GPT-5.2-Codex vs Grok 4.7
Grok 4.7 leads GPT-5.2-Codex on coding, 66.7 to 45.4 on the Vibe Coding Index, and Grok 4.7 costs 2.3x less per output token ($6.00 vs $14.00 per million). Updated October 21, 2026; 7 benchmarks have a result for both.
vs boards won of 7 shared
GPT-5.2-Codex or Grok 4.7: which to pick
Pick Grok 4.7 for coding: it scores 66.7 on the Vibe Coding Index against 45.4 for GPT-5.2-Codex.
Pick GPT-5.2-Codex for work measured by Long context reasoning (AA-LCR), where it leads.
Model AGPT-5.2-CodexOpenAI45.4/1003 independent benchmarks$0.43 per solved taskModel BGrok 4.7xAI66.7/1005 independent benchmarksNo cost per solved task
- Shared results
- 7
- GPT-5.2-Codex only
- 15
- Grok 4.7 only
- 92
- Like-for-like categories
- 4 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
Grok 4.7
Grok 4.7 scores 66.7 against 45.4.
3 and 5 benchmarks behind the scores
Intelligence
AA Intelligence Index
Grok 4.7
Grok 4.7 leads, 46.4 against 28.5.
Same board, both measured
Web design
Design Arena: websites
No call
Only Grok 4.7 has a Design Arena: websites result, 1254.
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 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Grok 4.7
GPT-5.2-Codex $0.0088, Grok 4.7 $0.0050 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.2-Codex has the number, $0.43.
Directional only
At a glance
- Coding index
- GPT-5.2-Codex 45.4 from 3 benchmarks, Grok 4.7 66.7 from 5 benchmarks.
- Widest category
- Coding, 44.5 points apart on average over 4 shared benchmarks.
- Widest single gap
- Vibe Code Bench (Vals AI): GPT-5.2-Codex 37.9% against Grok 4.7 86.2%, 48.3 points apart on a 0 to 100 scale.
- Output price
- GPT-5.2-Codex $14.00 against Grok 4.7 $6.00 per million output tokens, 2.3x apart.
- Context window
- GPT-5.2-Codex takes 400K tokens, Grok 4.7 500K.
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 | GPT-5.2-Codex | Bars | Grok 4.7 | Ties |
|---|---|---|---|---|
| Coding | 0 | 4 | ||
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 1 | ||
| General | 1 | 0 | ||
| All shared boards | 1 | 6 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index04
| Benchmark | GPT-5.2-Codex | Grok 4.7 | Leader |
|---|---|---|---|
| SWE-bench Verified | 72.8% | Not measured | Only GPT-5.2-Codex measured |
| SWE-bench Multilingual | 66.3% | Not measured | Only GPT-5.2-Codex measured |
| LiveBench coding | Not measured | 84.5% | Only Grok 4.7 measured |
| LMArena WebDev | 1339 | 1637 | Grok 4.7 |
| LiveCodeBench (Vals AI) | 88.0% | Not measured | Only GPT-5.2-Codex measured |
| SWE-bench Verified (Vals AI) | 72.4% | Not measured | Only GPT-5.2-Codex measured |
| Terminal-Bench 4 (Vals AI) | Not measured | 28.8% | Only Grok 4.7 measured |
| Vibe Code Bench (Vals AI) | 37.9% | 86.2% | Grok 4.7 |
| Code migration (Vals AI) | Not measured | 44.8% | Only Grok 4.7 measured |
| ProgramBench (Vals AI) | Not measured | 0.5% | Only Grok 4.7 measured |
| IOI (Vals AI) | Not measured | 57.7% | Only Grok 4.7 measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 73.4% | Only Grok 4.7 measured |
| SWE-bench Multimodal (Vals AI) | Not measured | 42.9% | Only Grok 4.7 measured |
| CyberBench (Vals AI) | Not measured | 69.5% | Only Grok 4.7 measured |
| SciCode | Not measured | 57.8% | Only Grok 4.7 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 37.1% | Not measured | Only GPT-5.2-Codex measured |
