Claude Fable 5 vs GPT-5.6 Terra
Claude Fable 5 leads GPT-5.6 Terra on coding, 79.3 to 63.9 on the Vibe Coding Index, and GPT-5.6 Terra costs 4.2x less per output token ($12.00 vs $50.00 per million). Updated October 22, 2026; 119 benchmarks have a result for both.
Claude Fable 5 or GPT-5.6 Terra: which to pick
Pick Claude Fable 5 for coding: it scores 79.3 on the Vibe Coding Index against 63.9 for GPT-5.6 Terra.
Pick GPT-5.6 Terra for output tokens 4.2x cheaper ($12.00 vs $50.00 per million) and a lower cost per solved task ($0.42 against $2.16).
- Shared results
- 119
- Claude Fable 5 only
- 8
- GPT-5.6 Terra only
- 8
- Like-for-like categories
- 8 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
Claude Fable 5
Claude Fable 5 scores 79.3 against 63.9.
6 benchmarks behind each score
Intelligence
AA Intelligence Index
Claude Fable 5
Claude Fable 5 leads, 49.6 against 42.1.
Same board, both measured
Agentic work
AA Agentic Index
Claude Fable 5
Claude Fable 5 leads, 50.7 against 43.2.
Same board, both measured
Reasoning
GPQA Diamond
Claude Fable 5
Claude Fable 5 leads, 93.2% against 90.9%.
Same board, both measured
Knowledge
MMLU Pro
Claude Fable 5
Claude Fable 5 leads, 91.5% against 86.7%.
Same board, both measured
Web design
Design Arena: websites
Claude Fable 5
Claude Fable 5 leads, 1295 against 1268.
Same board, both measured
Long documents
Listed context window
GPT-5.6 Terra
GPT-5.6 Terra takes 1.05M tokens against 1M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-5.6 Terra
Claude Fable 5 $0.035, GPT-5.6 Terra $0.0080 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GPT-5.6 Terra
Claude Fable 5 $2.16, GPT-5.6 Terra $0.42.
Published attempt cost and pass rate
At a glance
- Coding index
- Claude Fable 5 79.3 from 6 benchmarks, GPT-5.6 Terra 63.9 from 6 benchmarks.
- Widest category
- Agentic work, 14.0 points apart on average over 19 shared benchmarks.
- Widest single gap
- Kagi LLM Benchmark: Claude Fable 5 91.4% against GPT-5.6 Terra 51.3%, 40.1 points apart on a 0 to 100 scale.
- Output price
- Claude Fable 5 $50.00 against GPT-5.6 Terra $12.00 per million output tokens, 4.2x apart.
- Context window
- Claude Fable 5 takes 1M tokens, GPT-5.6 Terra 1.05M.
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 | Claude Fable 5 | Bars | GPT-5.6 Terra | Ties |
|---|---|---|---|---|
| Coding | 17 | 2 | 1 tie | |
| Intelligence | 2 | 0 | ||
| Agentic work | 18 | 1 | ||
| Reasoning | 14 | 2 | 1 tie | |
| Math | 6 | 0 | ||
| Knowledge | 11 | 0 | ||
| Web design | 9 | 0 | ||
| General | 32 | 3 | ||
| All shared boards | 109 | 8 | 2 ties |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index172
| Benchmark | Claude Fable 5 | GPT-5.6 Terra | Leader |
|---|---|---|---|
| LiveBench coding | 98.3% | 86.2% | Claude Fable 5 |
| LMArena WebDev | 1624 | 1522 | Claude Fable 5 |
| AA Coding Index | 76.5 | 76.7 | GPT-5.6 Terra |
| LiveCodeBench (Vals AI) | 89.8% | 85.9% | Claude Fable 5 |
| SWE-bench Verified (Vals AI) | 95.0% | 95.4% | GPT-5.6 Terra |
| Terminal-Bench 4 (Vals AI) | 41.4% | 22.7% | Claude Fable 5 |
| Vibe Code Bench (Vals AI) | 90.3% | 74.6% | Claude Fable 5 |
| Code migration (Vals AI) | 55.1% | 47.8% | Claude Fable 5 |
| ProgramBench (Vals AI) | 2.0% | 0.5% | Claude Fable 5 |
| IOI (Vals AI) | Not measured | 87.6% | Only GPT-5.6 Terra measured |
| Terminal-Bench 2.1 (Vals AI) | 80.5% | 77.5% | Claude Fable 5 |
| Vibe Code Bench 1-100 (Vals AI) | Not measured | 14.8% | Only GPT-5.6 Terra measured |
