Claude Opus 4.5 vs GPT-5 Nano
Updated October 21, 2026. 75 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick Claude Opus 4.5 for coding: it scores 66.8 on the Vibe Coding Index against 34.8 for GPT-5 Nano.
Pick GPT-5 Nano when output tokens cost 62.5x less, or when a solved task costs $0.109 against $1.37.
- Shared results
- 75
- Claude Opus 4.5 only
- 30
- GPT-5 Nano only
- 10
- Like-for-like categories
- 8 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
Claude Opus 4.5
Claude Opus 4.5 scores 66.8 against 34.8.
2 and 1 benchmarks behind the scores
Intelligence
AA Intelligence Index
Claude Opus 4.5
Claude Opus 4.5 leads, 29.1 against 13.0.
Same board, both measured
Reasoning
GPQA Diamond
Claude Opus 4.5
Claude Opus 4.5 leads, 85.9% against 63.4%.
Same board, both measured
Math
AIME
Claude Opus 4.5
Claude Opus 4.5 leads, 95.4% against 81.2%.
Same board, both measured
Knowledge
MMLU Pro
Claude Opus 4.5
Claude Opus 4.5 leads, 87.3% against 76.1%.
Same board, both measured
Web design
Design Arena: websites
Claude Opus 4.5
Claude Opus 4.5 leads, 1247 against 1101.
Same board, both measured
Long documents
Listed context window
GPT-5 Nano
GPT-5 Nano takes 400K tokens against 200K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-5 Nano
Claude Opus 4.5 $0.018, GPT-5 Nano $0.0003 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GPT-5 Nano
Claude Opus 4.5 $1.37, GPT-5 Nano $0.109.
Published attempt cost and pass rate
At a glance
- Coding index
- Claude Opus 4.5 66.8 from 2 benchmarks, GPT-5 Nano 34.8 from 1 benchmark.
- Widest category
- Agentic work, 52.9 points apart on average over 1 shared benchmark.
- Widest single gap
- ARC-AGI: Claude Opus 4.5 80.0% against GPT-5 Nano 20.7%, 59.3 points apart on a 0 to 100 scale.
- Output price
- Claude Opus 4.5 $25.00 against GPT-5 Nano $0.40 per million output tokens, 62.5x apart.
- Context window
- Claude Opus 4.5 takes 200K tokens, GPT-5 Nano 400K.
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 4.5 | Bars | GPT-5 Nano | Ties |
|---|---|---|---|---|
| Coding | 8 | 0 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 12 | 1 | ||
| Math | 10 | 0 | ||
| Knowledge | 13 | 0 | ||
| Web design | 6 | 0 | ||
| General | 21 | 1 | ||
| All shared boards | 73 | 2 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index80
| Benchmark | Claude Opus 4.5 | GPT-5 Nano | Leader |
|---|---|---|---|
| SWE-bench Verified | 79.2% | 34.8% | Claude Opus 4.5 |
| SWE-bench (full test set) | 52.6% | Not measured | Only Claude Opus 4.5 measured |
| SWE-bench Multilingual | 70.7% | Not measured | Only Claude Opus 4.5 measured |
| LMArena WebDev | 1494 | Not measured | Only Claude Opus 4.5 measured |
| LiveCodeBench (Vals AI) | 83.7% | 70.2% | Claude Opus 4.5 |
| SWE-bench Verified (Vals AI) | 76.4% | Not measured | Only Claude Opus 4.5 measured |
| Vibe Code Bench (Vals AI) | 20.6% | Not measured | Only Claude Opus 4.5 measured |
| LiveCodeBench (Artificial Analysis run) | 87.1% | 78.9% | Claude Opus 4.5 |
| Terminal-Bench Hard (Artificial Analysis run) | 47.0% | 17.4% | Claude Opus 4.5 |
| Terminal-Bench | 63.1% | 21.8% | Claude Opus 4.5 |
| GSO-Bench | 26.5% | Not measured | Only Claude Opus 4.5 measured |