| Terminal-Bench | 66.5% | Not measured | Only GPT-5.2-Codex measured |
| FrontierCode | Not measured | 47.6% | Only Grok 4.7 measured |
| FrontierSWE | Not measured | 29.5% | Only Grok 4.7 measured |
| ALE-Bench | 1299.9 | Not measured | Only GPT-5.2-Codex measured |
| LMArena Text: coding | Not measured | 1432 | Only Grok 4.7 measured |
| LMArena Text: software and IT | Not measured | 1421 | Only Grok 4.7 measured |
| LMArena WebDev: React | 1329 | 1663 | Grok 4.7 |
| LMArena WebDev: HTML | 1348 | 1627 | Grok 4.7 |
| LMArena WebDev: from an image | Not measured | 1586 | Only Grok 4.7 measured |
| Neither model has a result on the other 14 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-5.2-Codex | Grok 4.7 | Leader |
|---|---|---|---|
| AA Intelligence Index | 28.5 | 46.4 | Grok 4.7 |
| Epoch Capabilities Index | Not measured | 154.4 | Only Grok 4.7 measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index21 boards, none shared
| Benchmark | GPT-5.2-Codex | Grok 4.7 | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | 1051 | Not measured | Only GPT-5.2-Codex measured |
| Design Arena: agents building full-stack apps | 993 | Not measured | Only GPT-5.2-Codex measured |
| Design Arena: agents building mobile apps | 1090 | Not measured | Only GPT-5.2-Codex measured |
| Design Arena: agents building Android apps | 1176 | Not measured | Only GPT-5.2-Codex measured |
| Design Arena: agents building Godot games | 1142 | Not measured | Only GPT-5.2-Codex measured |
| LiveBench research | Not measured | 86.4% | Only Grok 4.7 measured |
| LiveBench browser | Not measured | 60.0% | Only Grok 4.7 measured |
| LiveBench long agentic | Not measured | 50.1% | Only Grok 4.7 measured |
| SREBench (Vals AI) | Not measured | 3.8% | Only Grok 4.7 measured |
| Finance Agent (Vals AI) | Not measured | 52.3% | Only Grok 4.7 measured |
| Tax Agent Bench (Vals AI) | Not measured | 34.2% | Only Grok 4.7 measured |
| Legal Research Bench (Vals AI) | Not measured | 47.1% | Only Grok 4.7 measured |
| Harvey's Legal Agent Benchmark (Vals AI) | Not measured | 18.3% | Only Grok 4.7 measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 67.0% | Only Grok 4.7 measured |
| BioMysteryBench (Vals AI) | Not measured | 69.3% | Only Grok 4.7 measured |
| Terminal-Bench-Science (Vals AI) | Not measured | 10.0% | Only Grok 4.7 measured |
| Vals Science Index | Not measured | 34.8% | Only Grok 4.7 measured |
| Robo-bench (Vals AI) | Not measured | 0.2% | Only Grok 4.7 measured |
| τ²-Bench Telecom | 92.1% | Not measured | Only GPT-5.2-Codex measured |
| APEX-Agents | Not measured | 54.6% | Only Grok 4.7 measured |
| Vending-Bench 2 | Not measured | $10,537 | Only Grok 4.7 measured |
| Neither model has a result on the other 20 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-5.2-Codex | Grok 4.7 | Leader |
|---|---|---|---|
| LiveBench reasoning | Not measured | 82.7% | Only Grok 4.7 measured |
| MysteryMechanism (Vals AI) | Not measured | 25.2% | Only Grok 4.7 measured |
| GPQA Diamond (Artificial Analysis run) | 89.9% | Not measured | Only GPT-5.2-Codex measured |
| Humanity's Last Exam (Artificial Analysis run) | 35.7% | 43.1% | Grok 4.7 |
| GPQA Diamond (Epoch AI run) | Not measured | 92.7% | Only Grok 4.7 measured |
| ARC-AGI | Not measured | 90.2% | Only Grok 4.7 measured |
| ARC-AGI-2 | Not measured | 61.4% | Only Grok 4.7 measured |
