Claude Opus 4.7 vs GPT-5.6 Sol
Updated October 21, 2026. 95 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick GPT-5.6 Sol for coding: it scores 76.7 on the Vibe Coding Index against 51.1 for Claude Opus 4.7.
Pick Claude Opus 4.7 when what matters is LiveCodeBench (Vals AI) or MirrorCode, where it leads.
- Shared results
- 95
- Claude Opus 4.7 only
- 6
- GPT-5.6 Sol only
- 41
- Like-for-like categories
- 7 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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, four public coding benchmarks
GPT-5.6 Sol
GPT-5.6 Sol scores 76.7 against 51.1.
1 and 2 benchmarks behind the scores
Intelligence
AA Intelligence Index
GPT-5.6 Sol
GPT-5.6 Sol leads, 47.0 against 40.7.
Same board, both measured
Agentic work
AA Agentic Index
GPT-5.6 Sol
GPT-5.6 Sol leads, 50.2 against 38.6.
Same board, both measured
Reasoning
GPQA Diamond
GPT-5.6 Sol
GPT-5.6 Sol leads, 95.2% against 90.2%.
Same board, both measured
Math
AIME
No call
Only Claude Opus 4.7 has a AIME result, 96.3%.
Directional only
Knowledge
MMLU Pro
Claude Opus 4.7
Claude Opus 4.7 leads, 89.9% against 89.1%.
Same board, both measured
Web design
Design Arena: websites
No call
Only GPT-5.6 Sol has a Design Arena: websites result, 1321.
Directional only
Long documents
Listed context window
GPT-5.6 Sol
GPT-5.6 Sol takes 1.05M tokens against 1M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-5.6 Sol
Claude Opus 4.7 $0.018, GPT-5.6 Sol $0.014 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GPT-5.6 Sol
Claude Opus 4.7 $2.95, GPT-5.6 Sol $1.20.
Published attempt cost and pass rate
At a glance
- Coding index
- Claude Opus 4.7 51.1 from 1 benchmark, GPT-5.6 Sol 76.7 from 2 benchmarks.
- Widest category
- Math, 18.0 points apart on average over 6 shared benchmarks.
- Widest single gap
- FrontierMath, tier 4: Claude Opus 4.7 31.7% against GPT-5.6 Sol 82.9%, 51.2 points apart on a 0 to 100 scale.
- Output price
- Claude Opus 4.7 $25.00 against GPT-5.6 Sol $20.00 per million output tokens, 1.3x apart.
- Context window
- Claude Opus 4.7 takes 1M tokens, GPT-5.6 Sol 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 Opus 4.7 | Bars | GPT-5.6 Sol | Ties |
|---|---|---|---|---|
| Coding | 4 | 14 | ||
| Intelligence | 0 | 2 | ||
| Agentic work | 2 | 8 | ||
| Reasoning | 5 | 11 | ||
| Math | 2 | 4 | ||
| Knowledge | 9 | 2 | ||
| General | 25 | 7 | ||
| All shared boards | 47 | 48 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index414
| Benchmark | Claude Opus 4.7 | GPT-5.6 Sol | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 89.7% | Only GPT-5.6 Sol measured |
| LMArena WebDev | 1558 | 1616 | GPT-5.6 Sol |
| AA Coding Index | 73.6 | 77.4 | GPT-5.6 Sol |
| LiveCodeBench (Vals AI) | 85.1% | 82.6% | Claude Opus 4.7 |
| SWE-bench Verified (Vals AI) | 82.0% | 96.2% | GPT-5.6 Sol |
| Terminal-Bench 4 (Vals AI) | Not measured | 37.9% | Only GPT-5.6 Sol measured |
