not measured | Only Grok 4.7 measured |
| SimpleBench | Not measured | 40.1% | Only o1 measured |
| Chess puzzles | 41.0% | 10.0% | Grok 4.7 |
| Mystery game puzzles | 29.0% | Not measured | Only Grok 4.7 measured |
| VPCT | Not measured | 37.0% | Only o1 measured |
| LMArena Text: hard prompts | 1415 | 1371 | Grok 4.7 |
| LMArena Text: expert | 1439 | 1361 | Grok 4.7 |
| LMArena Text: hard prompts in English | 1427 | 1372 | Grok 4.7 |
| LMArena Vision: diagrams | 1254 | Not measured | Only Grok 4.7 measured |
| LMArena Vision: homework | 1309 | Not measured | Only Grok 4.7 measured |
| Neither model has a result on the other 4 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | Grok 4.7 | o1 | Leader |
|---|---|---|---|
| AIME | Not measured | 71.5% | Only o1 measured |
| MATH 500 (Vals AI) | Not measured | 90.4% | Only o1 measured |
| MGSM (Vals AI) | Not measured | 89.3% | Only o1 measured |
| OTIS Mock AIME | 98.1% | 73.3% | Grok 4.7 |
| FrontierMath, tiers 1 to 3 | 53.0% | 14.7% | Grok 4.7 |
| FrontierMath, tier 4 | 17.1% | Not measured | Only Grok 4.7 measured |
| ProofBench | 26.0% | Not measured | Only Grok 4.7 measured |
| MATH level 5 | Not measured | 94.7% | Only o1 measured |
| LMArena Text: math | 1414 | 1388 | Grok 4.7 |
| LMArena Text: mathematical work | 1435 | 1390 | Grok 4.7 |
| Neither model has a result on the other 3 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | Grok 4.7 | o1 | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 83.5% | Only o1 measured |
| LegalBench (Vals AI) | 84.4% | 80.4% | Grok 4.7 |
| TaxEval (Vals AI) | Not measured | 74.3% | Only o1 measured |
| MedQA (Vals AI) | Not measured | 96.5% | Only o1 measured |
| MedCode (Vals AI) | 49.5% | Not measured | Only Grok 4.7 measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 84.1% | Only o1 measured |
| SimpleQA Verified | 56.0% | 41.1% | Grok 4.7 |
| GeoBench | Not measured | 80.0% | Only o1 measured |
| LMArena Text: science | 1419 | 1369 | Grok 4.7 |
| LMArena Text: medicine and healthcare | 1395 | 1355 | Grok 4.7 |
| LMArena Text: legal and government | 1423 | 1368 | Grok 4.7 |
| LMArena Text: business and finance | 1389 | 1328 | Grok 4.7 |
| Neither model has a result on the other 6 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | Grok 4.7 | o1 | Leader |
|---|---|---|---|
| Design Arena: websites | 1255 | Not measured | Only Grok 4.7 measured |
| Design Arena: UI components | 1275 | Not measured | Only Grok 4.7 measured |
| Design Arena: game dev | 1314 | Not measured | Only Grok 4.7 measured |
| Design Arena: data viz | 1238 | Not measured | Only Grok 4.7 measured |
| Design Arena: 3D | 1274 | Not measured | Only Grok 4.7 measured |
| Design Arena: all code categories | 1273 | Not measured | Only Grok 4.7 measured |
| LiveBench design | 78.0% | Not measured | Only Grok 4.7 measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index211
| Benchmark | Grok 4.7 | o1 | Leader |
|---|---|---|---|
| Vals Index | 58.6% | Not measured | Only Grok 4.7 measured |
| LiveBench data analysis | 76.9% | Not measured | Only Grok 4.7 measured |
| Public Benefits Bench (Vals AI) | 65.6% | Not measured | Only Grok 4.7 measured |
| SAGE (Vals AI) | 40.8% | Not measured | Only Grok 4.7 measured |