| WeirdML | 63.7% | 38.1% | Claude Opus 4.5 |
| Cybench | 82.0% | Not measured | Only Claude Opus 4.5 measured |
| LMArena Text: coding | 1503 | 1351 | Claude Opus 4.5 |
| LMArena Text: software and IT | 1483 | 1345 | Claude Opus 4.5 |
| LMArena WebDev: React | 1478 | Not measured | Only Claude Opus 4.5 measured |
| LMArena WebDev: HTML | 1510 | Not measured | Only Claude Opus 4.5 measured |
| Neither model has a result on the other 21 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Claude Opus 4.5 | GPT-5 Nano | Leader |
|---|---|---|---|
| AA Intelligence Index | 29.1 | 13.0 | Claude Opus 4.5 |
| Epoch Capabilities Index | 150.1 | 139.2 | Claude Opus 4.5 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Claude Opus 4.5 | GPT-5 Nano | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | 1159 | Not measured | Only Claude Opus 4.5 measured |
| Design Arena: agents building full-stack apps | 1156 | Not measured | Only Claude Opus 4.5 measured |
| Design Arena: agents building mobile apps | 1189 | Not measured | Only Claude Opus 4.5 measured |
| Design Arena: agents building Android apps | 1181 | Not measured | Only Claude Opus 4.5 measured |
| τ²-Bench Telecom | 89.5% | 36.5% | Claude Opus 4.5 |
| METR time horizons | 75.0% | Not measured | Only Claude Opus 4.5 measured |
| Remote Labor Index | 3.8% | Not measured | Only Claude Opus 4.5 measured |
| GDPval | 45.5% | Not measured | Only Claude Opus 4.5 measured |
| BALROG | 43.5% | Not measured | Only Claude Opus 4.5 measured |
| DeepResearch Bench | 54.8% | Not measured | Only Claude Opus 4.5 measured |
| OSWorld | 66.3% | Not measured | Only Claude Opus 4.5 measured |
| Vending-Bench 2 | $4,967 | Not measured | Only Claude Opus 4.5 measured |
| Neither model has a result on the other 27 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index121
| Benchmark | Claude Opus 4.5 | GPT-5 Nano | Leader |
|---|---|---|---|
| GPQA Diamond | 85.9% | 63.4% | Claude Opus 4.5 |
| MMMU Pro (Vals AI) | 83.0% | 70.9% | Claude Opus 4.5 |
| GPQA Diamond (Artificial Analysis run) | 86.6% | 67.6% | Claude Opus 4.5 |
| Humanity's Last Exam (Artificial Analysis run) | 30.1% | 9.5% | Claude Opus 4.5 |
| GPQA Diamond (Epoch AI run) | 86.0% | 69.4% | Claude Opus 4.5 |
| ARC-AGI | 80.0% | 20.7% | Claude Opus 4.5 |
| ARC-AGI-2 | 37.6% | 2.6% | Claude Opus 4.5 |
| SimpleBench | 62.0% | Not measured | Only Claude Opus 4.5 measured |
| Chess puzzles | 14.0% | 22.0% | GPT-5 Nano |
| Mystery game puzzles | 22.0% | 9.0% | Claude Opus 4.5 |
| EBR-bench | 14.3% | Not measured | Only Claude Opus 4.5 measured |
| VPCT | 40.0% | 37.2% | Claude Opus 4.5 |
| LMArena Text: hard prompts | 1476 | 1328 | Claude Opus 4.5 |
| LMArena Text: expert | 1487 | 1321 | Claude Opus 4.5 |
| LMArena Text: hard prompts in English | 1482 | 1338 | Claude Opus 4.5 |
| LMArena Vision: diagrams | Not measured | 1141 | Only GPT-5 Nano measured |
| LMArena Vision: homework | Not measured | 1157 | Only GPT-5 Nano measured |
| Neither model has a result on the other 4 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index100
| Benchmark | Claude Opus 4.5 | GPT-5 Nano | Leader |
|---|---|---|---|