| Chess puzzles | Not measured | 41.0% | Only Grok 4.7 measured |
| Mystery game puzzles | Not measured | 29.0% | Only Grok 4.7 measured |
| LMArena Text: hard prompts | Not measured | 1415 | Only Grok 4.7 measured |
| LMArena Text: expert | Not measured | 1439 | Only Grok 4.7 measured |
| LMArena Text: hard prompts in English | Not measured | 1427 | Only Grok 4.7 measured |
| LMArena Vision: diagrams | Not measured | 1254 | Only Grok 4.7 measured |
| LMArena Vision: homework | Not measured | 1309 | Only Grok 4.7 measured |
| Neither model has a result on the other 8 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-5.2-Codex | Grok 4.7 | Leader |
|---|---|---|---|
| OTIS Mock AIME | Not measured | 98.1% | Only Grok 4.7 measured |
| FrontierMath, tiers 1 to 3 | Not measured | 53.0% | Only Grok 4.7 measured |
| FrontierMath, tier 4 | Not measured | 17.1% | Only Grok 4.7 measured |
| ProofBench | Not measured | 26.0% | Only Grok 4.7 measured |
| LMArena Text: math | Not measured | 1414 | Only Grok 4.7 measured |
| LMArena Text: mathematical work | Not measured | 1435 | Only Grok 4.7 measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GPT-5.2-Codex | Grok 4.7 | Leader |
|---|---|---|---|
| LegalBench (Vals AI) | Not measured | 84.4% | Only Grok 4.7 measured |
| MedCode (Vals AI) | Not measured | 49.5% | Only Grok 4.7 measured |
| SimpleQA Verified | Not measured | 56.0% | Only Grok 4.7 measured |
| LMArena Text: science | Not measured | 1419 | Only Grok 4.7 measured |
| LMArena Text: medicine and healthcare | Not measured | 1395 | Only Grok 4.7 measured |
| LMArena Text: legal and government | Not measured | 1423 | Only Grok 4.7 measured |
| LMArena Text: business and finance | Not measured | 1389 | Only Grok 4.7 measured |
| Neither model has a result on the other 11 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GPT-5.2-Codex | Grok 4.7 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1254 | Only Grok 4.7 measured |
| Design Arena: UI components | Not measured | 1275 | Only Grok 4.7 measured |
| Design Arena: game dev | Not measured | 1313 | Only Grok 4.7 measured |
| Design Arena: data viz | Not measured | 1235 | Only Grok 4.7 measured |
| Design Arena: 3D | Not measured | 1270 | Only Grok 4.7 measured |
| Design Arena: all code categories | Not measured | 1271 | Only Grok 4.7 measured |
| LiveBench design | Not measured | 78.0% | 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 index10
| Benchmark | GPT-5.2-Codex | Grok 4.7 | Leader |
|---|---|---|---|
| Vals Index | Not measured | 58.6% | Only Grok 4.7 measured |
| LiveBench data analysis | Not measured | 76.9% | Only Grok 4.7 measured |
| Public Benefits Bench (Vals AI) | Not measured | 65.6% | Only Grok 4.7 measured |
| SAGE (Vals AI) | Not measured | 40.8% | Only Grok 4.7 measured |
| MedScribe (Vals AI) | Not measured | 89.4% | Only Grok 4.7 measured |
| IFBench | 77.6% | Not measured | Only GPT-5.2-Codex measured |
| Long context reasoning (AA-LCR) | 82.3% | 80.0% | GPT-5.2-Codex |
| DTBench | Not measured | 96.0% | Only Grok 4.7 measured |
| LMCA | Not measured | 49.4% | Only Grok 4.7 measured |
| Furniture assembly | Not measured | 20.8% | Only Grok 4.7 measured |
| LMArena Text | Not measured | 1399 | Only Grok 4.7 measured |
| LMArena Text: instruction following | Not measured | 1406 | Only Grok 4.7 measured |
