GPT-5.6 Terra vs Grok 4.20
GPT-5.6 Terra leads Grok 4.20 on coding, 63.9 to 33.3 on the Vibe Coding Index, and Grok 4.20 costs 4.8x less per output token ($2.50 vs $12.00 per million). Updated October 22, 2026; 48 benchmarks have a result for both.
GPT-5.6 Terra or Grok 4.20: which to pick
Pick GPT-5.6 Terra for coding: it scores 63.9 on the Vibe Coding Index against 33.3 for Grok 4.20.
Pick Grok 4.20 for output tokens 4.8x cheaper ($2.50 vs $12.00 per million) and a larger context window (2M against 1.05M tokens).
- Shared results
- 48
- GPT-5.6 Terra only
- 79
- Grok 4.20 only
- 7
- Like-for-like categories
- 7 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
GPT-5.6 Terra
GPT-5.6 Terra scores 63.9 against 33.3.
6 and 2 benchmarks behind the scores
Intelligence
AA Intelligence Index
GPT-5.6 Terra
GPT-5.6 Terra leads, 42.1 against 25.7.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only GPT-5.6 Terra has a AA Agentic Index result, 43.2.
Directional only
Reasoning
GPQA Diamond
GPT-5.6 Terra
GPT-5.6 Terra leads, 90.9% against 88.6%.
Same board, both measured
Math
AIME
No call
Only Grok 4.20 has a AIME result, 96.5%.
Directional only
Knowledge
MMLU Pro
GPT-5.6 Terra
GPT-5.6 Terra leads, 86.7% against 86.3%.
Same board, both measured
Web design
Design Arena: websites
GPT-5.6 Terra
GPT-5.6 Terra leads, 1268 against 1229.
Same board, both measured
Long documents
Listed context window
Grok 4.20
Grok 4.20 takes 2M tokens against 1.05M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Grok 4.20
GPT-5.6 Terra $0.0080, Grok 4.20 $0.0025 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GPT-5.6 Terra
GPT-5.6 Terra $0.42, Grok 4.20 $0.72.
Published attempt cost and pass rate
At a glance
- Coding index
- GPT-5.6 Terra 63.9 from 6 benchmarks, Grok 4.20 33.3 from 2 benchmarks.
- Widest category
- Coding, 32.5 points apart on average over 7 shared benchmarks.
- Widest single gap
- Vibe Code Bench (Vals AI): GPT-5.6 Terra 74.6% against Grok 4.20 4.1%, 70.5 points apart on a 0 to 100 scale.
- Output price
- GPT-5.6 Terra $12.00 against Grok 4.20 $2.50 per million output tokens, 4.8x apart.
- Context window
- GPT-5.6 Terra takes 1.05M tokens, Grok 4.20 2M.
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-5.6 Terra | Bars | Grok 4.20 | Ties |
|---|---|---|---|---|
| Coding | 7 | 0 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 8 | 3 | ||
| Reasoning | 6 | 1 | ||
| Knowledge | 6 | 0 | ||
| Web design | 7 | 1 | ||
| General | 6 | 1 | ||
| All shared boards | 42 | 6 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index70
| Benchmark | GPT-5.6 Terra | Grok 4.20 | Leader |
|---|---|---|---|
| LiveBench coding | 86.2% | Not measured | Only GPT-5.6 Terra measured |
| LMArena WebDev | 1522 | Not measured | Only GPT-5.6 Terra measured |
| AA Coding Index | 76.7 | Not measured | Only GPT-5.6 Terra measured |
| LiveCodeBench (Vals AI) | 85.9% | 84.3% | GPT-5.6 Terra |
| SWE-bench Verified (Vals AI) | 95.4% | 72.2% | GPT-5.6 Terra |
| Terminal-Bench 4 (Vals AI) | 22.7% | Not measured | Only GPT-5.6 Terra measured |
| Vibe Code Bench (Vals AI) | 74.6% | 4.1% | GPT-5.6 Terra |
| Code migration (Vals AI) | 47.8% | 0.3% | GPT-5.6 Terra |
| ProgramBench (Vals AI) | 0.5% | Not measured | Only GPT-5.6 Terra measured |
| IOI (Vals AI) | 87.6% | Not measured | Only GPT-5.6 Terra measured |
| Terminal-Bench 2.1 (Vals AI) | 77.5% | 44.2% | GPT-5.6 Terra |
| Vibe Code Bench 1-100 (Vals AI) | 14.8% | Not measured | Only GPT-5.6 Terra measured |
| SkillsBench (Vals AI) | 58.9% | Not measured | Only GPT-5.6 Terra measured |
| CyberBench (Vals AI) | 72.1% | Not measured | Only GPT-5.6 Terra measured |