| Vibe Code Bench (Vals AI) | 71.0% | 80.5% | GPT-5.6 Sol |
| Code migration (Vals AI) | 43.9% | 52.9% | GPT-5.6 Sol |
| ProgramBench (Vals AI) | 0.0% | 1.5% | GPT-5.6 Sol |
| IOI (Vals AI) | Not measured | 91.2% | Only GPT-5.6 Sol measured |
| Terminal-Bench 2.1 (Vals AI) | 68.5% | 85.8% | GPT-5.6 Sol |
| Vibe Code Bench 1-100 (Vals AI) | Not measured | 20.0% | Only GPT-5.6 Sol measured |
| SkillsBench (Vals AI) | Not measured | 54.1% | Only GPT-5.6 Sol measured |
| CyberBench (Vals AI) | Not measured | 76.3% | Only GPT-5.6 Sol measured |
| SciCode | Not measured | 57.8% | Only GPT-5.6 Sol measured |
| Terminal-Bench Hard (Artificial Analysis run) | 54.5% | 65.9% | GPT-5.6 Sol |
| Terminal-Bench | 80.2% | Not measured | Only Claude Opus 4.7 measured |
| FrontierCode | 38.5% | 47.5% | GPT-5.6 Sol |
| FrontierSWE | Not measured | 32.2% | Only GPT-5.6 Sol measured |
| GSO-Bench | 44.1% | 76.5% | GPT-5.6 Sol |
| WeirdML | 76.4% | 88.8% | GPT-5.6 Sol |
| MirrorCode | 31.1% | 20.0% | Claude Opus 4.7 |
| ALE-Bench | Not measured | 2176.9 | Only GPT-5.6 Sol measured |
| LMArena Text: coding | 1519 | 1496 | Claude Opus 4.7 |
| LMArena Text: software and IT | 1516 | 1489 | Claude Opus 4.7 |
| LMArena WebDev: React | 1560 | 1620 | GPT-5.6 Sol |
| LMArena WebDev: HTML | 1554 | 1618 | GPT-5.6 Sol |
| LMArena WebDev: from an image | 1577 | 1609 | GPT-5.6 Sol |
| Neither model has a result on the other 10 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Claude Opus 4.7 | GPT-5.6 Sol | Leader |
|---|---|---|---|
| AA Intelligence Index | 40.7 | 47.0 | GPT-5.6 Sol |
| Epoch Capabilities Index | 156.8 | 162.8 | GPT-5.6 Sol |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index28
| Benchmark | Claude Opus 4.7 | GPT-5.6 Sol | Leader |
|---|---|---|---|
| AA Agentic Index | 38.6 | 50.2 | GPT-5.6 Sol |
| Design Arena: agents building web apps | Not measured | 1245 | Only GPT-5.6 Sol measured |
| Design Arena: agents building full-stack apps | Not measured | 1261 | Only GPT-5.6 Sol measured |
| Design Arena: agents building mobile apps | Not measured | 1249 | Only GPT-5.6 Sol measured |
| Design Arena: agents building Android apps | Not measured | 1246 | Only GPT-5.6 Sol measured |
| Design Arena: agents building Godot games | Not measured | 1260 | Only GPT-5.6 Sol measured |
| Design Arena: agents building games | Not measured | 1267 | Only GPT-5.6 Sol measured |
| Design Arena: HTML slides | Not measured | 1286 | Only GPT-5.6 Sol measured |
| Design Arena: PowerPoint slides | Not measured | 1240 | Only GPT-5.6 Sol measured |
| LiveBench research | Not measured | 54.5% | Only GPT-5.6 Sol measured |
| LiveBench browser | Not measured | 88.0% | Only GPT-5.6 Sol measured |
| LiveBench long agentic | Not measured | 48.5% | Only GPT-5.6 Sol measured |
| SREBench (Vals AI) | Not measured | 30.5% | Only GPT-5.6 Sol measured |
