Nova Premier 1.0 vs DeepSeek V3.1
DeepSeek V3.1 leads Nova Premier 1.0 on the Artificial Analysis Intelligence Index, 13.7 to 9.2, and DeepSeek V3.1 costs 8.3x less per output token ($1.50 vs $12.50 per million). Updated October 21, 2026; 2 benchmarks have a result for both.
Nova Premier 1.0 or DeepSeek V3.1: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Nova Premier 1.0 for a larger context window (1M against 164K tokens) and work measured by AA Intelligence Index or Design Arena: websites, where it leads.
- Shared results
- 2
- Nova Premier 1.0 only
- 11
- DeepSeek V3.1 only
- 8
- Like-for-like categories
- 2 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
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
DeepSeek V3.1
DeepSeek V3.1 leads, 13.7 against 9.2.
Same board, both measured
Web design
Design Arena: websites
DeepSeek V3.1
DeepSeek V3.1 leads, 1123 against 835.
Same board, both measured
Long documents
Listed context window
Nova Premier 1.0
Nova Premier 1.0 takes 1M tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V3.1
Nova Premier 1.0 $0.0088, DeepSeek V3.1 $0.0013 per turn.
List prices
At a glance
- Coding index
- Nova Premier 1.0 not benchmarked, DeepSeek V3.1 not benchmarked.
- Widest category
- Web design, 36.0 points apart on average over 1 shared benchmark.
- Widest single gap
- Design Arena: websites: Nova Premier 1.0 835 against DeepSeek V3.1 1123, 36.0 points apart on a 0 to 100 scale.
- Output price
- Nova Premier 1.0 $12.50 against DeepSeek V3.1 $1.50 per million output tokens, 8.3x apart.
- Context window
- Nova Premier 1.0 takes 1M tokens, DeepSeek V3.1 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 Premier 1.0 | Bars | DeepSeek V3.1 | Ties |
|---|---|---|---|---|
| Intelligence | 0 | 1 | ||
| Web design | 0 | 1 | ||
| All shared boards | 0 | 2 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index3 boards, none shared
| Benchmark | Nova Premier 1.0 | DeepSeek V3.1 | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | 31.7% | Not measured | Only Nova Premier 1.0 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 6.8% | Not measured | Only Nova Premier 1.0 measured |
| ALE-Bench | 147.4 | Not measured | Only Nova Premier 1.0 measured |
| Neither model has a result on the other 35 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Nova Premier 1.0 | DeepSeek V3.1 | Leader |
|---|---|---|---|
| AA Intelligence Index | 9.2 | 13.7 | DeepSeek V3.1 |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Nova Premier 1.0 | DeepSeek V3.1 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 38.3% | Not measured | Only Nova Premier 1.0 measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Nova Premier 1.0 | DeepSeek V3.1 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 56.9% | Not measured | Only Nova Premier 1.0 measured |
| Humanity's Last Exam (Artificial Analysis run) | 4.2% | Not measured | Only Nova Premier 1.0 measured |
| Neither model has a result on the other 19 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Nova Premier 1.0 | DeepSeek V3.1 | Leader |
|---|---|---|---|
| AA Math Index | 17.3 | Not measured | Only Nova Premier 1.0 measured |
| AIME 2025 (Artificial Analysis run) | 17.3% | Not measured | Only Nova Premier 1.0 measured |
| Neither model has a result on the other 11 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Nova Premier 1.0 | DeepSeek V3.1 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 73.3% | Not measured | Only Nova Premier 1.0 measured |
| Neither model has a result on the other 16 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Nova Premier 1.0 | DeepSeek V3.1 | Leader |
|---|---|---|---|
| Design Arena: websites | 835 | 1123 | DeepSeek V3.1 |
| Design Arena: UI components | Not measured | 1085 | Only DeepSeek V3.1 measured |
| Design Arena: game dev | Not measured | 1084 | Only DeepSeek V3.1 measured |
| Design Arena: data viz | Not measured | 1095 | Only DeepSeek V3.1 measured |
| Design Arena: 3D | Not measured | 1077 | Only DeepSeek V3.1 measured |
| Design Arena: all code categories | Not measured | 1112 | Only DeepSeek V3.1 measured |
| Design Arena: SVG | Not measured | 967 | Only DeepSeek V3.1 measured |
| Neither model has a result on the other 2 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | Nova Premier 1.0 | DeepSeek V3.1 | Leader |
|---|---|---|---|
| IFBench | 36.2% | Not measured | Only Nova Premier 1.0 measured |
| Long context reasoning (AA-LCR) | 34.3% | Not measured | Only Nova Premier 1.0 measured |
| DTBench | Not measured | 82.7% | Only DeepSeek V3.1 measured |
| LMCA | Not measured | 24.3% | Only DeepSeek V3.1 measured |
| Neither model has a result on the other 42 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Nova Premier 1.0 | DeepSeek V3.1 | 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 V3.1 costs less
Repository review
50K input + 3K output tokens
DeepSeek V3.1 costs less
Agent loop
200K input + 10K output tokens
DeepSeek V3.1 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 Premier 1.0 | DeepSeek V3.1 |
|---|---|---|
| Maker | Amazon | DeepSeek |
| Weights | No public weights listed | Open weights |
| Released | October 31, 2025 | August 21, 2025 |
| Context window | 1M | 164K |
| Input $/Mtok | $2.50 | $0.55 |
| Output $/Mtok | $12.50 | $1.50 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Nova Premier 1.0 better than DeepSeek V3.1 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Nova Premier 1.0 for a larger context window (1M against 164K tokens) and work measured by AA Intelligence Index or Design Arena: websites, where it leads.
Which is cheaper, Nova Premier 1.0 or DeepSeek V3.1?
Nova Premier 1.0 costs $2.50 per million input tokens and $12.50 per million output tokens; DeepSeek V3.1 costs $0.55 and $1.50. Cost per solved task is known for neither of them.
Which has the bigger context window, Nova Premier 1.0 or DeepSeek V3.1?
Nova Premier 1.0, with 1M tokens against 164K.
How many benchmarks is the Nova Premier 1.0 vs DeepSeek V3.1 comparison based on?
2 benchmarks have a result for both models as of October 21, 2026. Nova Premier 1.0's coding index rests on 0 independent benchmarks and DeepSeek V3.1's on 0. Where only one model has a result, the page says so instead of naming a winner.