| SciCode | 55.0% | Not measured | Only GPT-5.6 Terra measured |
| Terminal-Bench Hard (Artificial Analysis run) | 62.9% | 37.9% | GPT-5.6 Terra |
| Terminal-Bench | Not measured | 57.3% | Only Grok 4.20 measured |
| FrontierCode | 41.3% | Not measured | Only GPT-5.6 Terra measured |
| WeirdML | 78.3% | 52.3% | GPT-5.6 Terra |
| ALE-Bench | 1951.4 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: coding | 1485 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: software and IT | 1482 | Not measured | Only GPT-5.6 Terra measured |
| LMArena WebDev: React | 1513 | Not measured | Only GPT-5.6 Terra measured |
| LMArena WebDev: HTML | 1526 | Not measured | Only GPT-5.6 Terra measured |
| LMArena WebDev: from an image | 1541 | Not measured | Only GPT-5.6 Terra measured |
| SciCode | 10.8% | Not measured | Only GPT-5.6 Terra measured |
| Neither model has a result on the other 13 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.6 Terra | Grok 4.20 | Leader |
|---|---|---|---|
| AA Intelligence Index | 42.1 | 25.7 | GPT-5.6 Terra |
| Epoch Capabilities Index | 160.1 | 152.1 | GPT-5.6 Terra |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index83
| Benchmark | GPT-5.6 Terra | Grok 4.20 | Leader |
|---|---|---|---|
| AA Agentic Index | 43.2 | Not measured | Only GPT-5.6 Terra measured |
| Design Arena: agents building web apps | 1120 | 1144 | Grok 4.20 |
| Design Arena: agents building full-stack apps | 1061 | 1054 | GPT-5.6 Terra |
| Design Arena: agents building mobile apps | 1111 | 1090 | GPT-5.6 Terra |
| Design Arena: agents building Android apps | 1062 | 1045 | GPT-5.6 Terra |
| Design Arena: agents building Godot games | Not measured | 1089 | Only Grok 4.20 measured |
| Design Arena: agents building games | 1083 | Not measured | Only GPT-5.6 Terra measured |
| Design Arena: HTML slides | 1058 | 1180 | Grok 4.20 |
| Design Arena: PowerPoint slides | 1105 | Not measured | Only GPT-5.6 Terra measured |
| LiveBench research | 60.4% | Not measured | Only GPT-5.6 Terra measured |
| LiveBench browser | 84.0% | Not measured | Only GPT-5.6 Terra measured |
| LiveBench long agentic | 48.4% | Not measured | Only GPT-5.6 Terra measured |
| SREBench (Vals AI) | 9.5% | Not measured | Only GPT-5.6 Terra measured |
| Finance Agent (Vals AI) | 54.4% | 28.5% | GPT-5.6 Terra |
| Tax Agent Bench (Vals AI) | 30.0% | 7.0% | GPT-5.6 Terra |
| Legal Research Bench (Vals AI) | 41.4% | 13.9% | GPT-5.6 Terra |
| Excel Modeling Benchmark (Vals AI) | 66.2% | 11.7% | GPT-5.6 Terra |
| Terminal-Bench-Science (Vals AI) | 10.0% | Not measured | Only GPT-5.6 Terra measured |
| τ²-Bench Telecom | 86.3% | 93.0% | Grok 4.20 |
| APEX-Agents | 58.2% | Not measured | Only GPT-5.6 Terra measured |
| BALROG | 53.2% | Not measured | Only GPT-5.6 Terra measured |
| Vending-Bench 2 | $7,343 | $4,663 | GPT-5.6 Terra |
| Neither model has a result on the other 19 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index61
| Benchmark | GPT-5.6 Terra | Grok 4.20 | Leader |
|---|---|---|---|
| GPQA Diamond | 90.9% | 88.6% | GPT-5.6 Terra |
| LiveBench reasoning | 90.6% | Not measured | Only GPT-5.6 Terra measured |
| MMMU Pro (Vals AI) | 86.5% | 83.5% | GPT-5.6 Terra |
| GPQA Diamond (Artificial Analysis run) | 92.5% | 91.1% | GPT-5.6 Terra |
| Humanity's Last Exam (Artificial Analysis run) | 42.9% | 34.5% | GPT-5.6 Terra |
| GPQA Diamond (Epoch AI run) | 93.3% | Not measured | Only GPT-5.6 Terra measured |
| ARC-AGI | 96.5% | 89.5% | GPT-5.6 Terra |
| ARC-AGI-2 | 83.9% | 65.1% | GPT-5.6 Terra |
| SimpleBench | 48.9% | Not measured | Only GPT-5.6 Terra measured |
| Chess puzzles | 47.0% | Not measured | Only GPT-5.6 Terra measured |
