Nova Pro 1.0 vs Mercury 2
Updated October 21, 2026. 27 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 128K tokens, and where what matters is Terminal-Bench Hard (Artificial Analysis run) or LMArena Text: coding, where it leads.
- Shared results
- 27
- Nova Pro 1.0 only
- 15
- Mercury 2 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
Mercury 2
Mercury 2 leads, 10.8 against 7.0.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Mercury 2 has a AA Agentic Index result, 1.6.
Directional only
Web design
Design Arena: websites
Mercury 2
Mercury 2 leads, 1036 against 796.
Same board, both measured
Long documents
Listed context window
Nova Pro 1.0
Nova Pro 1.0 takes 300K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mercury 2
Nova Pro 1.0 $0.0024, Mercury 2 $0.0006 per turn.
List prices
At a glance
- Coding index
- Nova Pro 1.0 not benchmarked, Mercury 2 not benchmarked.
- Widest category
- Agentic work, 56.7 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: Nova Pro 1.0 14.0% against Mercury 2 70.8%, 56.7 points apart on a 0 to 100 scale.
- Output price
- Nova Pro 1.0 $3.20 against Mercury 2 $0.75 per million output tokens, 4.3x apart.
- Context window
- Nova Pro 1.0 takes 300K tokens, Mercury 2 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 Pro 1.0 | Bars | Mercury 2 | Ties |
|---|---|---|---|---|
| Coding | 0 | 3 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 5 | ||
| Knowledge | 0 | 3 | ||
| Web design | 0 | 1 | ||
| General | 0 | 13 | ||
| All shared boards | 0 | 27 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
| Benchmark | Nova Pro 1.0 | Mercury 2 | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1171 | Only Mercury 2 measured |
| AA Coding Index | Not measured | 31.1 | Only Mercury 2 measured |
| LiveCodeBench (Artificial Analysis run) | 23.3% | Not measured | Only Nova Pro 1.0 measured |
| SciCode | Not measured | 37.7% | Only Mercury 2 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 6.1% | 26.5% | Mercury 2 |
| WeirdML | Not measured | 43.2% | Only Mercury 2 measured |
| ALE-Bench | Not measured | 785.6 | Only Mercury 2 measured |
| LMArena Text: coding | 1270 | 1390 | Mercury 2 |
| LMArena Text: software and IT | 1271 | 1385 | Mercury 2 |
| LMArena WebDev: React | Not measured | 1160 | Only Mercury 2 measured |
| LMArena WebDev: HTML | Not measured | 1199 | Only Mercury 2 measured |
| Neither model has a result on the other 27 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Nova Pro 1.0 | Mercury 2 | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.0 | 10.8 | Mercury 2 |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Nova Pro 1.0 | Mercury 2 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 1.6 | Only Mercury 2 measured |
| τ²-Bench Telecom | 14.0% | 70.8% | Mercury 2 |
| 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 | Mercury 2 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 49.9% | 77.0% | Mercury 2 |
| Humanity's Last Exam (Artificial Analysis run) | 3.2% | 17.1% | Mercury 2 |
| LMArena Text: hard prompts | 1246 | 1362 | Mercury 2 |
| LMArena Text: expert | 1211 | 1358 | Mercury 2 |
| LMArena Text: hard prompts in English | 1259 | 1374 | Mercury 2 |
| 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 | Mercury 2 | 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 | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | Nova Pro 1.0 | Mercury 2 | 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 | 1343 | Mercury 2 |
| LMArena Text: medicine and healthcare | 1260 | 1363 | Mercury 2 |
| LMArena Text: legal and government | 1281 | Not measured | Only Nova Pro 1.0 measured |
| LMArena Text: business and finance | 1229 | 1349 | Mercury 2 |
| 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 | Mercury 2 | Leader |
|---|---|---|---|
| Design Arena: websites | 796 | 1036 | Mercury 2 |
| Design Arena: UI components | Not measured | 998 | Only Mercury 2 measured |
| Design Arena: game dev | Not measured | 985 | Only Mercury 2 measured |
| Design Arena: data viz | Not measured | 1009 | Only Mercury 2 measured |
| Design Arena: 3D | Not measured | 983 | Only Mercury 2 measured |
| Design Arena: all code categories | Not measured | 1022 | Only Mercury 2 measured |
| Design Arena: SVG | Not measured | 998 | Only Mercury 2 measured |
| Design Arena: ASCII art | Not measured | 990 | Only Mercury 2 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index013
| Benchmark | Nova Pro 1.0 | Mercury 2 | Leader |
|---|---|---|---|
| IFBench | 38.1% | 69.8% | Mercury 2 |
| Long context reasoning (AA-LCR) | 21.0% | 43.7% | Mercury 2 |
| LMArena Text | 1259 | 1354 | Mercury 2 |
| LMArena Text: instruction following | 1235 | 1328 | Mercury 2 |
| LMArena Text: creative writing | 1212 | 1286 | Mercury 2 |
| LMArena Vision | 980 | Not measured | Only Nova Pro 1.0 measured |
| LMArena Text: English | 1276 | 1378 | Mercury 2 |
| LMArena Text: non-English | 1233 | 1330 | Mercury 2 |
| LMArena Text: Chinese | 1244 | 1414 | Mercury 2 |
| 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 | 1304 | Mercury 2 |
| LMArena Text: multi-turn | 1246 | 1357 | Mercury 2 |
| LMArena Text: longer queries | 1255 | 1329 | Mercury 2 |
| LMArena Text: writing and language | 1247 | 1300 | Mercury 2 |
| LMArena Text: entertainment and media | 1210 | 1314 | Mercury 2 |
| 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 | Mercury 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
Mercury 2 costs less
Repository review
50K input + 3K output tokens
Mercury 2 costs less
Agent loop
200K input + 10K output tokens
Mercury 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 | Amazon Nova Pro 1.0 | Inception Mercury 2 |
|---|---|---|
| Maker | Amazon | Inception |
| Weights | No public weights listed | No public weights listed |
| Released | December 5, 2024 | March 4, 2026 |
| Context window | 300K | 128K |
| Input $/Mtok | $0.80 | $0.25 |
| Output $/Mtok | $3.20 | $0.75 |
| 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 Mercury 2 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 128K tokens, and where what matters is Terminal-Bench Hard (Artificial Analysis run) or LMArena Text: coding, where it leads.
Which is cheaper, Nova Pro 1.0 or Mercury 2?
Nova Pro 1.0 costs $0.80 per million input tokens and $3.20 per million output tokens; Mercury 2 costs $0.25 and $0.75. Cost per solved task is known for neither of them.
Which has the bigger context window, Nova Pro 1.0 or Mercury 2?
Nova Pro 1.0, with 300K tokens against 128K.
How many benchmarks is the Nova Pro 1.0 vs Mercury 2 comparison based on?
27 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 Mercury 2's on 1. Where only one model has a result, the page says so instead of naming a winner.