| Finance Agent (Vals AI) | 51.5% | 53.8% | GPT-5.6 Sol |
| Tax Agent Bench (Vals AI) | 23.7% | 31.1% | GPT-5.6 Sol |
| Legal Research Bench (Vals AI) | 38.5% | 48.1% | GPT-5.6 Sol |
| Excel Modeling Benchmark (Vals AI) | 63.7% | 72.3% | GPT-5.6 Sol |
| BioMysteryBench (Vals AI) | Not measured | 71.1% | Only GPT-5.6 Sol measured |
| Terminal-Bench-Science (Vals AI) | Not measured | 20.0% | Only GPT-5.6 Sol measured |
| Vals Science Index | Not measured | 41.5% | Only GPT-5.6 Sol measured |
| Time Horizon Index (Vals AI) | Not measured | 23.8% | Only GPT-5.6 Sol measured |
| CUA-bench (Vals AI) | Not measured | 8.3% | Only GPT-5.6 Sol measured |
| τ²-Bench Telecom | 88.6% | 85.1% | Claude Opus 4.7 |
| APEX-Agents | 49.2% | Not measured | Only Claude Opus 4.7 measured |
| OSWorld 2.0 | 18.2% | 27.3% | GPT-5.6 Sol |
| BALROG | Not measured | 60.0% | Only GPT-5.6 Sol measured |
| PostTrainBench | 28.6% | 36.2% | GPT-5.6 Sol |
| Vending-Bench 2 | $10,937 | $9,619 | Claude Opus 4.7 |
| LMArena Search | 1230 | 1261 | GPT-5.6 Sol |
| Neither model has a result on the other 10 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index511
| Benchmark | Claude Opus 4.7 | GPT-5.6 Sol | Leader |
|---|---|---|---|
| GPQA Diamond | 90.2% | 95.2% | GPT-5.6 Sol |
| LiveBench reasoning | Not measured | 91.7% | Only GPT-5.6 Sol measured |
| MysteryMechanism (Vals AI) | Not measured | 33.3% | Only GPT-5.6 Sol measured |
| MMMU Pro (Vals AI) | 85.5% | 88.8% | GPT-5.6 Sol |
| GPQA Diamond (Artificial Analysis run) | 91.4% | 94.1% | GPT-5.6 Sol |
| Humanity's Last Exam (Artificial Analysis run) | 42.3% | 49.5% | GPT-5.6 Sol |
| GPQA Diamond (Epoch AI run) | 90.2% | 93.5% | GPT-5.6 Sol |
| ARC-AGI | 93.5% | 97.5% | GPT-5.6 Sol |
| ARC-AGI-2 | 75.8% | 92.5% | GPT-5.6 Sol |
| SimpleBench | 61.7% | 64.8% | GPT-5.6 Sol |
| Chess puzzles | 41.0% | 59.0% | GPT-5.6 Sol |
| Mystery game puzzles | 28.0% | 58.0% | GPT-5.6 Sol |
| EBR-bench | 19.0% | 44.8% | GPT-5.6 Sol |
| LMArena Text: hard prompts | 1506 | 1483 | Claude Opus 4.7 |
| LMArena Text: expert | 1521 | 1517 | Claude Opus 4.7 |
| LMArena Text: hard prompts in English | 1508 | 1477 | Claude Opus 4.7 |
| LMArena Vision: diagrams | 1336 | 1305 | Claude Opus 4.7 |
| LMArena Vision: homework | 1337 | 1305 | Claude Opus 4.7 |
| Neither model has a result on the other 3 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index24
| Benchmark | Claude Opus 4.7 | GPT-5.6 Sol | Leader |
|---|---|---|---|
| AIME | 96.3% | Not measured | Only Claude Opus 4.7 measured |
| OTIS Mock AIME | 97.8% | 100.0% | GPT-5.6 Sol |
| FrontierMath, tiers 1 to 3 | 70.2% | 89.1% | GPT-5.6 Sol |
| FrontierMath, tier 4 | 31.7% | 82.9% | GPT-5.6 Sol |
| ProofBench | 54.0% | 83.0% | GPT-5.6 Sol |
| LMArena Text: math | 1498 | 1473 | Claude Opus 4.7 |
| LMArena Text: mathematical work | 1509 | 1496 | Claude Opus 4.7 |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index92
