Nova Pro 1.0 vs DeepSeek V3.2 Exp
Updated October 21, 2026. 39 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Neither model has a coding score yet, so the two cannot be ranked on coding.
Nova Pro 1.0 has the edge where the context window is larger, 300K against 164K tokens, and where what matters is LiveCodeBench (Artificial Analysis run) or Terminal-Bench Hard (Artificial Analysis run), where it leads.
- Shared results
- 39
- Nova Pro 1.0 only
- 3
- DeepSeek V3.2 Exp only
- 18
- 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, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
DeepSeek V3.2 Exp
DeepSeek V3.2 Exp leads, 16.6 against 7.0.
Same board, both measured
Web design
Design Arena: websites
DeepSeek V3.2 Exp
DeepSeek V3.2 Exp leads, 1179 against 796.
Same board, both measured
Long documents
Listed context window
Nova Pro 1.0
Nova Pro 1.0 takes 300K tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V3.2 Exp
Nova Pro 1.0 $0.0024, DeepSeek V3.2 Exp $0.0005 per turn.
List prices
At a glance
- Coding index
- Nova Pro 1.0 not benchmarked, DeepSeek V3.2 Exp not benchmarked.
- Widest category
- Math, 54.6 points apart on average over 4 shared benchmarks.
- Widest single gap
- AA Math Index: Nova Pro 1.0 7.0 against DeepSeek V3.2 Exp 87.7, 80.7 points apart on a 0 to 100 scale.
- Output price
- Nova Pro 1.0 $3.20 against DeepSeek V3.2 Exp $0.41 per million output tokens, 7.8x apart.
- Context window
- Nova Pro 1.0 takes 300K tokens, DeepSeek V3.2 Exp 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 | Nova Pro 1.0 | Bars | DeepSeek V3.2 Exp | Ties |
|---|---|---|---|---|
| Coding | 0 | 4 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 5 | ||
| Math | 0 | 4 | ||
| Knowledge | 0 | 5 | ||
| Web design | 0 | 1 | ||
| General | 0 | 18 | ||
| All shared boards | 0 | 39 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index04
| Benchmark | Nova Pro 1.0 | DeepSeek V3.2 Exp | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 74.2% | Only DeepSeek V3.2 Exp measured |
| LMArena WebDev | Not measured | 1272 | Only DeepSeek V3.2 Exp measured |
| LiveCodeBench (Artificial Analysis run) | 23.3% | 78.9% | DeepSeek V3.2 Exp |
| Terminal-Bench Hard (Artificial Analysis run) | 6.1% | 31.1% | DeepSeek V3.2 Exp |
| WeirdML | Not measured | 39.5% | Only DeepSeek V3.2 Exp measured |
| LMArena Text: coding | 1270 | 1438 | DeepSeek V3.2 Exp |
| LMArena Text: software and IT | 1271 | 1438 | DeepSeek V3.2 Exp |
| LMArena WebDev: HTML | Not measured | 1281 | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 30 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Nova Pro 1.0 | DeepSeek V3.2 Exp | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.0 | 16.6 | DeepSeek V3.2 Exp |
| Epoch Capabilities Index | Not measured | 144.9 | Only DeepSeek V3.2 Exp measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Nova Pro 1.0 | DeepSeek V3.2 Exp | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 14.0% | 33.9% | DeepSeek V3.2 Exp |
| TheAgentCompany | Not measured | 42.9% | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | Nova Pro 1.0 | DeepSeek V3.2 Exp | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 49.9% | 79.7% | DeepSeek V3.2 Exp |
| Humanity's Last Exam (Artificial Analysis run) | 3.2% | 14.9% | DeepSeek V3.2 Exp |
| LMArena Text: hard prompts | 1246 | 1429 | DeepSeek V3.2 Exp |
| LMArena Text: expert | 1211 | 1422 | DeepSeek V3.2 Exp |
| LMArena Text: hard prompts in English | 1259 | 1444 | DeepSeek V3.2 Exp |
| Neither model has a result on the other 16 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | Nova Pro 1.0 | DeepSeek V3.2 Exp | Leader |
|---|---|---|---|
| AA Math Index | 7.0 | 87.7 | DeepSeek V3.2 Exp |
| AIME 2025 (Artificial Analysis run) | 7.0% | 87.7% | DeepSeek V3.2 Exp |
| LMArena Text: math | 1252 | 1422 | DeepSeek V3.2 Exp |
| LMArena Text: mathematical work | 1261 | 1433 | DeepSeek V3.2 Exp |
| Neither model has a result on the other 9 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | Nova Pro 1.0 | DeepSeek V3.2 Exp | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 69.1% | 85.0% | DeepSeek V3.2 Exp |
| MMLU | 82.0% | Not measured | Only Nova Pro 1.0 measured |
| LMArena Text: science | 1255 | 1443 | DeepSeek V3.2 Exp |
| LMArena Text: medicine and healthcare | 1260 | 1468 | DeepSeek V3.2 Exp |
| LMArena Text: legal and government | 1281 | 1419 | DeepSeek V3.2 Exp |
