Claude Opus 4.7 vs DeepSeek V3.2
Claude Opus 4.7 leads DeepSeek V3.2 on coding, 67.5 to 30.0 on the Vibe Coding Index, and DeepSeek V3.2 costs 28.4x less per output token ($0.88 vs $25.00 per million). Updated October 21, 2026; 57 benchmarks have a result for both.
Claude Opus 4.7 or DeepSeek V3.2: which to pick
Pick Claude Opus 4.7 for coding: it scores 67.5 on the Vibe Coding Index against 30.0 for DeepSeek V3.2.
Pick DeepSeek V3.2 for output tokens 28.4x cheaper ($0.88 vs $25.00 per million) and a lower cost per solved task ($0.99 against $2.95).
- Shared results
- 57
- Claude Opus 4.7 only
- 46
- DeepSeek V3.2 only
- 18
- 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
Claude Opus 4.7
Claude Opus 4.7 scores 67.5 against 30.0.
4 and 3 benchmarks behind the scores
Intelligence
AA Intelligence Index
Claude Opus 4.7
Claude Opus 4.7 leads, 40.7 against 21.5.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Claude Opus 4.7 has a AA Agentic Index result, 38.6.
Directional only
Reasoning
GPQA Diamond
Claude Opus 4.7
Claude Opus 4.7 leads, 90.2% against 80.3%.
Same board, both measured
Math
AIME
Claude Opus 4.7
Claude Opus 4.7 leads, 96.3% against 84.6%.
Same board, both measured
Knowledge
MMLU Pro
Claude Opus 4.7
Claude Opus 4.7 leads, 89.9% against 84.9%.
Same board, both measured
Web design
Design Arena: websites
No call
Only DeepSeek V3.2 has a Design Arena: websites result, 1175.
Directional only
Long documents
Listed context window
Claude Opus 4.7
Claude Opus 4.7 takes 1M tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V3.2
Claude Opus 4.7 $0.018, DeepSeek V3.2 $0.0007 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
DeepSeek V3.2
Claude Opus 4.7 $2.95, DeepSeek V3.2 $0.99.
Published attempt cost and pass rate
At a glance
- Coding index
- Claude Opus 4.7 67.5 from 4 benchmarks, DeepSeek V3.2 30.0 from 3 benchmarks.
- Widest category
- Agentic work, 30.6 points apart on average over 3 shared benchmarks.
- Widest single gap
- ARC-AGI-2: Claude Opus 4.7 75.8% against DeepSeek V3.2 4.0%, 71.8 points apart on a 0 to 100 scale.
- Output price
- Claude Opus 4.7 $25.00 against DeepSeek V3.2 $0.88 per million output tokens, 28.4x apart.
- Context window
- Claude Opus 4.7 takes 1M tokens, DeepSeek V3.2 164K.
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 | DeepSeek V3.2 | Ties |
|---|---|---|---|---|
| Coding | 11 | 0 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 2 | 1 | ||
| Reasoning | 8 | 0 | ||
| Math | 3 | 0 | ||
| Knowledge | 9 | 0 | ||
| General | 20 | 1 | ||
| All shared boards | 55 | 2 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index110
| Benchmark | Claude Opus 4.7 | DeepSeek V3.2 | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 70.0% | Only DeepSeek V3.2 measured |
| SWE-bench Multilingual | Not measured | 59.0% | Only DeepSeek V3.2 measured |
| LMArena WebDev | 1558 | 1362 | Claude Opus 4.7 |
| AA Coding Index | 73.6 | 44.2 | Claude Opus 4.7 |
| LiveCodeBench (Vals AI) | 85.1% | 80.7% | Claude Opus 4.7 |
| SWE-bench Verified (Vals AI) | 82.0% | 67.6% | Claude Opus 4.7 |
| Vibe Code Bench (Vals AI) | 71.0% | 5.1% | Claude Opus 4.7 |
| Code migration (Vals AI) | 43.9% | Not measured | Only Claude Opus 4.7 measured |
| ProgramBench (Vals AI) | 0.0% | Not measured | Only Claude Opus 4.7 measured |
| Terminal-Bench 2.1 (Vals AI) | 68.5% | Not measured | Only Claude Opus 4.7 measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 86.2% | Only DeepSeek V3.2 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 54.5% | 35.6% | Claude Opus 4.7 |