| AIME | 95.4% | 81.2% | Claude Opus 4.5 |
| MATH 500 (Vals AI) | Not measured | 93.8% | Only GPT-5 Nano measured |
| MGSM (Vals AI) | 95.2% | 89.3% | Claude Opus 4.5 |
| AA Math Index | 91.3 | 83.7 | Claude Opus 4.5 |
| AIME 2025 (Artificial Analysis run) | 91.3% | 83.7% | Claude Opus 4.5 |
| OTIS Mock AIME | 86.1% | 81.1% | Claude Opus 4.5 |
| FrontierMath, tiers 1 to 3 | 34.4% | 20.0% | Claude Opus 4.5 |
| FrontierMath, tier 4 | 4.9% | 2.4% | Claude Opus 4.5 |
| ProofBench | 36.0% | 12.0% | Claude Opus 4.5 |
| MATH level 5 | Not measured | 95.2% | Only GPT-5 Nano measured |
| LMArena Text: math | 1463 | 1317 | Claude Opus 4.5 |
| LMArena Text: mathematical work | 1468 | 1326 | Claude Opus 4.5 |
| Neither model has a result on the other 1 board here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index130
| Benchmark | Claude Opus 4.5 | GPT-5 Nano | Leader |
|---|---|---|---|
| MMLU Pro | 87.3% | 76.1% | Claude Opus 4.5 |
| LegalBench (Vals AI) | 84.6% | 50.1% | Claude Opus 4.5 |
| CaseLaw (Vals AI) | 62.6% | 52.6% | Claude Opus 4.5 |
| CorpFin (Vals AI) | 65.1% | Not measured | Only Claude Opus 4.5 measured |
| TaxEval (Vals AI) | 74.9% | 67.4% | Claude Opus 4.5 |
| MortgageTax (Vals AI) | 68.7% | 53.6% | Claude Opus 4.5 |
| MedQA (Vals AI) | 95.9% | 93.3% | Claude Opus 4.5 |
| MedCode (Vals AI) | 49.2% | 30.4% | Claude Opus 4.5 |
| MMLU Pro (Artificial Analysis run) | 89.5% | 78.0% | Claude Opus 4.5 |
| SimpleQA Verified | 45.7% | 11.7% | Claude Opus 4.5 |
| GeoBench | 75.0% | Not measured | Only Claude Opus 4.5 measured |
| LMArena Text: science | 1454 | 1318 | Claude Opus 4.5 |
| LMArena Text: medicine and healthcare | 1444 | 1335 | Claude Opus 4.5 |
| LMArena Text: legal and government | 1459 | 1298 | Claude Opus 4.5 |
| LMArena Text: business and finance | 1448 | 1311 | Claude Opus 4.5 |
| Neither model has a result on the other 2 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | Claude Opus 4.5 | GPT-5 Nano | Leader |
|---|---|---|---|
| Design Arena: websites | 1247 | 1101 | Claude Opus 4.5 |
| Design Arena: UI components | 1238 | 1066 | Claude Opus 4.5 |
| Design Arena: game dev | 1229 | 1045 | Claude Opus 4.5 |
| Design Arena: data viz | 1243 | 1056 | Claude Opus 4.5 |
| Design Arena: 3D | 1214 | 966 | Claude Opus 4.5 |
| Design Arena: all code categories | 1242 | 1084 | Claude Opus 4.5 |
| Design Arena: SVG | 1180 | Not measured | Only Claude Opus 4.5 measured |
| Design Arena: ASCII art | 1190 | Not measured | Only Claude Opus 4.5 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index211
| Benchmark | Claude Opus 4.5 | GPT-5 Nano | Leader |
|---|---|---|---|
| SAGE (Vals AI) | 52.1% | 30.4% | Claude Opus 4.5 |
| MedScribe (Vals AI) | 85.3% | 72.9% | Claude Opus 4.5 |
| IFBench | 58.0% | 67.6% | GPT-5 Nano |
| Long context reasoning (AA-LCR) | 77.3% | 45.0% | Claude Opus 4.5 |
| DTBench | 89.9% | 62.7% | Claude Opus 4.5 |
| LMCA | 44.5% | 7.9% | Claude Opus 4.5 |
| Fiction.LiveBench | Not measured | 44.4% | Only GPT-5 Nano measured |
| CL-bench | 21.1% | Not measured | Only Claude Opus 4.5 measured |
| Furniture assembly | 28.3% | Not measured | Only Claude Opus 4.5 measured |