| SkillsBench (Vals AI) | Not measured | 58.9% | Only GPT-5.6 Terra measured |
| CyberBench (Vals AI) | Not measured | 72.1% | Only GPT-5.6 Terra measured |
| SciCode | 61.0% | 55.0% | Claude Fable 5 |
| Terminal-Bench Hard (Artificial Analysis run) | 62.9% | 62.9% | Tie |
| FrontierCode | 53.5% | 41.3% | Claude Fable 5 |
| FrontierSWE | 47.0% | Not measured | Only Claude Fable 5 measured |
| GSO-Bench | 78.4% | Not measured | Only Claude Fable 5 measured |
| WeirdML | 91.9% | 78.3% | Claude Fable 5 |
| MirrorCode | 63.9% | Not measured | Only Claude Fable 5 measured |
| ALE-Bench | 2041.3 | 1951.4 | Claude Fable 5 |
| LMArena Text: coding | 1518 | 1485 | Claude Fable 5 |
| LMArena Text: software and IT | 1513 | 1482 | Claude Fable 5 |
| LMArena WebDev: React | 1631 | 1513 | Claude Fable 5 |
| LMArena WebDev: HTML | 1651 | 1526 | Claude Fable 5 |
| LMArena WebDev: from an image | 1623 | 1541 | Claude Fable 5 |
| SciCode | Not measured | 10.8% | Only GPT-5.6 Terra measured |
| Neither model has a result on the other 11 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Claude Fable 5 | GPT-5.6 Terra | Leader |
|---|---|---|---|
| AA Intelligence Index | 49.6 | 42.1 | Claude Fable 5 |
| Epoch Capabilities Index | 163.4 | 160.1 | Claude Fable 5 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index181
| Benchmark | Claude Fable 5 | GPT-5.6 Terra | Leader |
|---|---|---|---|
| AA Agentic Index | 50.7 | 43.2 | Claude Fable 5 |
| Design Arena: agents building web apps | 1261 | 1120 | Claude Fable 5 |
| Design Arena: agents building full-stack apps | 1280 | 1061 | Claude Fable 5 |
| Design Arena: agents building mobile apps | 1264 | 1111 | Claude Fable 5 |
| Design Arena: agents building Android apps | 1284 | 1062 | Claude Fable 5 |
| Design Arena: agents building Godot games | 1292 | Not measured | Only Claude Fable 5 measured |
| Design Arena: agents building games | 1285 | 1083 | Claude Fable 5 |
| Design Arena: HTML slides | 1227 | 1058 | Claude Fable 5 |
| Design Arena: PowerPoint slides | 1270 | 1105 | Claude Fable 5 |
| LiveBench research | 86.9% | 60.4% | Claude Fable 5 |
| LiveBench browser | 92.0% | 84.0% | Claude Fable 5 |
| LiveBench long agentic | 53.2% | 48.4% | Claude Fable 5 |
| SREBench (Vals AI) | Not measured | 9.5% | Only GPT-5.6 Terra measured |
| Finance Agent (Vals AI) | 56.3% | 54.4% | Claude Fable 5 |
| Tax Agent Bench (Vals AI) | 38.3% | 30.0% | Claude Fable 5 |
| Legal Research Bench (Vals AI) | 49.5% | 41.4% | Claude Fable 5 |
| Excel Modeling Benchmark (Vals AI) | 73.7% | 66.2% | Claude Fable 5 |
| Terminal-Bench-Science (Vals AI) | 15.7% | 10.0% | Claude Fable 5 |
| τ²-Bench Telecom | 98.5% | 86.3% | Claude Fable 5 |
| APEX-Agents | 63.6% | 58.2% | Claude Fable 5 |
| Remote Labor Index | 16.1% | Not measured | Only Claude Fable 5 measured |
| BALROG | Not measured | 53.2% | Only GPT-5.6 Terra measured |
| PostTrainBench | 41.8% | Not measured | Only Claude Fable 5 measured |
| Vending-Bench 2 | $5,680 | $7,343 | GPT-5.6 Terra |
| LMArena Search | 1229 | Not measured | Only Claude Fable 5 measured |
| Neither model has a result on the other 16 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index142
| Benchmark | Claude Fable 5 | GPT-5.6 Terra | Leader |
|---|---|---|---|
| GPQA Diamond | 93.2% | 90.9% | Claude Fable 5 |
| LiveBench reasoning | 89.7% | 90.6% | GPT-5.6 Terra |
| MMMU Pro (Vals AI) | 89.3% | 86.5% | Claude Fable 5 |
| GPQA Diamond (Artificial Analysis run) | 92.6% | 92.5% | Claude Fable 5 |