| Terminal-Bench | 80.2% | 39.6% | Claude Opus 4.7 |
| FrontierCode | 38.5% | Not measured | Only Claude Opus 4.7 measured |
| GSO-Bench | 44.1% | Not measured | Only Claude Opus 4.7 measured |
| WeirdML | 76.4% | Not measured | Only Claude Opus 4.7 measured |
| MirrorCode | 31.1% | Not measured | Only Claude Opus 4.7 measured |
| LMArena Text: coding | 1519 | 1453 | Claude Opus 4.7 |
| LMArena Text: software and IT | 1516 | 1449 | Claude Opus 4.7 |
| LMArena WebDev: React | 1560 | 1350 | Claude Opus 4.7 |
| LMArena WebDev: HTML | 1554 | 1371 | Claude Opus 4.7 |
| LMArena WebDev: from an image | 1577 | Not measured | Only Claude Opus 4.7 measured |
| Neither model has a result on the other 17 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Claude Opus 4.7 | DeepSeek V3.2 | Leader |
|---|---|---|---|
| AA Intelligence Index | 40.7 | 21.5 | Claude Opus 4.7 |
| Epoch Capabilities Index | 156.8 | 146.1 | Claude Opus 4.7 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index21
| Benchmark | Claude Opus 4.7 | DeepSeek V3.2 | Leader |
|---|---|---|---|
| AA Agentic Index | 38.6 | Not measured | Only Claude Opus 4.7 measured |
| Finance Agent (Vals AI) | 51.5% | Not measured | Only Claude Opus 4.7 measured |
| Tax Agent Bench (Vals AI) | 23.7% | Not measured | Only Claude Opus 4.7 measured |
| Legal Research Bench (Vals AI) | 38.5% | Not measured | Only Claude Opus 4.7 measured |
| Excel Modeling Benchmark (Vals AI) | 63.7% | Not measured | Only Claude Opus 4.7 measured |
| τ²-Bench Telecom | 88.6% | 90.6% | DeepSeek V3.2 |
| APEX-Agents | 49.2% | 21.3% | Claude Opus 4.7 |
| OSWorld 2.0 | 18.2% | Not measured | Only Claude Opus 4.7 measured |
| PostTrainBench | 28.6% | Not measured | Only Claude Opus 4.7 measured |
| Vending-Bench 2 | $10,937 | $1,034 | Claude Opus 4.7 |
| LMArena Search | 1230 | Not measured | Only Claude Opus 4.7 measured |
| Neither model has a result on the other 30 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index80
| Benchmark | Claude Opus 4.7 | DeepSeek V3.2 | Leader |
|---|---|---|---|
| GPQA Diamond | 90.2% | 80.3% | Claude Opus 4.7 |
| MMMU Pro (Vals AI) | 85.5% | Not measured | Only Claude Opus 4.7 measured |
| GPQA Diamond (Artificial Analysis run) | 91.4% | 84.0% | Claude Opus 4.7 |
| Humanity's Last Exam (Artificial Analysis run) | 42.3% | 24.6% | Claude Opus 4.7 |
| GPQA Diamond (Epoch AI run) | 90.2% | Not measured | Only Claude Opus 4.7 measured |
| ARC-AGI | 93.5% | 57.0% | Claude Opus 4.7 |
| ARC-AGI-2 | 75.8% | 4.0% | Claude Opus 4.7 |
| SimpleBench | 61.7% | Not measured | Only Claude Opus 4.7 measured |
| Chess puzzles | 41.0% | Not measured | Only Claude Opus 4.7 measured |
| Mystery game puzzles | 28.0% | Not measured | Only Claude Opus 4.7 measured |
| EBR-bench | 19.0% | Not measured | Only Claude Opus 4.7 measured |
| LMArena Text: hard prompts | 1506 | 1434 | Claude Opus 4.7 |
| LMArena Text: expert | 1521 | 1436 | Claude Opus 4.7 |
| LMArena Text: hard prompts in English | 1508 | 1446 | Claude Opus 4.7 |
| LMArena Vision: diagrams | 1336 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Vision: homework | 1337 | Not measured | Only Claude Opus 4.7 measured |
| Kagi LLM Benchmark | 80.7% | Not measured | Only Claude Opus 4.7 measured |
| Neither model has a result on the other 5 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | Claude Opus 4.7 | DeepSeek V3.2 | Leader |
|---|---|---|---|
| AIME | 96.3% | 84.6% | Claude Opus 4.7 |
