Nova Pro 1.0 vs GPT-5.3-Codex
Updated October 21, 2026. 8 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Only GPT-5.3-Codex has a coding score, 60.0 on the Vibe Coding Index; Nova Pro 1.0 has none, so the two cannot be ranked on coding.
Nova Pro 1.0 has the edge where output tokens cost 4.4x less.
- Shared results
- 8
- Nova Pro 1.0 only
- 34
- GPT-5.3-Codex only
- 26
- Like-for-like categories
- 6 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
Only GPT-5.3-Codex has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GPT-5.3-Codex
GPT-5.3-Codex leads, 32.5 against 7.0.
Same board, both measured
Web design
Design Arena: websites
GPT-5.3-Codex
GPT-5.3-Codex leads, 1163 against 796.
Same board, both measured
Long documents
Listed context window
GPT-5.3-Codex
GPT-5.3-Codex takes 400K tokens against 300K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Nova Pro 1.0
Nova Pro 1.0 $0.0024, GPT-5.3-Codex $0.0088 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.3-Codex has the number, $0.59.
Directional only
At a glance
- Coding index
- Nova Pro 1.0 not benchmarked, GPT-5.3-Codex 60.0 from 3 benchmarks.
- Widest category
- Agentic work, 71.9 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: Nova Pro 1.0 14.0% against GPT-5.3-Codex 86.0%, 71.9 points apart on a 0 to 100 scale.
- Output price
- Nova Pro 1.0 $3.20 against GPT-5.3-Codex $14.00 per million output tokens, 4.4x apart.
- Context window
- Nova Pro 1.0 takes 300K tokens, GPT-5.3-Codex 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 | Nova Pro 1.0 | Bars | GPT-5.3-Codex | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 2 | ||
| Web design | 0 | 1 | ||
| General | 0 | 2 | ||
| All shared boards | 0 | 8 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
| Benchmark | Nova Pro 1.0 | GPT-5.3-Codex | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1407 | Only GPT-5.3-Codex measured |
| LiveCodeBench (Vals AI) | Not measured | 87.3% | Only GPT-5.3-Codex measured |
| SWE-bench Verified (Vals AI) | Not measured | 78.0% | Only GPT-5.3-Codex measured |
| Vibe Code Bench (Vals AI) | Not measured | 61.8% | Only GPT-5.3-Codex measured |
| IOI (Vals AI) | Not measured | 53.8% | Only GPT-5.3-Codex measured |
| LiveCodeBench (Artificial Analysis run) | 23.3% | Not measured | Only Nova Pro 1.0 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 6.1% | 53.0% | GPT-5.3-Codex |
| Terminal-Bench | Not measured | 78.4% | Only GPT-5.3-Codex measured |
| WeirdML | Not measured | 79.3% | Only GPT-5.3-Codex measured |
| ALE-Bench | Not measured | 1655.2 | Only GPT-5.3-Codex measured |
| LMArena Text: coding | 1270 | Not measured | Only Nova Pro 1.0 measured |
| LMArena Text: software and IT | 1271 | Not measured | Only Nova Pro 1.0 measured |
| LMArena WebDev: React | Not measured | 1395 | Only GPT-5.3-Codex measured |
| LMArena WebDev: HTML | Not measured | 1422 | Only GPT-5.3-Codex measured |
| LMArena WebDev: from an image | Not measured | 1443 | Only GPT-5.3-Codex measured |
| Neither model has a result on the other 23 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Nova Pro 1.0 | GPT-5.3-Codex | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.0 | 32.5 | GPT-5.3-Codex |
| Epoch Capabilities Index | Not measured | 157.1 | Only GPT-5.3-Codex measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Nova Pro 1.0 | GPT-5.3-Codex | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | Not measured | 1032 | Only GPT-5.3-Codex measured |
| Design Arena: agents building full-stack apps | Not measured | 983 | Only GPT-5.3-Codex measured |
| Design Arena: agents building mobile apps | Not measured | 1053 | Only GPT-5.3-Codex measured |
| Design Arena: agents building Android apps | Not measured | 1084 | Only GPT-5.3-Codex measured |
| Design Arena: agents building Godot games | Not measured | 1124 | Only GPT-5.3-Codex measured |
| τ²-Bench Telecom | 14.0% | 86.0% | GPT-5.3-Codex |
| METR time horizons | Not measured | 74.5% | Only GPT-5.3-Codex measured |
| Vending-Bench 2 | Not measured | $5,940 | Only GPT-5.3-Codex measured |
| Neither model has a result on the other 31 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Nova Pro 1.0 | GPT-5.3-Codex | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 49.9% | 91.5% | GPT-5.3-Codex |
| Humanity's Last Exam (Artificial Analysis run) | 3.2% | 42.5% | GPT-5.3-Codex |
| LMArena Text: hard prompts | 1246 | Not measured | Only Nova Pro 1.0 measured |
| LMArena Text: expert | 1211 | Not measured | Only Nova Pro 1.0 measured |
| LMArena Text: hard prompts in English | 1259 | Not measured | Only Nova Pro 1.0 measured |
| Neither model has a result on the other 16 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | Nova Pro 1.0 | GPT-5.3-Codex | Leader |
|---|---|---|---|
| AA Math Index | 7.0 | Not measured | Only Nova Pro 1.0 measured |