| Benchmark | Claude Opus 4.7 | GPT-5.6 Sol | Leader |
|---|---|---|---|
| MMLU Pro | 89.9% | 89.1% | Claude Opus 4.7 |
| LegalBench (Vals AI) | 85.3% | 87.0% | GPT-5.6 Sol |
| CaseLaw (Vals AI) | 68.4% | Not measured | Only Claude Opus 4.7 measured |
| CorpFin (Vals AI) | 66.1% | 64.4% | Claude Opus 4.7 |
| TaxEval (Vals AI) | 75.3% | 74.8% | Claude Opus 4.7 |
| MortgageTax (Vals AI) | 70.3% | 67.3% | Claude Opus 4.7 |
| MedCode (Vals AI) | 54.9% | 44.0% | Claude Opus 4.7 |
| SimpleQA Verified | 51.7% | 69.7% | GPT-5.6 Sol |
| LMArena Text: science | 1508 | 1455 | Claude Opus 4.7 |
| LMArena Text: medicine and healthcare | 1495 | 1431 | Claude Opus 4.7 |
| LMArena Text: legal and government | 1504 | 1476 | Claude Opus 4.7 |
| LMArena Text: business and finance | 1490 | 1457 | Claude Opus 4.7 |
| Neither model has a result on the other 5 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | Claude Opus 4.7 | GPT-5.6 Sol | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1321 | Only GPT-5.6 Sol measured |
| Design Arena: UI components | Not measured | 1338 | Only GPT-5.6 Sol measured |
| Design Arena: game dev | Not measured | 1368 | Only GPT-5.6 Sol measured |
| Design Arena: data viz | Not measured | 1323 | Only GPT-5.6 Sol measured |
| Design Arena: 3D | Not measured | 1385 | Only GPT-5.6 Sol measured |
| Design Arena: all code categories | Not measured | 1341 | Only GPT-5.6 Sol measured |
| Design Arena: SVG | Not measured | 1316 | Only GPT-5.6 Sol measured |
| Design Arena: ASCII art | Not measured | 1302 | Only GPT-5.6 Sol measured |
| LiveBench design | Not measured | 81.6% | Only GPT-5.6 Sol measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index257
| Benchmark | Claude Opus 4.7 | GPT-5.6 Sol | Leader |
|---|---|---|---|
| LiveBench data analysis | Not measured | 79.8% | Only GPT-5.6 Sol measured |
| Public Benefits Bench (Vals AI) | Not measured | 66.5% | Only GPT-5.6 Sol measured |
| SAGE (Vals AI) | 56.1% | 52.6% | Claude Opus 4.7 |
| MedScribe (Vals AI) | 83.0% | 85.2% | GPT-5.6 Sol |
| Vals Multimodal Index | 67.4% | 72.6% | GPT-5.6 Sol |
| IFBench | 58.6% | 72.7% | GPT-5.6 Sol |
| Long context reasoning (AA-LCR) | 78.7% | 84.0% | GPT-5.6 Sol |
| DTBench | 94.7% | 95.7% | GPT-5.6 Sol |
| LMCA | 52.2% | 58.5% | GPT-5.6 Sol |
| Surface Evolver Bench | Not measured | 93.1% | Only GPT-5.6 Sol measured |
| Furniture assembly | 33.3% | 56.7% | GPT-5.6 Sol |
| ExploitBench | 26.5% | Not measured | Only Claude Opus 4.7 measured |
| LMArena Text | 1490 | 1457 | Claude Opus 4.7 |
| LMArena Text: instruction following | 1498 | 1483 | Claude Opus 4.7 |
| LMArena Text: creative writing | 1486 | 1449 | Claude Opus 4.7 |
| LMArena Vision | 1317 | 1281 | Claude Opus 4.7 |
| LMArena Text: English | 1496 | 1457 | Claude Opus 4.7 |
| LMArena Text: non-English | 1480 | 1451 | Claude Opus 4.7 |