| Humanity's Last Exam (Artificial Analysis run) | 55.5% | 42.9% | Claude Fable 5 |
| GPQA Diamond (Epoch AI run) | 85.9% | 93.3% | GPT-5.6 Terra |
| ARC-AGI | 98.5% | 96.5% | Claude Fable 5 |
| ARC-AGI-2 | 89.2% | 83.9% | Claude Fable 5 |
| SimpleBench | 81.9% | 48.9% | Claude Fable 5 |
| Chess puzzles | 47.0% | 47.0% | Tie |
| Mystery game puzzles | 52.0% | 35.0% | Claude Fable 5 |
| EBR-bench | 39.5% | Not measured | Only Claude Fable 5 measured |
| LMArena Text: hard prompts | 1507 | 1469 | Claude Fable 5 |
| LMArena Text: expert | 1534 | 1496 | Claude Fable 5 |
| LMArena Text: hard prompts in English | 1509 | 1465 | Claude Fable 5 |
| LMArena Vision: diagrams | 1357 | 1295 | Claude Fable 5 |
| LMArena Vision: homework | 1361 | 1312 | Claude Fable 5 |
| Kagi LLM Benchmark | 91.4% | 51.3% | Claude Fable 5 |
| Neither model has a result on the other 4 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | Claude Fable 5 | GPT-5.6 Terra | Leader |
|---|---|---|---|
| OTIS Mock AIME | 100.0% | 99.7% | Claude Fable 5 |
| FrontierMath, tiers 1 to 3 | 87.0% | 86.0% | Claude Fable 5 |
| FrontierMath, tier 4 | 90.2% | 70.7% | Claude Fable 5 |
| ProofBench | 95.0% | 74.0% | Claude Fable 5 |
| LMArena Text: math | 1516 | 1465 | Claude Fable 5 |
| LMArena Text: mathematical work | 1515 | 1478 | Claude Fable 5 |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index110
| Benchmark | Claude Fable 5 | GPT-5.6 Terra | Leader |
|---|---|---|---|
| MMLU Pro | 91.5% | 86.7% | Claude Fable 5 |
| LegalBench (Vals AI) | 88.6% | 85.1% | Claude Fable 5 |
| CorpFin (Vals AI) | 71.8% | 65.3% | Claude Fable 5 |
| TaxEval (Vals AI) | 76.9% | 76.2% | Claude Fable 5 |
| MortgageTax (Vals AI) | 68.9% | 67.3% | Claude Fable 5 |
| MedCode (Vals AI) | 56.1% | 43.4% | Claude Fable 5 |
| SimpleQA Verified | 70.7% | 43.2% | Claude Fable 5 |
| LMArena Text: science | 1506 | 1452 | Claude Fable 5 |
| LMArena Text: medicine and healthcare | 1480 | 1430 | Claude Fable 5 |
| LMArena Text: legal and government | 1498 | 1461 | Claude Fable 5 |
| LMArena Text: business and finance | 1480 | 1448 | Claude Fable 5 |
| FACTS Benchmark Suite | Not measured | 52.0% | Only GPT-5.6 Terra measured |
| Neither model has a result on the other 6 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index90
| Benchmark | Claude Fable 5 | GPT-5.6 Terra | Leader |
|---|---|---|---|
| Design Arena: websites | 1295 | 1268 | Claude Fable 5 |
| Design Arena: UI components | 1316 | 1256 | Claude Fable 5 |
| Design Arena: game dev | 1335 | 1229 | Claude Fable 5 |
| Design Arena: data viz | 1304 | 1240 | Claude Fable 5 |
| Design Arena: 3D | 1318 | 1134 | Claude Fable 5 |
| Design Arena: all code categories | 1310 | 1241 | Claude Fable 5 |
| Design Arena: SVG | 1302 | 1219 | Claude Fable 5 |
| Design Arena: ASCII art | 1329 | 1177 | Claude Fable 5 |
| LiveBench design | 84.7% | 76.0% | Claude Fable 5 |
GeneralLike-for-likePublic boards, shown as published, kept out of the index323
| Benchmark | Claude Fable 5 | GPT-5.6 Terra | Leader |
|---|---|---|---|
| LiveBench data analysis | 80.5% | 79.3% | Claude Fable 5 |
| Public Benefits Bench (Vals AI) | 70.4% | 62.4% | Claude Fable 5 |
| SAGE (Vals AI) | 51.9% | 47.0% | Claude Fable 5 |
| MedScribe (Vals AI) | 88.5% | 82.9% | Claude Fable 5 |
| Vals Multimodal Index | 74.2% | 65.1% | Claude Fable 5 |
| IFBench | 63.5% | 71.2% | GPT-5.6 Terra |
| Long context reasoning (AA-LCR) | 82.3% | 83.0% | GPT-5.6 Terra |
