Nova Pro 1.0 vs gpt-oss-20b
gpt-oss-20b leads Nova Pro 1.0 on the Artificial Analysis Intelligence Index, 10.0 to 7.0, and gpt-oss-20b costs 21.3x less per output token ($0.15 vs $3.20 per million). Updated October 21, 2026; 38 benchmarks have a result for both.
Nova Pro 1.0 or gpt-oss-20b: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Nova Pro 1.0 for a larger context window (300K against 131K tokens) and work measured by LiveCodeBench (Artificial Analysis run) or Terminal-Bench Hard (Artificial Analysis run), where it leads.
- Shared results
- 38
- Nova Pro 1.0 only
- 4
- gpt-oss-20b only
- 24
- Like-for-like categories
- 8 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
gpt-oss-20b
gpt-oss-20b leads, 10.0 against 7.0.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only gpt-oss-20b has a GPQA Diamond result, 68.9%.
Directional only
Math
AIME
No call
Only gpt-oss-20b has a AIME result, 86.0%.
Directional only
Knowledge
MMLU Pro
No call
Only gpt-oss-20b has a MMLU Pro result, 71.6%.
Directional only
Web design
Design Arena: websites
gpt-oss-20b
gpt-oss-20b leads, 852 against 796.
Same board, both measured
Long documents
Listed context window
Nova Pro 1.0
Nova Pro 1.0 takes 300K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
gpt-oss-20b
Nova Pro 1.0 $0.0024, gpt-oss-20b $0.0001 per turn.
List prices
At a glance
- Coding index
- Nova Pro 1.0 not benchmarked, gpt-oss-20b not benchmarked.
- Widest category
- Agentic work, 46.2 points apart on average over 1 shared benchmark.
- Widest single gap
- AIME 2025 (Artificial Analysis run): Nova Pro 1.0 7.0% against gpt-oss-20b 89.3%, 82.3 points apart on a 0 to 100 scale.
- Output price
- Nova Pro 1.0 $3.20 against gpt-oss-20b $0.15 per million output tokens, 21.3x apart.
- Context window
- Nova Pro 1.0 takes 300K tokens, gpt-oss-20b 131K.
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-oss-20b | Ties |
|---|---|---|---|---|
| Coding | 0 | 4 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 5 | ||
| Math | 0 | 4 | ||
| Knowledge | 1 | 4 | ||
| Web design | 0 | 1 | ||
| General | 3 | 14 | ||
| All shared boards | 4 | 34 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index04
| Benchmark | Nova Pro 1.0 | gpt-oss-20b | Leader |
|---|---|---|---|
| LiveCodeBench (Vals AI) | Not measured | 80.4% | Only gpt-oss-20b measured |
| LiveCodeBench (Artificial Analysis run) | 23.3% | 77.7% | gpt-oss-20b |
| SciCode | Not measured | 38.9% | Only gpt-oss-20b measured |
| Terminal-Bench Hard (Artificial Analysis run) | 6.1% | 10.6% | gpt-oss-20b |
| Terminal-Bench | Not measured | 3.4% | Only gpt-oss-20b measured |
| WeirdML | Not measured | 40.9% | Only gpt-oss-20b measured |
| ALE-Bench | Not measured | 566.0 | Only gpt-oss-20b measured |
| LMArena Text: coding | 1270 | 1307 | gpt-oss-20b |
| LMArena Text: software and IT | 1271 | 1311 | gpt-oss-20b |
| Neither model has a result on the other 30 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Nova Pro 1.0 | gpt-oss-20b | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.0 | 10.0 | gpt-oss-20b |
| Epoch Capabilities Index | Not measured | 137.7 | Only gpt-oss-20b measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Nova Pro 1.0 | gpt-oss-20b | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 14.0% | 60.2% | gpt-oss-20b |
| BrowseComp-Plus | Not measured | 50.6% | Only gpt-oss-20b measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | Nova Pro 1.0 | gpt-oss-20b | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 68.9% | Only gpt-oss-20b measured |
| GPQA Diamond (Artificial Analysis run) | 49.9% | 68.8% | gpt-oss-20b |
| Humanity's Last Exam (Artificial Analysis run) | 3.2% | 11.0% | gpt-oss-20b |
| GPQA Diamond (Epoch AI run) | Not measured | 60.8% | Only gpt-oss-20b measured |
| LMArena Text: hard prompts | 1246 | 1274 | gpt-oss-20b |
| LMArena Text: expert | 1211 | 1258 | gpt-oss-20b |
| LMArena Text: hard prompts in English | 1259 | 1292 | gpt-oss-20b |
| Kagi LLM Benchmark | Not measured | 53.2% | Only gpt-oss-20b measured |
| Neither model has a result on the other 14 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | Nova Pro 1.0 | gpt-oss-20b | Leader |
|---|---|---|---|
| AIME | Not measured | 86.0% | Only gpt-oss-20b measured |
| MATH 500 (Vals AI) | Not measured | 94.2% | Only gpt-oss-20b measured |
| MGSM (Vals AI) | Not measured | 89.0% | Only gpt-oss-20b measured |
| AA Math Index | 7.0 | 89.3 | gpt-oss-20b |
| AIME 2025 (Artificial Analysis run) | 7.0% | 89.3% | gpt-oss-20b |