| MedScribe (Vals AI) | 89.4% | Not measured | Only Grok 4.7 measured |
| IFBench | Not measured | 70.3% | Only o1 measured |
| Long context reasoning (AA-LCR) | 80.0% | 65.0% | Grok 4.7 |
| DTBench | 96.0% | 74.7% | Grok 4.7 |
| LMCA | 49.4% | 22.3% | Grok 4.7 |
| Fiction.LiveBench | Not measured | 83.3% | Only o1 measured |
| Creative writing (Lech Mazur) | Not measured | 70.2% | Only o1 measured |
| Furniture assembly | 20.8% | Not measured | Only Grok 4.7 measured |
| LMArena Text | 1399 | 1366 | Grok 4.7 |
| LMArena Text: instruction following | 1406 | 1367 | Grok 4.7 |
| LMArena Text: creative writing | 1381 | 1347 | Grok 4.7 |
| LMArena Vision | 1231 | 1168 | Grok 4.7 |
| LMArena Text: English | 1411 | 1372 | Grok 4.7 |
| LMArena Text: non-English | 1386 | 1358 | Grok 4.7 |
| LMArena Text: Chinese | 1449 | 1394 | Grok 4.7 |
| LMArena Text: German | 1366 | 1337 | Grok 4.7 |
| LMArena Text: Japanese | 1359 | 1346 | Grok 4.7 |
| LMArena Text: Korean | 1383 | 1396 | o1 |
| LMArena Text: French | 1431 | 1344 | Grok 4.7 |
| LMArena Text: Spanish | 1406 | 1345 | Grok 4.7 |
| LMArena Text: Russian | 1399 | 1356 | Grok 4.7 |
| LMArena Text: Polish | 1420 | Not measured | Only Grok 4.7 measured |
| LMArena Text: multi-turn | 1394 | 1355 | Grok 4.7 |
| LMArena Text: longer queries | 1416 | 1378 | Grok 4.7 |
| LMArena Text: writing and language | 1394 | 1360 | Grok 4.7 |
| LMArena Text: entertainment and media | 1376 | 1358 | Grok 4.7 |
| LMArena Vision: English | 1229 | 1162 | Grok 4.7 |
| LMArena Vision: Chinese | 1250 | 1235 | Grok 4.7 |
| LMArena Vision: reading text | 1241 | Not measured | Only Grok 4.7 measured |
| LMArena Vision: creative writing | 1217 | Not measured | Only Grok 4.7 measured |
| Neither model has a result on the other 12 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Grok 4.7 | o1 | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
Grok 4.7 costs less
Repository review
50K input + 3K output tokens
Grok 4.7 costs less
Agent loop
200K input + 10K output tokens
Grok 4.7 costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | SpaceXAI Grok 4.7 | OpenAI o1 |
|---|---|---|
| Maker | xAI | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | September 21, 2026 | December 17, 2024 |
| Context window | 500K | 200K |
| Input $/Mtok | $2.00 | $15.00 |
| Output $/Mtok | $6.00 | $60.00 |
| Coding index | 66.7 | Not benchmarked |
| Independent measurements behind it | 5 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Grok 4.7 better than o1 for coding?
Only Grok 4.7 has a coding score, 66.7 on the Vibe Coding Index; o1 has none, so the two cannot be ranked on coding. Pick o1 for work measured by LMArena Text: Korean, where it leads.
Which is cheaper, Grok 4.7 or o1?
Grok 4.7 costs $2.00 per million input tokens and $6.00 per million output tokens; o1 costs $15.00 and $60.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Grok 4.7 or o1?
Grok 4.7, with 500K tokens against 200K.
How many benchmarks is the Grok 4.7 vs o1 comparison based on?
43 benchmarks have a result for both models as of October 22, 2026. Grok 4.7's coding index rests on 5 independent benchmarks and o1's on 0. Where only one model has a result, the page says so instead of naming a winner.