| DTBench | 98.4% | 93.3% | Claude Fable 5 |
| LMCA | 61.2% | 54.9% | Claude Fable 5 |
| Surface Evolver Bench | 95.0% | 83.8% | Claude Fable 5 |
| Furniture assembly | 35.8% | 54.2% | GPT-5.6 Terra |
| LMArena Text | 1491 | 1448 | Claude Fable 5 |
| LMArena Text: instruction following | 1502 | 1456 | Claude Fable 5 |
| LMArena Text: creative writing | 1494 | 1413 | Claude Fable 5 |
| LMArena Vision | 1325 | 1271 | Claude Fable 5 |
| LMArena Text: English | 1498 | 1449 | Claude Fable 5 |
| LMArena Text: non-English | 1482 | 1441 | Claude Fable 5 |
| LMArena Text: Chinese | 1543 | 1514 | Claude Fable 5 |
| LMArena Text: German | 1486 | 1460 | Claude Fable 5 |
| LMArena Text: Japanese | 1504 | 1454 | Claude Fable 5 |
| LMArena Text: Korean | 1487 | 1427 | Claude Fable 5 |
| LMArena Text: French | 1505 | 1466 | Claude Fable 5 |
| LMArena Text: Spanish | 1498 | 1451 | Claude Fable 5 |
| LMArena Text: Russian | 1503 | 1452 | Claude Fable 5 |
| LMArena Text: Polish | 1494 | 1446 | Claude Fable 5 |
| LMArena Text: multi-turn | 1504 | 1453 | Claude Fable 5 |
| LMArena Text: longer queries | 1510 | 1454 | Claude Fable 5 |
| LMArena Text: writing and language | 1501 | 1431 | Claude Fable 5 |
| LMArena Text: entertainment and media | 1482 | 1418 | Claude Fable 5 |
| LMArena Vision: English | 1323 | 1272 | Claude Fable 5 |
| LMArena Vision: Chinese | 1373 | 1337 | Claude Fable 5 |
| LMArena Vision: reading text | 1337 | 1278 | Claude Fable 5 |
| LMArena Vision: humor | 1341 | 1236 | Claude Fable 5 |
| LMArena Vision: creative writing | 1338 | 1248 | Claude Fable 5 |
| LMArena Document | 1496 | 1472 | Claude Fable 5 |
| Neither model has a result on the other 11 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Claude Fable 5 | GPT-5.6 Terra | 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-5.6 Terra costs less
Repository review
50K input + 3K output tokens
GPT-5.6 Terra costs less
Agent loop
200K input + 10K output tokens
GPT-5.6 Terra 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 | Anthropic Claude Fable 5 | OpenAI GPT-5.6 Terra |
|---|---|---|
| Maker | Anthropic | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | June 9, 2026 | July 9, 2026 |
| Context window | 1M | 1.05M |
| Input $/Mtok | $10.00 | $2.00 |
| Output $/Mtok | $50.00 | $12.00 |
| Coding index | 79.3 | 63.9 |
| Independent measurements behind it | 6 | 6 |
| Cost per solved task | $2.16 | $0.42 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Claude Fable 5 better than GPT-5.6 Terra for coding?
Pick Claude Fable 5 for coding: it scores 79.3 on the Vibe Coding Index against 63.9 for GPT-5.6 Terra. Pick GPT-5.6 Terra for output tokens 4.2x cheaper ($12.00 vs $50.00 per million) and a lower cost per solved task ($0.42 against $2.16).
Which is cheaper, Claude Fable 5 or GPT-5.6 Terra?
Claude Fable 5 costs $10.00 per million input tokens and $50.00 per million output tokens; GPT-5.6 Terra costs $2.00 and $12.00. Per solved SWE-bench Verified task, Claude Fable 5 costs $2.16 and GPT-5.6 Terra $0.42.
Which has the bigger context window, Claude Fable 5 or GPT-5.6 Terra?
GPT-5.6 Terra, with 1.05M tokens against 1M.
How many benchmarks is the Claude Fable 5 vs GPT-5.6 Terra comparison based on?
119 benchmarks have a result for both models as of October 22, 2026. Claude Fable 5's coding index rests on 6 independent benchmarks and GPT-5.6 Terra's on 6. Where only one model has a result, the page says so instead of naming a winner.