| Mystery game puzzles | 35.0% | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: hard prompts | 1469 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: expert | 1496 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: hard prompts in English | 1465 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Vision: diagrams | 1295 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Vision: homework | 1312 | Not measured | Only GPT-5.6 Terra measured |
| Kagi LLM Benchmark | 51.3% | 75.0% | Grok 4.20 |
| Neither model has a result on the other 5 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GPT-5.6 Terra | Grok 4.20 | Leader |
|---|---|---|---|
| AIME | Not measured | 96.5% | Only Grok 4.20 measured |
| OTIS Mock AIME | 99.7% | Not measured | Only GPT-5.6 Terra measured |
| FrontierMath, tiers 1 to 3 | 86.0% | Not measured | Only GPT-5.6 Terra measured |
| FrontierMath, tier 4 | 70.7% | Not measured | Only GPT-5.6 Terra measured |
| ProofBench | 74.0% | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: math | 1465 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: mathematical work | 1478 | Not measured | Only GPT-5.6 Terra measured |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | GPT-5.6 Terra | Grok 4.20 | Leader |
|---|---|---|---|
| MMLU Pro | 86.7% | 86.3% | GPT-5.6 Terra |
| LegalBench (Vals AI) | 85.1% | 77.7% | GPT-5.6 Terra |
| CaseLaw (Vals AI) | Not measured | 54.4% | Only Grok 4.20 measured |
| CorpFin (Vals AI) | 65.3% | 63.7% | GPT-5.6 Terra |
| TaxEval (Vals AI) | 76.2% | 74.1% | GPT-5.6 Terra |
| MortgageTax (Vals AI) | 67.3% | 45.4% | GPT-5.6 Terra |
| MedQA (Vals AI) | Not measured | 94.5% | Only Grok 4.20 measured |
| MedCode (Vals AI) | 43.4% | 32.2% | GPT-5.6 Terra |
| SimpleQA Verified | 43.2% | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: science | 1452 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: medicine and healthcare | 1430 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: legal and government | 1461 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: business and finance | 1448 | Not measured | Only GPT-5.6 Terra measured |
| FACTS Benchmark Suite | 52.0% | Not measured | Only GPT-5.6 Terra measured |
| Neither model has a result on the other 4 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index71
| Benchmark | GPT-5.6 Terra | Grok 4.20 | Leader |
|---|---|---|---|
| Design Arena: websites | 1268 | 1229 | GPT-5.6 Terra |
| Design Arena: UI components | 1256 | 1200 | GPT-5.6 Terra |
| Design Arena: game dev | 1229 | 1193 | GPT-5.6 Terra |
| Design Arena: data viz | 1240 | 1217 | GPT-5.6 Terra |
| Design Arena: 3D | 1134 | 1193 | Grok 4.20 |
| Design Arena: all code categories | 1241 | 1221 | GPT-5.6 Terra |
| Design Arena: SVG | 1219 | 1165 | GPT-5.6 Terra |
| Design Arena: ASCII art | 1177 | 1175 | GPT-5.6 Terra |
| LiveBench design | 76.0% | Not measured | Only GPT-5.6 Terra measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index61
| Benchmark | GPT-5.6 Terra | Grok 4.20 | Leader |
|---|---|---|---|
| LiveBench data analysis | 79.3% | Not measured | Only GPT-5.6 Terra measured |
| Public Benefits Bench (Vals AI) | 62.4% | Not measured | Only GPT-5.6 Terra measured |
| SAGE (Vals AI) | 47.0% | 38.2% | GPT-5.6 Terra |
| MedScribe (Vals AI) | 82.9% | 63.4% | GPT-5.6 Terra |
| Vals Multimodal Index | 65.1% | 39.1% | GPT-5.6 Terra |
| IFBench | 71.2% | 81.2% | Grok 4.20 |
| Long context reasoning (AA-LCR) | 83.0% | 69.0% | GPT-5.6 Terra |
| DTBench | 93.3% | 90.1% | GPT-5.6 Terra |
| LMCA | 54.9% | 38.7% | GPT-5.6 Terra |
| CL-bench | Not measured | 22.2% | Only Grok 4.20 measured |
| Surface Evolver Bench | 83.8% | Not measured | Only GPT-5.6 Terra measured |