| LMArena Text: business and finance | 1229 | 1412 | DeepSeek V3.2 Exp |
| Neither model has a result on the other 11 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Nova Pro 1.0 | DeepSeek V3.2 Exp | Leader |
|---|---|---|---|
| Design Arena: websites | 796 | 1179 | DeepSeek V3.2 Exp |
| Design Arena: UI components | Not measured | 1167 | Only DeepSeek V3.2 Exp measured |
| Design Arena: game dev | Not measured | 1144 | Only DeepSeek V3.2 Exp measured |
| Design Arena: data viz | Not measured | 1152 | Only DeepSeek V3.2 Exp measured |
| Design Arena: 3D | Not measured | 1148 | Only DeepSeek V3.2 Exp measured |
| Design Arena: all code categories | Not measured | 1171 | Only DeepSeek V3.2 Exp measured |
| Design Arena: SVG | Not measured | 1032 | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index018
| Benchmark | Nova Pro 1.0 | DeepSeek V3.2 Exp | Leader |
|---|---|---|---|
| IFBench | 38.1% | 54.1% | DeepSeek V3.2 Exp |
| Long context reasoning (AA-LCR) | 21.0% | 72.3% | DeepSeek V3.2 Exp |
| DTBench | Not measured | 87.7% | Only DeepSeek V3.2 Exp measured |
| LMCA | Not measured | 29.1% | Only DeepSeek V3.2 Exp measured |
| Fiction.LiveBench | Not measured | 83.3% | Only DeepSeek V3.2 Exp measured |
| CL-bench | Not measured | 13.2% | Only DeepSeek V3.2 Exp measured |
| CL-bench Life | Not measured | 9.5% | Only DeepSeek V3.2 Exp measured |
| LMArena Text | 1259 | 1425 | DeepSeek V3.2 Exp |
| LMArena Text: instruction following | 1235 | 1401 | DeepSeek V3.2 Exp |
| LMArena Text: creative writing | 1212 | 1404 | DeepSeek V3.2 Exp |
| LMArena Vision | 980 | Not measured | Only Nova Pro 1.0 measured |
| LMArena Text: English | 1276 | 1441 | DeepSeek V3.2 Exp |
| LMArena Text: non-English | 1233 | 1404 | DeepSeek V3.2 Exp |
| LMArena Text: Chinese | 1244 | 1461 | DeepSeek V3.2 Exp |
| LMArena Text: German | 1243 | 1440 | DeepSeek V3.2 Exp |
| LMArena Text: Japanese | 1200 | 1331 | DeepSeek V3.2 Exp |
| LMArena Text: Korean | 1203 | 1372 | DeepSeek V3.2 Exp |
| LMArena Text: French | 1271 | 1434 | DeepSeek V3.2 Exp |
| LMArena Text: Spanish | 1182 | 1441 | DeepSeek V3.2 Exp |
| LMArena Text: Russian | 1240 | 1412 | DeepSeek V3.2 Exp |
| LMArena Text: Polish | Not measured | 1435 | Only DeepSeek V3.2 Exp measured |
| LMArena Text: multi-turn | 1246 | 1422 | DeepSeek V3.2 Exp |
| LMArena Text: longer queries | 1255 | 1418 | DeepSeek V3.2 Exp |
| LMArena Text: writing and language | 1247 | 1407 | DeepSeek V3.2 Exp |
| LMArena Text: entertainment and media | 1210 | 1407 | DeepSeek V3.2 Exp |
| LMArena Vision: English | 987 | Not measured | Only Nova Pro 1.0 measured |
| Neither model has a result on the other 20 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Nova Pro 1.0 | DeepSeek V3.2 Exp | 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 Exp costs less
Repository review
50K input + 3K output tokens
DeepSeek V3.2 Exp costs less
Agent loop
200K input + 10K output tokens
DeepSeek V3.2 Exp 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 | Amazon Nova Pro 1.0 | DeepSeek V3.2 Exp |
|---|---|---|
| Maker | Amazon | DeepSeek |
| Weights | No public weights listed | Open weights |
| Released | December 5, 2024 | September 29, 2025 |
| Context window | 300K | 164K |
| Input $/Mtok | $0.80 | $0.27 |
| Output $/Mtok | $3.20 | $0.41 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Nova Pro 1.0 better than DeepSeek V3.2 Exp for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Nova Pro 1.0 has the edge where the context window is larger, 300K against 164K tokens, and where what matters is LiveCodeBench (Artificial Analysis run) or Terminal-Bench Hard (Artificial Analysis run), where it leads.
Which is cheaper, Nova Pro 1.0 or DeepSeek V3.2 Exp?
Nova Pro 1.0 costs $0.80 per million input tokens and $3.20 per million output tokens; DeepSeek V3.2 Exp costs $0.27 and $0.41. Cost per solved task is known for neither of them.
Which has the bigger context window, Nova Pro 1.0 or DeepSeek V3.2 Exp?
Nova Pro 1.0, with 300K tokens against 164K.
How many benchmarks is the Nova Pro 1.0 vs DeepSeek V3.2 Exp comparison based on?
39 benchmarks have a result for both models as of October 21, 2026. Nova Pro 1.0's coding index rests on 0 independent benchmarks and DeepSeek V3.2 Exp's on 1. Where only one model has a result, the page says so instead of naming a winner.