| MGSM (Vals AI) | Not measured | 86.0% | Only DeepSeek V3.2 measured |
| AA Math Index | Not measured | 92.0 | Only DeepSeek V3.2 measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 92.0% | Only DeepSeek V3.2 measured |
| OTIS Mock AIME | 97.8% | Not measured | Only Claude Opus 4.7 measured |
| FrontierMath, tiers 1 to 3 | 70.2% | Not measured | Only Claude Opus 4.7 measured |
| FrontierMath, tier 4 | 31.7% | Not measured | Only Claude Opus 4.7 measured |
| ProofBench | 54.0% | Not measured | Only Claude Opus 4.7 measured |
| LMArena Text: math | 1498 | 1435 | Claude Opus 4.7 |
| LMArena Text: mathematical work | 1509 | 1440 | Claude Opus 4.7 |
| Neither model has a result on the other 3 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index90
| Benchmark | Claude Opus 4.7 | DeepSeek V3.2 | Leader |
|---|---|---|---|
| MMLU Pro | 89.9% | 84.9% | Claude Opus 4.7 |
| LegalBench (Vals AI) | 85.3% | 76.1% | Claude Opus 4.7 |
| CaseLaw (Vals AI) | 68.4% | 55.4% | Claude Opus 4.7 |
| CorpFin (Vals AI) | 66.1% | 51.0% | Claude Opus 4.7 |
| TaxEval (Vals AI) | 75.3% | 68.2% | Claude Opus 4.7 |
| MortgageTax (Vals AI) | 70.3% | Not measured | Only Claude Opus 4.7 measured |
| MedQA (Vals AI) | Not measured | 93.9% | Only DeepSeek V3.2 measured |
| MedCode (Vals AI) | 54.9% | Not measured | Only Claude Opus 4.7 measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 86.2% | Only DeepSeek V3.2 measured |
| SimpleQA Verified | 51.7% | Not measured | Only Claude Opus 4.7 measured |
| LMArena Text: science | 1508 | 1446 | Claude Opus 4.7 |
| LMArena Text: medicine and healthcare | 1495 | 1440 | Claude Opus 4.7 |
| LMArena Text: legal and government | 1504 | 1425 | Claude Opus 4.7 |
| LMArena Text: business and finance | 1490 | 1410 | Claude Opus 4.7 |
| FACTS Benchmark Suite | 45.5% | Not measured | Only Claude Opus 4.7 measured |
| Neither model has a result on the other 3 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | Claude Opus 4.7 | DeepSeek V3.2 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1175 | Only DeepSeek V3.2 measured |
| Design Arena: UI components | Not measured | 1149 | Only DeepSeek V3.2 measured |
| Design Arena: game dev | Not measured | 1132 | Only DeepSeek V3.2 measured |
| Design Arena: data viz | Not measured | 1158 | Only DeepSeek V3.2 measured |
| Design Arena: 3D | Not measured | 1133 | Only DeepSeek V3.2 measured |
| Design Arena: all code categories | Not measured | 1164 | Only DeepSeek V3.2 measured |
| Design Arena: SVG | Not measured | 1032 | Only DeepSeek V3.2 measured |
| Design Arena: ASCII art | Not measured | 1083 | Only DeepSeek V3.2 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index201
| Benchmark | Claude Opus 4.7 | DeepSeek V3.2 | Leader |
|---|---|---|---|
| SAGE (Vals AI) | 56.1% | Not measured | Only Claude Opus 4.7 measured |
| MedScribe (Vals AI) | 83.0% | Not measured | Only Claude Opus 4.7 measured |
| Vals Multimodal Index | 67.4% | Not measured | Only Claude Opus 4.7 measured |
| IFBench | 58.6% | 60.7% | DeepSeek V3.2 |
| Long context reasoning (AA-LCR) | 78.7% | 73.3% | Claude Opus 4.7 |
| DTBench | 94.7% | 85.6% | Claude Opus 4.7 |
| LMCA | 52.2% | 28.8% | Claude Opus 4.7 |
| CL-bench | Not measured | 12.4% | Only DeepSeek V3.2 measured |
| Furniture assembly | 33.3% | Not measured | Only Claude Opus 4.7 measured |
| ExploitBench | 26.5% | Not measured | Only Claude Opus 4.7 measured |
| CL-bench Life | Not measured | 7.4% | Only DeepSeek V3.2 measured |
