Nova Premier 1.0 vs GPT-4o
Updated October 21, 2026. 11 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 Premier 1.0 has the edge where the context window is larger, 1M against 128K tokens, and where what matters is Terminal-Bench Hard (Artificial Analysis run) or MMLU Pro (Artificial Analysis run), where it leads.
- Shared results
- 11
- Nova Premier 1.0 only
- 2
- GPT-4o only
- 15
- 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, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
No call
Only Nova Premier 1.0 has a AA Intelligence Index result, 9.2.
Directional only
Web design
Design Arena: websites
Nova Premier 1.0
Nova Premier 1.0 leads, 835 against 831.
Same board, both measured
Long documents
Listed context window
Nova Premier 1.0
Nova Premier 1.0 takes 1M tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-4o
Nova Premier 1.0 $0.0088, GPT-4o $0.0075 per turn.
List prices
At a glance
- Coding index
- Nova Premier 1.0 not benchmarked, GPT-4o not benchmarked.
- Widest category
- Agentic work, 13.2 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): Nova Premier 1.0 34.3% against GPT-4o 49.3%, 15.0 points apart on a 0 to 100 scale.
- Output price
- Nova Premier 1.0 $12.50 against GPT-4o $10.00 per million output tokens, 1.3x apart.
- Context window
- Nova Premier 1.0 takes 1M tokens, GPT-4o 128K.
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 | GPT-4o | Ties |
|---|---|---|---|---|
| Coding | 1 | 1 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 0 | 1 | ||
| Web design | 1 | 0 | ||
| General | 1 | 1 | ||
| All shared boards | 8 | 3 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index11
| Benchmark | Nova Premier 1.0 | GPT-4o | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 38.8% | Only GPT-4o measured |
| SWE-bench Lite | Not measured | 38.0% | Only GPT-4o measured |
| SWE-bench (full test set) | Not measured | 18.8% | Only GPT-4o measured |
| SWE-bench Multimodal | Not measured | 30.4% | Only GPT-4o measured |
| LiveCodeBench (Artificial Analysis run) | 31.7% | 30.9% | Nova Premier 1.0 |
| Terminal-Bench Hard (Artificial Analysis run) | 6.8% | 8.3% | GPT-4o |
| ALE-Bench | 147.4 | Not measured | Only Nova Premier 1.0 measured |
| Neither model has a result on the other 31 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Nova Premier 1.0 | GPT-4o | Leader |
|---|---|---|---|
| AA Intelligence Index | 9.2 | Not measured | Only Nova Premier 1.0 measured |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Nova Premier 1.0 | GPT-4o | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 38.3% | 25.1% | Nova Premier 1.0 |
| GDPval | Not measured | 9.9% | Only GPT-4o measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Nova Premier 1.0 | GPT-4o | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 56.9% | 54.3% | Nova Premier 1.0 |
| Humanity's Last Exam (Artificial Analysis run) | 4.2% | 2.4% | Nova Premier 1.0 |
| ARC-AGI | Not measured | 4.5% | Only GPT-4o measured |
| ARC-AGI-2 | Not measured | 0.0% | Only GPT-4o measured |
| Neither model has a result on the other 17 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Nova Premier 1.0 | GPT-4o | Leader |
|---|---|---|---|
| AA Math Index | 17.3 | 6.0 | Nova Premier 1.0 |
| AIME 2025 (Artificial Analysis run) | 17.3% | 6.0% | Nova Premier 1.0 |
| Neither model has a result on the other 11 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Nova Premier 1.0 | GPT-4o | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 73.3% | 74.8% | GPT-4o |
| MMLU | Not measured | 88.1% | Only GPT-4o measured |
| Neither model has a result on the other 15 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Nova Premier 1.0 | GPT-4o | Leader |
|---|---|---|---|
| Design Arena: websites | 835 | 831 | Nova Premier 1.0 |
| Design Arena: UI components | Not measured | 885 | Only GPT-4o measured |
| Design Arena: game dev | Not measured | 906 | Only GPT-4o measured |
| Design Arena: data viz | Not measured | 858 | Only GPT-4o measured |
| Design Arena: 3D | Not measured | 869 | Only GPT-4o measured |
| Design Arena: all code categories | Not measured | 861 | Only GPT-4o measured |
| Neither model has a result on the other 3 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | Nova Premier 1.0 | GPT-4o | Leader |
|---|---|---|---|
| IFBench | 36.2% | 34.3% | Nova Premier 1.0 |
| Long context reasoning (AA-LCR) | 34.3% | 49.3% | GPT-4o |
| DTBench | Not measured | 64.5% | Only GPT-4o measured |
| LMCA | Not measured | 16.6% | Only GPT-4o 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 | GPT-4o | 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-4o costs less
Repository review
50K input + 3K output tokens
GPT-4o costs less
Agent loop
200K input + 10K output tokens
GPT-4o 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 | OpenAI GPT-4o |
|---|---|---|
| Maker | Amazon | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | October 31, 2025 | May 13, 2024 |
| Context window | 1M | 128K |
| Input $/Mtok | $2.50 | $2.50 |
| Output $/Mtok | $12.50 | $10.00 |
| 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 GPT-4o for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Nova Premier 1.0 has the edge where the context window is larger, 1M against 128K tokens, and where what matters is Terminal-Bench Hard (Artificial Analysis run) or MMLU Pro (Artificial Analysis run), where it leads.
Which is cheaper, Nova Premier 1.0 or GPT-4o?
Nova Premier 1.0 costs $2.50 per million input tokens and $12.50 per million output tokens; GPT-4o costs $2.50 and $10.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Nova Premier 1.0 or GPT-4o?
Nova Premier 1.0, with 1M tokens against 128K.
How many benchmarks is the Nova Premier 1.0 vs GPT-4o comparison based on?
11 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 GPT-4o's on 0. Where only one model has a result, the page says so instead of naming a winner.