| OTIS Mock AIME | Not measured | 65.3% | Only gpt-oss-20b measured |
| LMArena Text: math | 1252 | 1317 | gpt-oss-20b |
| LMArena Text: mathematical work | 1261 | 1305 | gpt-oss-20b |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index14
| Benchmark | Nova Pro 1.0 | gpt-oss-20b | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 71.6% | Only gpt-oss-20b measured |
| LegalBench (Vals AI) | Not measured | 70.8% | Only gpt-oss-20b measured |
| CaseLaw (Vals AI) | Not measured | 43.8% | Only gpt-oss-20b measured |
| CorpFin (Vals AI) | Not measured | 53.1% | Only gpt-oss-20b measured |
| TaxEval (Vals AI) | Not measured | 63.7% | Only gpt-oss-20b measured |
| MedQA (Vals AI) | Not measured | 82.9% | Only gpt-oss-20b measured |
| MMLU Pro (Artificial Analysis run) | 69.1% | 74.8% | gpt-oss-20b |
| MMLU | 82.0% | Not measured | Only Nova Pro 1.0 measured |
| LMArena Text: science | 1255 | 1296 | gpt-oss-20b |
| LMArena Text: medicine and healthcare | 1260 | 1310 | gpt-oss-20b |
| LMArena Text: legal and government | 1281 | 1280 | Nova Pro 1.0 |
| LMArena Text: business and finance | 1229 | 1284 | gpt-oss-20b |
| Neither model has a result on the other 6 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Nova Pro 1.0 | gpt-oss-20b | Leader |
|---|---|---|---|
| Design Arena: websites | 796 | 852 | gpt-oss-20b |
| Design Arena: data viz | Not measured | 933 | Only gpt-oss-20b measured |
| Neither model has a result on the other 7 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index314
| Benchmark | Nova Pro 1.0 | gpt-oss-20b | Leader |
|---|---|---|---|
| IFBench | 38.1% | 65.1% | gpt-oss-20b |
| Long context reasoning (AA-LCR) | 21.0% | 34.7% | gpt-oss-20b |
| DTBench | Not measured | 68.0% | Only gpt-oss-20b measured |
| LMCA | Not measured | 14.5% | Only gpt-oss-20b measured |
| LMArena Text | 1259 | 1288 | gpt-oss-20b |
| LMArena Text: instruction following | 1235 | 1236 | gpt-oss-20b |
| LMArena Text: creative writing | 1212 | 1201 | Nova Pro 1.0 |
| LMArena Vision | 980 | Not measured | Only Nova Pro 1.0 measured |
| LMArena Text: English | 1276 | 1303 | gpt-oss-20b |
| LMArena Text: non-English | 1233 | 1269 | gpt-oss-20b |
| LMArena Text: Chinese | 1244 | 1315 | gpt-oss-20b |
| LMArena Text: German | 1243 | 1255 | gpt-oss-20b |
| LMArena Text: Japanese | 1200 | 1244 | gpt-oss-20b |
| LMArena Text: Korean | 1203 | 1236 | gpt-oss-20b |
| LMArena Text: French | 1271 | Not measured | Only Nova Pro 1.0 measured |
| LMArena Text: Spanish | 1182 | 1267 | gpt-oss-20b |
| LMArena Text: Russian | 1240 | 1278 | gpt-oss-20b |
| LMArena Text: Polish | Not measured | 1252 | Only gpt-oss-20b measured |
| LMArena Text: multi-turn | 1246 | 1268 | gpt-oss-20b |
| LMArena Text: longer queries | 1255 | 1250 | Nova Pro 1.0 |
| LMArena Text: writing and language | 1247 | 1238 | Nova Pro 1.0 |
| LMArena Text: entertainment and media | 1210 | 1216 | gpt-oss-20b |
| LMArena Vision: English | 987 | Not measured | Only Nova Pro 1.0 measured |
| Neither model has a result on the other 23 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Nova Pro 1.0 | gpt-oss-20b | 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-oss-20b costs less
Repository review
50K input + 3K output tokens
gpt-oss-20b costs less
Agent loop
200K input + 10K output tokens
gpt-oss-20b 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-oss-20b |
|---|---|---|
| Maker | Amazon | OpenAI |
| Weights | No public weights listed | Open weights |
| Released | December 5, 2024 | August 5, 2025 |
| Context window | 300K | 131K |
| Input $/Mtok | $0.80 | $0.03 |
| Output $/Mtok | $3.20 | $0.15 |
| 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 gpt-oss-20b for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Nova Pro 1.0 for a larger context window (300K against 131K tokens) and work measured by LiveCodeBench (Artificial Analysis run) or Terminal-Bench Hard (Artificial Analysis run), where it leads.
Which is cheaper, Nova Pro 1.0 or gpt-oss-20b?
Nova Pro 1.0 costs $0.80 per million input tokens and $3.20 per million output tokens; gpt-oss-20b costs $0.03 and $0.15. Cost per solved task is known for neither of them.
Which has the bigger context window, Nova Pro 1.0 or gpt-oss-20b?
Nova Pro 1.0, with 300K tokens against 131K.
How many benchmarks is the Nova Pro 1.0 vs gpt-oss-20b comparison based on?
38 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-oss-20b's on 0. Where only one model has a result, the page says so instead of naming a winner.