| LMArena Text | 1451 | 1320 | Claude Opus 4.5 |
| LMArena Text: instruction following | 1478 | 1306 | Claude Opus 4.5 |
| LMArena Text: creative writing | 1445 | 1249 | Claude Opus 4.5 |
| LMArena Vision | Not measured | 1159 | Only GPT-5 Nano measured |
| LMArena Text: English | 1461 | 1321 | Claude Opus 4.5 |
| LMArena Text: non-English | 1438 | 1313 | Claude Opus 4.5 |
| LMArena Text: Chinese | 1470 | 1356 | Claude Opus 4.5 |
| LMArena Text: German | 1449 | 1327 | Claude Opus 4.5 |
| LMArena Text: Japanese | 1417 | 1227 | Claude Opus 4.5 |
| LMArena Text: Korean | 1424 | 1269 | Claude Opus 4.5 |
| LMArena Text: French | 1471 | Not measured | Only Claude Opus 4.5 measured |
| LMArena Text: Spanish | 1457 | 1360 | Claude Opus 4.5 |
| LMArena Text: Russian | 1447 | 1296 | Claude Opus 4.5 |
| LMArena Text: Polish | 1439 | 1314 | Claude Opus 4.5 |
| LMArena Text: multi-turn | 1466 | 1311 | Claude Opus 4.5 |
| LMArena Text: longer queries | 1479 | 1312 | Claude Opus 4.5 |
| LMArena Text: writing and language | 1452 | 1283 | Claude Opus 4.5 |
| LMArena Text: entertainment and media | 1434 | 1268 | Claude Opus 4.5 |
| LMArena Vision: English | Not measured | 1170 | Only GPT-5 Nano measured |
| LMArena Vision: Chinese | Not measured | 1176 | Only GPT-5 Nano measured |
| LMArena Vision: reading text | Not measured | 1152 | Only GPT-5 Nano measured |
| LMArena Vision: humor | Not measured | 1110 | Only GPT-5 Nano measured |
| LMArena Document | 1462 | Not measured | Only Claude Opus 4.5 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 | Claude Opus 4.5 | GPT-5 Nano | 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 Nano costs less
Repository review
50K input + 3K output tokens
GPT-5 Nano costs less
Agent loop
200K input + 10K output tokens
GPT-5 Nano 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 4.5 | OpenAI GPT-5 Nano |
|---|---|---|
| Maker | Anthropic | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | November 24, 2025 | August 7, 2025 |
| Context window | 200K | 400K |
| Input $/Mtok | $5.00 | $0.05 |
| Output $/Mtok | $25.00 | $0.40 |
| Coding index | 66.8 | 34.8 |
| Independent measurements behind it | 2 | 1 |
| Cost per solved task | $1.37 | $0.109 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Claude Opus 4.5 better than GPT-5 Nano for coding?
Pick Claude Opus 4.5 for coding: it scores 66.8 on the Vibe Coding Index against 34.8 for GPT-5 Nano. Pick GPT-5 Nano when output tokens cost 62.5x less, or when a solved task costs $0.109 against $1.37.
Which is cheaper, Claude Opus 4.5 or GPT-5 Nano?
Claude Opus 4.5 costs $5.00 per million input tokens and $25.00 per million output tokens; GPT-5 Nano costs $0.05 and $0.40. Per solved SWE-bench Verified task, Claude Opus 4.5 costs $1.37 and GPT-5 Nano $0.109.
Which has the bigger context window, Claude Opus 4.5 or GPT-5 Nano?
GPT-5 Nano, with 400K tokens against 200K.
How many benchmarks is the Claude Opus 4.5 vs GPT-5 Nano comparison based on?
75 benchmarks have a result for both models as of October 21, 2026. Claude Opus 4.5's coding index rests on 2 independent benchmarks and GPT-5 Nano's on 1. Where only one model has a result, the page says so instead of naming a winner.