| Furniture assembly | 54.2% | Not measured | Only GPT-5.6 Terra measured |
| CL-bench Life | Not measured | 11.9% | Only Grok 4.20 measured |
| LMArena Text | 1448 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: instruction following | 1456 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: creative writing | 1413 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Vision | 1271 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: English | 1449 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: non-English | 1441 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: Chinese | 1514 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: German | 1460 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: Japanese | 1454 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: Korean | 1427 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: French | 1466 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: Spanish | 1451 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: Russian | 1452 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: Polish | 1446 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: multi-turn | 1453 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: longer queries | 1454 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: writing and language | 1431 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: entertainment and media | 1418 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Vision: English | 1272 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Vision: Chinese | 1337 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Vision: reading text | 1278 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Vision: humor | 1236 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Vision: creative writing | 1248 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Document | 1472 | Not measured | Only GPT-5.6 Terra measured |
| Neither model has a result on the other 9 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.6 Terra | Grok 4.20 | 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 4.20 costs less
Repository review
50K input + 3K output tokens
Grok 4.20 costs less
Agent loop
200K input + 10K output tokens
Grok 4.20 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-5.6 Terra | SpaceXAI Grok 4.20 |
|---|---|---|
| Maker | OpenAI | xAI |
| Weights | No public weights listed | No public weights listed |
| Released | July 9, 2026 | March 31, 2026 |
| Context window | 1.05M | 2M |
| Input $/Mtok | $2.00 | $1.25 |
| Output $/Mtok | $12.00 | $2.50 |
| Coding index | 63.9 | 33.3 |
| Independent measurements behind it | 6 | 2 |
| Cost per solved task | $0.42 | $0.72 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.6 Terra better than Grok 4.20 for coding?
Pick GPT-5.6 Terra for coding: it scores 63.9 on the Vibe Coding Index against 33.3 for Grok 4.20. Pick Grok 4.20 for output tokens 4.8x cheaper ($2.50 vs $12.00 per million) and a larger context window (2M against 1.05M tokens).
Which is cheaper, GPT-5.6 Terra or Grok 4.20?
GPT-5.6 Terra costs $2.00 per million input tokens and $12.00 per million output tokens; Grok 4.20 costs $1.25 and $2.50. Per solved SWE-bench Verified task, GPT-5.6 Terra costs $0.42 and Grok 4.20 $0.72.
Which has the bigger context window, GPT-5.6 Terra or Grok 4.20?
Grok 4.20, with 2M tokens against 1.05M.
How many benchmarks is the GPT-5.6 Terra vs Grok 4.20 comparison based on?
48 benchmarks have a result for both models as of October 22, 2026. GPT-5.6 Terra's coding index rests on 6 independent benchmarks and Grok 4.20's on 2. Where only one model has a result, the page says so instead of naming a winner.