Nova Micro 1.0 vs Qwen2.5 Coder 32B Instruct
Qwen2.5 Coder 32B Instruct leads Nova Micro 1.0 on the Artificial Analysis Intelligence Index, 6.7 to 5.9, and Nova Micro 1.0 costs 7.1x less per output token ($0.14 vs $1.00 per million). Updated October 21, 2026; 28 benchmarks have a result for both.
Nova Micro 1.0 or Qwen2.5 Coder 32B Instruct: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Nova Micro 1.0 for output tokens 7.1x cheaper ($0.14 vs $1.00 per million) and a larger context window (128K against 33K tokens).
- Shared results
- 28
- Nova Micro 1.0 only
- 11
- Qwen2.5 Coder 32B Instruct only
- 7
- Like-for-like categories
- 6 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
Qwen2.5 Coder 32B Instruct
Qwen2.5 Coder 32B Instruct leads, 6.7 against 5.9.
Same board, both measured
Long documents
Listed context window
Nova Micro 1.0
Nova Micro 1.0 takes 128K tokens against 33K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Nova Micro 1.0
Nova Micro 1.0 $0.0001, Qwen2.5 Coder 32B Instruct $0.0012 per turn.
List prices
At a glance
- Coding index
- Nova Micro 1.0 not benchmarked, Qwen2.5 Coder 32B Instruct not benchmarked.
- Widest category
- Coding, 10.9 points apart on average over 3 shared benchmarks.
- Widest single gap
- LiveCodeBench (Artificial Analysis run): Nova Micro 1.0 14.0% against Qwen2.5 Coder 32B Instruct 29.5%, 15.5 points apart on a 0 to 100 scale.
- Output price
- Nova Micro 1.0 $0.14 against Qwen2.5 Coder 32B Instruct $1.00 per million output tokens, 7.1x apart.
- Context window
- Nova Micro 1.0 takes 128K 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 Micro 1.0 | Bars | Qwen2.5 Coder 32B Instruct | Ties |
|---|---|---|---|---|
| Coding | 0 | 3 | ||
| Intelligence | 0 | 1 | ||
| Reasoning | 1 | 4 | ||
| Math | 0 | 2 | ||
| Knowledge | 1 | 5 | ||
| General | 2 | 9 | ||
| All shared boards | 4 | 24 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
| Benchmark | Nova Micro 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) | 14.0% | 29.5% | Qwen2.5 Coder 32B Instruct |
| Terminal-Bench Hard (Artificial Analysis run) | 1.5% | Not measured | Only Nova Micro 1.0 measured |
| LMArena Text: coding | 1218 | 1276 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: software and IT | 1215 | 1261 | Qwen2.5 Coder 32B Instruct |
| Neither model has a result on the other 32 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Nova Micro 1.0 | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 5.9 | 6.7 | Qwen2.5 Coder 32B Instruct |
| 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 Micro 1.0 | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 14.0% | Not measured | Only Nova Micro 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 Micro 1.0 | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 35.8% | 41.7% | Qwen2.5 Coder 32B Instruct |
| Humanity's Last Exam (Artificial Analysis run) | 4.6% | 3.5% | Nova Micro 1.0 |
| LMArena Text: hard prompts | 1191 | 1251 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: expert | 1183 | 1221 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: hard prompts in English | 1203 | 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 index02
| Benchmark | Nova Micro 1.0 | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| AA Math Index | 6.0 | Not measured | Only Nova Micro 1.0 measured |
| AIME 2025 (Artificial Analysis run) | 6.0% | Not measured | Only Nova Micro 1.0 measured |
| GSM8K | Not measured | 93.0% | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: math | 1206 | 1251 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: mathematical work | 1212 | 1243 | Qwen2.5 Coder 32B Instruct |
| Neither model has a result on the other 8 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index15
| Benchmark | Nova Micro 1.0 | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 53.1% | 63.5% | Qwen2.5 Coder 32B Instruct |
| MMLU | 70.8% | 79.1% | Qwen2.5 Coder 32B Instruct |
| AI2 Reasoning Challenge | Not measured | 70.5% | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: science | 1210 | 1221 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: medicine and healthcare | 1207 | 1187 | Nova Micro 1.0 |
| LMArena Text: legal and government | 1221 | 1257 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: business and finance | 1182 | 1224 | Qwen2.5 Coder 32B Instruct |
| Neither model has a result on the other 10 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Nova Micro 1.0 | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index29
| Benchmark | Nova Micro 1.0 | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| IFBench | 29.4% | Not measured | Only Nova Micro 1.0 measured |
| Long context reasoning (AA-LCR) | 13.0% | Not measured | Only Nova Micro 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 | 1208 | 1230 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: instruction following | 1174 | 1223 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: creative writing | 1172 | 1174 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: English | 1224 | 1248 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: non-English | 1186 | 1205 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: Chinese | 1209 | 1222 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: German | 1192 | Not measured | Only Nova Micro 1.0 measured |
| LMArena Text: Japanese | 1154 | Not measured | Only Nova Micro 1.0 measured |
| LMArena Text: Korean | 1150 | Not measured | Only Nova Micro 1.0 measured |
| LMArena Text: French | 1238 | Not measured | Only Nova Micro 1.0 measured |
| LMArena Text: Spanish | 1225 | Not measured | Only Nova Micro 1.0 measured |
| LMArena Text: Russian | 1185 | 1228 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: multi-turn | 1178 | 1222 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: longer queries | 1205 | 1251 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: writing and language | 1192 | 1191 | Nova Micro 1.0 |
| LMArena Text: entertainment and media | 1163 | 1150 | Nova Micro 1.0 |
| 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 Micro 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
Nova Micro 1.0 costs less
Repository review
50K input + 3K output tokens
Nova Micro 1.0 costs less
Agent loop
200K input + 10K output tokens
Nova Micro 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 Micro 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 | 128K | 33K |
| Input $/Mtok | $0.035 | $0.66 |
| Output $/Mtok | $0.14 | $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 Micro 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. Pick Nova Micro 1.0 for output tokens 7.1x cheaper ($0.14 vs $1.00 per million) and a larger context window (128K against 33K tokens).
Which is cheaper, Nova Micro 1.0 or Qwen2.5 Coder 32B Instruct?
Nova Micro 1.0 costs $0.035 per million input tokens and $0.14 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 Micro 1.0 or Qwen2.5 Coder 32B Instruct?
Nova Micro 1.0, with 128K tokens against 33K.
How many benchmarks is the Nova Micro 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 Micro 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.