| LMArena Text | 1490 | 1425 | Claude Opus 4.7 |
| LMArena Text: instruction following | 1498 | 1413 | Claude Opus 4.7 |
| LMArena Text: creative writing | 1486 | 1400 | Claude Opus 4.7 |
| LMArena Vision | 1317 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Text: English | 1496 | 1439 | Claude Opus 4.7 |
| LMArena Text: non-English | 1480 | 1409 | Claude Opus 4.7 |
| LMArena Text: Chinese | 1531 | 1455 | Claude Opus 4.7 |
| LMArena Text: German | 1495 | 1413 | Claude Opus 4.7 |
| LMArena Text: Japanese | 1474 | 1374 | Claude Opus 4.7 |
| LMArena Text: Korean | 1464 | 1370 | Claude Opus 4.7 |
| LMArena Text: French | 1503 | 1429 | Claude Opus 4.7 |
| LMArena Text: Spanish | 1494 | 1424 | Claude Opus 4.7 |
| LMArena Text: Russian | 1494 | 1424 | Claude Opus 4.7 |
| LMArena Text: Polish | 1489 | 1419 | Claude Opus 4.7 |
| LMArena Text: multi-turn | 1505 | 1427 | Claude Opus 4.7 |
| LMArena Text: longer queries | 1505 | 1428 | Claude Opus 4.7 |
| LMArena Text: writing and language | 1492 | 1407 | Claude Opus 4.7 |
| LMArena Text: entertainment and media | 1474 | 1395 | Claude Opus 4.7 |
| LMArena Vision: English | 1322 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Vision: Chinese | 1371 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Vision: reading text | 1327 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Vision: humor | 1309 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Vision: recognizing things | 1278 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Vision: creative writing | 1326 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Document | 1495 | Not measured | Only Claude Opus 4.7 measured |
| Neither model has a result on the other 10 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Claude Opus 4.7 | DeepSeek V3.2 | 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
DeepSeek V3.2 costs less
Repository review
50K input + 3K output tokens
DeepSeek V3.2 costs less
Agent loop
200K input + 10K output tokens
DeepSeek V3.2 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 | DeepSeek V3.2 |
|---|---|---|
| Maker | Anthropic | DeepSeek |
| Weights | No public weights listed | Open weights |
| Released | April 16, 2026 | December 1, 2025 |
| Context window | 1M | 164K |
| Input $/Mtok | $5.00 | $0.29 |
| Output $/Mtok | $25.00 | $0.88 |
| Coding index | 67.5 | 30.0 |
| Independent measurements behind it | 4 | 3 |
| Cost per solved task | $2.95 | $0.99 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Claude Opus 4.7 better than DeepSeek V3.2 for coding?
Pick Claude Opus 4.7 for coding: it scores 67.5 on the Vibe Coding Index against 30.0 for DeepSeek V3.2. Pick DeepSeek V3.2 for output tokens 28.4x cheaper ($0.88 vs $25.00 per million) and a lower cost per solved task ($0.99 against $2.95).
Which is cheaper, Claude Opus 4.7 or DeepSeek V3.2?
Claude Opus 4.7 costs $5.00 per million input tokens and $25.00 per million output tokens; DeepSeek V3.2 costs $0.29 and $0.88. Per solved SWE-bench Verified task, Claude Opus 4.7 costs $2.95 and DeepSeek V3.2 $0.99.
Which has the bigger context window, Claude Opus 4.7 or DeepSeek V3.2?
Claude Opus 4.7, with 1M tokens against 164K.
How many benchmarks is the Claude Opus 4.7 vs DeepSeek V3.2 comparison based on?
57 benchmarks have a result for both models as of October 21, 2026. Claude Opus 4.7's coding index rests on 4 independent benchmarks and DeepSeek V3.2's on 3. Where only one model has a result, the page says so instead of naming a winner.