Nova Pro 1.0 vs Qwen2.5 Coder 32B Instruct
Updated October 21, 2026. 28 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 33K tokens, and where what matters is LiveCodeBench (Artificial Analysis run) or LMArena Text: coding, where it leads.
- Shared results
- 28
- Nova Pro 1.0 only
- 14
- Qwen2.5 Coder 32B Instruct only
- 7
- 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
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
Nova Pro 1.0
Nova Pro 1.0 leads, 7.0 against 6.7.
Same board, both measured
Web design
Design Arena: websites
No call
Only Nova Pro 1.0 has a Design Arena: websites result, 796.
Directional only
Long documents
Listed context window
Nova Pro 1.0
Nova Pro 1.0 takes 300K tokens against 33K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen2.5 Coder 32B Instruct
Nova Pro 1.0 $0.0024, Qwen2.5 Coder 32B Instruct $0.0012 per turn.
List prices
At a glance
- Coding index
- Nova Pro 1.0 not benchmarked, Qwen2.5 Coder 32B Instruct not benchmarked.
- Widest category
- Knowledge, 5.2 points apart on average over 6 shared benchmarks.
- Widest single gap
- LMArena Text: medicine and healthcare: Nova Pro 1.0 1260 against Qwen2.5 Coder 32B Instruct 1187, 12.3 points apart on a 0 to 100 scale.
- Output price
- Nova Pro 1.0 $3.20 against Qwen2.5 Coder 32B Instruct $1.00 per million output tokens, 3.2x apart.
- Context window
- Nova Pro 1.0 takes 300K tokens, Qwen2.5 Coder 32B Instruct 33K.
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 | Qwen2.5 Coder 32B Instruct | Ties |
|---|---|---|---|---|
| Coding | 1 | 2 | ||
| Intelligence | 1 | 0 | ||
| Reasoning | 1 | 4 | ||
| Math | 2 | 0 | ||
| Knowledge | 6 | 0 | ||
| General | 11 | 0 | ||
| All shared boards | 22 | 6 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index12
| Benchmark | Nova Pro 1.0 | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 16.4% | Only Qwen2.5 Coder 32B Instruct measured |
| SWE-bench Verified | Not measured | 47.0% | Only Qwen2.5 Coder 32B Instruct measured |
| LiveCodeBench (Artificial Analysis run) | 23.3% | 29.5% | Qwen2.5 Coder 32B Instruct |
| Terminal-Bench Hard (Artificial Analysis run) | 6.1% | Not measured | Only Nova Pro 1.0 measured |
| LMArena Text: coding | 1270 | 1276 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: software and IT | 1271 | 1261 | Nova Pro 1.0 |
| Neither model has a result on the other 32 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Nova Pro 1.0 | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.0 | 6.7 | Nova Pro 1.0 |
| Epoch Capabilities Index | Not measured | 119.5 | Only Qwen2.5 Coder 32B Instruct measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Nova Pro 1.0 | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 14.0% | Not measured | Only Nova Pro 1.0 measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index14
| Benchmark | Nova Pro 1.0 | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 49.9% | 41.7% | Nova Pro 1.0 |
| Humanity's Last Exam (Artificial Analysis run) | 3.2% | 3.5% | Qwen2.5 Coder 32B Instruct |
| LMArena Text: hard prompts | 1246 | 1251 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: expert | 1211 | 1221 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: hard prompts in English | 1259 | 1269 | Qwen2.5 Coder 32B Instruct |
| Neither model has a result on the other 16 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Nova Pro 1.0 | Qwen2.5 Coder 32B Instruct | 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 |
| GSM8K | Not measured | 93.0% | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: math | 1252 | 1251 | Nova Pro 1.0 |
| LMArena Text: mathematical work | 1261 | 1243 | Nova Pro 1.0 |
| Neither model has a result on the other 8 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | Nova Pro 1.0 | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 69.1% | 63.5% | Nova Pro 1.0 |
| MMLU | 82.0% | 79.1% | Nova Pro 1.0 |
| AI2 Reasoning Challenge | Not measured | 70.5% | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: science | 1255 | 1221 | Nova Pro 1.0 |
| LMArena Text: medicine and healthcare | 1260 | 1187 | Nova Pro 1.0 |
| LMArena Text: legal and government | 1281 | 1257 | Nova Pro 1.0 |
| LMArena Text: business and finance | 1229 | 1224 | Nova Pro 1.0 |
| Neither model has a result on the other 10 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Nova Pro 1.0 | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| Design Arena: websites | 796 | Not measured | Only Nova Pro 1.0 measured |
| Neither model has a result on the other 8 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index110
| Benchmark | Nova Pro 1.0 | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| IFBench | 38.1% | Not measured | Only Nova Pro 1.0 measured |
| Long context reasoning (AA-LCR) | 21.0% | Not measured | Only Nova Pro 1.0 measured |
| HellaSwag | Not measured | 83.0% | Only Qwen2.5 Coder 32B Instruct measured |
| WinoGrande | Not measured | 80.8% | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text | 1259 | 1230 | Nova Pro 1.0 |
| LMArena Text: instruction following | 1235 | 1223 | Nova Pro 1.0 |
| LMArena Text: creative writing | 1212 | 1174 | Nova Pro 1.0 |
| LMArena Vision | 980 | Not measured | Only Nova Pro 1.0 measured |
| LMArena Text: English | 1276 | 1248 | Nova Pro 1.0 |
| LMArena Text: non-English | 1233 | 1205 | Nova Pro 1.0 |
| LMArena Text: Chinese | 1244 | 1222 | Nova Pro 1.0 |
| 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 | 1228 | Nova Pro 1.0 |
| LMArena Text: multi-turn | 1246 | 1222 | Nova Pro 1.0 |
| LMArena Text: longer queries | 1255 | 1251 | Nova Pro 1.0 |
| LMArena Text: writing and language | 1247 | 1191 | Nova Pro 1.0 |
| LMArena Text: entertainment and media | 1210 | 1150 | Nova Pro 1.0 |
| LMArena Vision: English | 987 | Not measured | Only Nova Pro 1.0 measured |
| Neither model has a result on the other 24 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Nova Pro 1.0 | Qwen2.5 Coder 32B Instruct | 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
Qwen2.5 Coder 32B Instruct costs less
Repository review
50K input + 3K output tokens
Qwen2.5 Coder 32B Instruct costs less
Agent loop
200K input + 10K output tokens
Qwen2.5 Coder 32B Instruct 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 | Qwen2.5 Coder 32B Instruct |
|---|---|---|
| Maker | Amazon | Qwen |
| Weights | No public weights listed | Open weights |
| Released | December 5, 2024 | November 11, 2024 |
| Context window | 300K | 33K |
| Input $/Mtok | $0.80 | $0.66 |
| Output $/Mtok | $3.20 | $1.00 |
| 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 Pro 1.0 better than Qwen2.5 Coder 32B Instruct 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 33K tokens, and where what matters is LiveCodeBench (Artificial Analysis run) or LMArena Text: coding, where it leads.
Which is cheaper, Nova Pro 1.0 or Qwen2.5 Coder 32B Instruct?
Nova Pro 1.0 costs $0.80 per million input tokens and $3.20 per million output tokens; Qwen2.5 Coder 32B Instruct costs $0.66 and $1.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Nova Pro 1.0 or Qwen2.5 Coder 32B Instruct?
Nova Pro 1.0, with 300K tokens against 33K.
How many benchmarks is the Nova Pro 1.0 vs Qwen2.5 Coder 32B Instruct comparison based on?
28 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 Qwen2.5 Coder 32B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.