| LMArena Text: Chinese | 1531 | 1525 | Claude Opus 4.7 |
| LMArena Text: German | 1495 | 1476 | Claude Opus 4.7 |
| LMArena Text: Japanese | 1474 | 1472 | Claude Opus 4.7 |
| LMArena Text: Korean | 1464 | 1442 | Claude Opus 4.7 |
| LMArena Text: French | 1503 | 1472 | Claude Opus 4.7 |
| LMArena Text: Spanish | 1494 | 1443 | Claude Opus 4.7 |
| LMArena Text: Russian | 1494 | 1471 | Claude Opus 4.7 |
| LMArena Text: Polish | 1489 | 1459 | Claude Opus 4.7 |
| LMArena Text: multi-turn | 1505 | 1461 | Claude Opus 4.7 |
| LMArena Text: longer queries | 1505 | 1481 | Claude Opus 4.7 |
| LMArena Text: writing and language | 1492 | 1462 | Claude Opus 4.7 |
| LMArena Text: entertainment and media | 1474 | 1450 | Claude Opus 4.7 |
| LMArena Vision: English | 1322 | 1280 | Claude Opus 4.7 |
| LMArena Vision: Chinese | 1371 | 1313 | Claude Opus 4.7 |
| LMArena Vision: reading text | 1327 | 1290 | Claude Opus 4.7 |
| LMArena Vision: humor | 1309 | 1246 | Claude Opus 4.7 |
| LMArena Vision: recognizing things | 1278 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Vision: creative writing | 1326 | 1283 | Claude Opus 4.7 |
| LMArena Document | 1495 | 1483 | Claude Opus 4.7 |
| 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 | Claude Opus 4.7 | GPT-5.6 Sol | 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 Sol costs less
Repository review
50K input + 3K output tokens
GPT-5.6 Sol costs less
Agent loop
200K input + 10K output tokens
GPT-5.6 Sol 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 Opus 4.7 | OpenAI GPT-5.6 Sol |
|---|---|---|
| Maker | Anthropic | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | April 16, 2026 | July 9, 2026 |
| Context window | 1M | 1.05M |
| Input $/Mtok | $5.00 | $4.00 |
| Output $/Mtok | $25.00 | $20.00 |
| Coding index | 51.1 | 76.7 |
| Independent measurements behind it | 1 | 2 |
| Cost per solved task | $2.95 | $1.20 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Claude Opus 4.7 better than GPT-5.6 Sol for coding?
Pick GPT-5.6 Sol for coding: it scores 76.7 on the Vibe Coding Index against 51.1 for Claude Opus 4.7. Pick Claude Opus 4.7 when what matters is LiveCodeBench (Vals AI) or MirrorCode, where it leads.
Which is cheaper, Claude Opus 4.7 or GPT-5.6 Sol?
Claude Opus 4.7 costs $5.00 per million input tokens and $25.00 per million output tokens; GPT-5.6 Sol costs $4.00 and $20.00. Per solved SWE-bench Verified task, Claude Opus 4.7 costs $2.95 and GPT-5.6 Sol $1.20.
Which has the bigger context window, Claude Opus 4.7 or GPT-5.6 Sol?
GPT-5.6 Sol, with 1.05M tokens against 1M.
How many benchmarks is the Claude Opus 4.7 vs GPT-5.6 Sol comparison based on?
95 benchmarks have a result for both models as of October 21, 2026. Claude Opus 4.7's coding index rests on 1 independent benchmark and GPT-5.6 Sol's on 2. Where only one model has a result, the page says so instead of naming a winner.