| AIME 2025 (Artificial Analysis run) | 7.0% | Not measured | Only Nova Pro 1.0 measured |
| LMArena Text: math | 1252 | Not measured | Only Nova Pro 1.0 measured |
| LMArena Text: mathematical work | 1261 | Not measured | Only Nova Pro 1.0 measured |
| Neither model has a result on the other 9 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Nova Pro 1.0 | GPT-5.3-Codex | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 69.1% | Not measured | Only Nova Pro 1.0 measured |
| MMLU | 82.0% | Not measured | Only Nova Pro 1.0 measured |
| LMArena Text: science | 1255 | Not measured | Only Nova Pro 1.0 measured |
| LMArena Text: medicine and healthcare | 1260 | Not measured | Only Nova Pro 1.0 measured |
| LMArena Text: legal and government | 1281 | Not measured | Only Nova Pro 1.0 measured |
| LMArena Text: business and finance | 1229 | Not measured | Only Nova Pro 1.0 measured |
| 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 | GPT-5.3-Codex | Leader |
|---|---|---|---|
| Design Arena: websites | 796 | 1163 | GPT-5.3-Codex |
| Design Arena: UI components | Not measured | 1137 | Only GPT-5.3-Codex measured |
| Design Arena: game dev | Not measured | 1161 | Only GPT-5.3-Codex measured |
| Design Arena: data viz | Not measured | 1163 | Only GPT-5.3-Codex measured |
| Design Arena: 3D | Not measured | 1008 | Only GPT-5.3-Codex measured |
| Design Arena: all code categories | Not measured | 1145 | Only GPT-5.3-Codex measured |
| Design Arena: SVG | Not measured | 1129 | Only GPT-5.3-Codex measured |
| Design Arena: ASCII art | Not measured | 1151 | Only GPT-5.3-Codex measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Nova Pro 1.0 | GPT-5.3-Codex | Leader |
|---|---|---|---|
| IFBench | 38.1% | 75.4% | GPT-5.3-Codex |
| Long context reasoning (AA-LCR) | 21.0% | 83.3% | GPT-5.3-Codex |
| LMArena Text | 1259 | Not measured | Only Nova Pro 1.0 measured |
| LMArena Text: instruction following | 1235 | Not measured | Only Nova Pro 1.0 measured |
| LMArena Text: creative writing | 1212 | Not measured | Only Nova Pro 1.0 measured |
| LMArena Vision | 980 | Not measured | Only Nova Pro 1.0 measured |
| LMArena Text: English | 1276 | Not measured | Only Nova Pro 1.0 measured |
| LMArena Text: non-English | 1233 | Not measured | Only Nova Pro 1.0 measured |
| LMArena Text: Chinese | 1244 | Not measured | Only Nova Pro 1.0 measured |
| LMArena Text: German | 1243 | Not measured | Only Nova Pro 1.0 measured |
| LMArena Text: Japanese | 1200 | Not measured | Only Nova Pro 1.0 measured |
| LMArena Text: Korean | 1203 | Not measured | Only Nova Pro 1.0 measured |
| LMArena Text: French | 1271 | Not measured | Only Nova Pro 1.0 measured |
| LMArena Text: Spanish | 1182 | Not measured | Only Nova Pro 1.0 measured |
| LMArena Text: Russian | 1240 | Not measured | Only Nova Pro 1.0 measured |
| LMArena Text: multi-turn | 1246 | Not measured | Only Nova Pro 1.0 measured |
| LMArena Text: longer queries | 1255 | Not measured | Only Nova Pro 1.0 measured |
| LMArena Text: writing and language | 1247 | Not measured | Only Nova Pro 1.0 measured |
| LMArena Text: entertainment and media | 1210 | Not measured | Only Nova Pro 1.0 measured |
| LMArena Vision: English | 987 | Not measured | Only Nova Pro 1.0 measured |
| Neither model has a result on the other 26 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Nova Pro 1.0 | GPT-5.3-Codex | 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
Nova Pro 1.0 costs less
Repository review
50K input + 3K output tokens
Nova Pro 1.0 costs less
Agent loop
200K input + 10K output tokens
Nova Pro 1.0 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 | OpenAI GPT-5.3-Codex |
|---|---|---|
| Maker | Amazon | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | December 5, 2024 | February 24, 2026 |
| Context window | 300K | 400K |
| Input $/Mtok | $0.80 | $1.75 |
| Output $/Mtok | $3.20 | $14.00 |
| Coding index | Not benchmarked | 60.0 |
| Independent measurements behind it | 0 | 3 |
| Cost per solved task | Not computable | $0.59 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Nova Pro 1.0 better than GPT-5.3-Codex for coding?
Only GPT-5.3-Codex has a coding score, 60.0 on the Vibe Coding Index; Nova Pro 1.0 has none, so the two cannot be ranked on coding. Nova Pro 1.0 has the edge where output tokens cost 4.4x less.
Which is cheaper, Nova Pro 1.0 or GPT-5.3-Codex?
Nova Pro 1.0 costs $0.80 per million input tokens and $3.20 per million output tokens; GPT-5.3-Codex costs $1.75 and $14.00. Cost per solved task is known for GPT-5.3-Codex only.
Which has the bigger context window, Nova Pro 1.0 or GPT-5.3-Codex?
GPT-5.3-Codex, with 400K tokens against 300K.
How many benchmarks is the Nova Pro 1.0 vs GPT-5.3-Codex comparison based on?
8 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 GPT-5.3-Codex's on 3. Where only one model has a result, the page says so instead of naming a winner.