| LMArena Text: creative writing | Not measured | 1381 | Only Grok 4.7 measured |
| LMArena Vision | Not measured | 1231 | Only Grok 4.7 measured |
| LMArena Text: English | Not measured | 1411 | Only Grok 4.7 measured |
| LMArena Text: non-English | Not measured | 1386 | Only Grok 4.7 measured |
| LMArena Text: Chinese | Not measured | 1449 | Only Grok 4.7 measured |
| LMArena Text: German | Not measured | 1366 | Only Grok 4.7 measured |
| LMArena Text: Japanese | Not measured | 1359 | Only Grok 4.7 measured |
| LMArena Text: Korean | Not measured | 1383 | Only Grok 4.7 measured |
| LMArena Text: French | Not measured | 1431 | Only Grok 4.7 measured |
| LMArena Text: Spanish | Not measured | 1406 | Only Grok 4.7 measured |
| LMArena Text: Russian | Not measured | 1399 | Only Grok 4.7 measured |
| LMArena Text: Polish | Not measured | 1420 | Only Grok 4.7 measured |
| LMArena Text: multi-turn | Not measured | 1394 | Only Grok 4.7 measured |
| LMArena Text: longer queries | Not measured | 1416 | Only Grok 4.7 measured |
| LMArena Text: writing and language | Not measured | 1394 | Only Grok 4.7 measured |
| LMArena Text: entertainment and media | Not measured | 1376 | Only Grok 4.7 measured |
| LMArena Vision: English | Not measured | 1229 | Only Grok 4.7 measured |
| LMArena Vision: Chinese | Not measured | 1250 | Only Grok 4.7 measured |
| LMArena Vision: reading text | Not measured | 1241 | Only Grok 4.7 measured |
| LMArena Vision: creative writing | Not measured | 1217 | Only Grok 4.7 measured |
| Neither model has a result on the other 14 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.2-Codex | Grok 4.7 | 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
GPT-5.2-Codex$0.0088Fits in one request
Grok 4.7$0.0050Fits in one request
Grok 4.7 costs less
Repository review
50K input + 3K output tokens
GPT-5.2-Codex$0.130Fits in one request
Grok 4.7$0.118Fits in one request
Grok 4.7 costs less
Agent loop
200K input + 10K output tokens
GPT-5.2-Codex$0.490Fits in one request
Grok 4.7$0.460Fits in one request
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 | OpenAI GPT-5.2-Codex | SpaceXAI Grok 4.7 |
|---|---|---|
| Maker | OpenAI | xAI |
| Weights | No public weights listed | No public weights listed |
| Released | January 14, 2026 | September 21, 2026 |
| Context window | 400K | 500K |
| Input $/Mtok | $1.75 | $2.00 |
| Output $/Mtok | $14.00 | $6.00 |
| Coding index | 45.4 | 66.7 |
| Independent measurements behind it | 3 | 5 |
| Cost per solved task | $0.43 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.2-Codex better than Grok 4.7 for coding?
Pick Grok 4.7 for coding: it scores 66.7 on the Vibe Coding Index against 45.4 for GPT-5.2-Codex. Pick GPT-5.2-Codex for work measured by Long context reasoning (AA-LCR), where it leads.
Which is cheaper, GPT-5.2-Codex or Grok 4.7?
GPT-5.2-Codex costs $1.75 per million input tokens and $14.00 per million output tokens; Grok 4.7 costs $2.00 and $6.00. Cost per solved task is known for GPT-5.2-Codex only.
Which has the bigger context window, GPT-5.2-Codex or Grok 4.7?
Grok 4.7, with 500K tokens against 400K.
How many benchmarks is the GPT-5.2-Codex vs Grok 4.7 comparison based on?
7 benchmarks have a result for both models as of October 21, 2026. GPT-5.2-Codex's coding index rests on 3 independent benchmarks and Grok 4.7's on 5. Where only one model has a result, the page says so instead of naming a winner.