Nova Pro 1.0 vs Llama 3.1 8B Instruct
Nova Pro 1.0 leads Llama 3.1 8B Instruct on the Artificial Analysis Intelligence Index, 7.0 to 6.9, and Llama 3.1 8B Instruct costs 40.0x less per output token ($0.08 vs $3.20 per million). Updated October 21, 2026; 39 benchmarks have a result for both.
Nova Pro 1.0 or Llama 3.1 8B Instruct: 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 τ²-Bench Telecom or Humanity's Last Exam (Artificial Analysis run), where it leads.
- Shared results
- 39
- Nova Pro 1.0 only
- 3
- Llama 3.1 8B Instruct only
- 12
- Like-for-like categories
- 7 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
Nova Pro 1.0
Nova Pro 1.0 leads, 7.0 against 6.9.
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 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Llama 3.1 8B Instruct
Nova Pro 1.0 $0.0024, Llama 3.1 8B Instruct $0.0001 per turn.
List prices
At a glance
- Coding index
- Nova Pro 1.0 not benchmarked, Llama 3.1 8B Instruct not benchmarked.
- Widest category
- Knowledge, 15.6 points apart on average over 6 shared benchmarks.
- Widest single gap
- MMLU: Nova Pro 1.0 82.0% against Llama 3.1 8B Instruct 56.1%, 25.9 points apart on a 0 to 100 scale.
- Output price
- Nova Pro 1.0 $3.20 against Llama 3.1 8B Instruct $0.08 per million output tokens, 40.0x apart.
- Context window
- Nova Pro 1.0 takes 300K tokens, Llama 3.1 8B Instruct 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 | Llama 3.1 8B Instruct | Ties |
|---|---|---|---|---|
| Coding | 4 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 4 | 1 | ||
| Math | 4 | 0 | ||
| Knowledge | 6 | 0 | ||
| General | 18 | 0 | ||
| All shared boards | 37 | 2 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index40
| Benchmark | Nova Pro 1.0 | Llama 3.1 8B Instruct | Leader |
|---|---|---|---|
| AA Coding Index | Not measured | 5.4 | Only Llama 3.1 8B Instruct measured |
| LiveCodeBench (Artificial Analysis run) | 23.3% | 11.6% | Nova Pro 1.0 |
| Terminal-Bench Hard (Artificial Analysis run) | 6.1% | 0.8% | Nova Pro 1.0 |
| WeirdML | Not measured | 1.7% | Only Llama 3.1 8B Instruct measured |
| LMArena Text: coding | 1270 | 1195 | Nova Pro 1.0 |
| LMArena Text: software and IT | 1271 | 1195 | Nova Pro 1.0 |
| Neither model has a result on the other 33 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Nova Pro 1.0 | Llama 3.1 8B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.0 | 6.9 | Nova Pro 1.0 |
| Epoch Capabilities Index | Not measured | 116.5 | Only Llama 3.1 8B Instruct measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Nova Pro 1.0 | Llama 3.1 8B Instruct | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 14.0% | 16.4% | Llama 3.1 8B Instruct |
| BALROG | Not measured | 15.1% | Only Llama 3.1 8B Instruct measured |
| Berkeley Function Calling Leaderboard (BFCL) | Not measured | 25.8% | Only Llama 3.1 8B Instruct measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index41
| Benchmark | Nova Pro 1.0 | Llama 3.1 8B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 49.9% | 25.9% | Nova Pro 1.0 |
| Humanity's Last Exam (Artificial Analysis run) | 3.2% | 5.3% | Llama 3.1 8B Instruct |
| GPQA Diamond (Epoch AI run) | Not measured | 27.0% | Only Llama 3.1 8B Instruct measured |
| LMArena Text: hard prompts | 1246 | 1175 | Nova Pro 1.0 |
| LMArena Text: expert | 1211 | 1144 | Nova Pro 1.0 |
| LMArena Text: hard prompts in English | 1259 | 1196 | Nova Pro 1.0 |
| Neither model has a result on the other 16 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | Nova Pro 1.0 | Llama 3.1 8B Instruct | Leader |
|---|---|---|---|
| AA Math Index | 7.0 | 4.3 | Nova Pro 1.0 |
| AIME 2025 (Artificial Analysis run) | 7.0% | 4.3% | Nova Pro 1.0 |
| OTIS Mock AIME | Not measured | 1.7% | Only Llama 3.1 8B Instruct measured |
| MATH level 5 | Not measured | 22.9% | Only Llama 3.1 8B Instruct measured |
| GSM8K | Not measured | 82.4% | Only Llama 3.1 8B Instruct measured |
| LMArena Text: math | 1252 | 1179 | Nova Pro 1.0 |
| LMArena Text: mathematical work | 1261 | 1180 | Nova Pro 1.0 |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | Nova Pro 1.0 | Llama 3.1 8B Instruct | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 69.1% | 47.6% | Nova Pro 1.0 |
| MMLU | 82.0% | 56.1% | Nova Pro 1.0 |
| LMArena Text: science | 1255 | 1193 | Nova Pro 1.0 |
| LMArena Text: medicine and healthcare | 1260 | 1182 | Nova Pro 1.0 |
| LMArena Text: legal and government | 1281 | 1206 | Nova Pro 1.0 |
| LMArena Text: business and finance | 1229 | 1166 | Nova Pro 1.0 |
| Neither model has a result on the other 12 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Nova Pro 1.0 | Llama 3.1 8B 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 index180
| Benchmark | Nova Pro 1.0 | Llama 3.1 8B Instruct | Leader |
|---|---|---|---|
| IFBench | 38.1% | 28.6% | Nova Pro 1.0 |
| Long context reasoning (AA-LCR) | 21.0% | 18.0% | Nova Pro 1.0 |
| DTBench | Not measured | 50.9% | Only Llama 3.1 8B Instruct measured |
| LMCA | Not measured | 5.4% | Only Llama 3.1 8B Instruct measured |
| PIQA | Not measured | 81.2% | Only Llama 3.1 8B Instruct measured |
| LMArena Text | 1259 | 1187 | Nova Pro 1.0 |
| LMArena Text: instruction following | 1235 | 1159 | Nova Pro 1.0 |
| LMArena Text: creative writing | 1212 | 1154 | Nova Pro 1.0 |
| LMArena Vision | 980 | Not measured | Only Nova Pro 1.0 measured |
| LMArena Text: English | 1276 | 1218 | Nova Pro 1.0 |
| LMArena Text: non-English | 1233 | 1148 | Nova Pro 1.0 |
| LMArena Text: Chinese | 1244 | 1151 | Nova Pro 1.0 |
| LMArena Text: German | 1243 | 1144 | Nova Pro 1.0 |
| LMArena Text: Japanese | 1200 | 1061 | Nova Pro 1.0 |
| LMArena Text: Korean | 1203 | 1053 | Nova Pro 1.0 |
| LMArena Text: French | 1271 | 1178 | Nova Pro 1.0 |
| LMArena Text: Spanish | 1182 | 1169 | Nova Pro 1.0 |
| LMArena Text: Russian | 1240 | 1158 | Nova Pro 1.0 |
| LMArena Text: multi-turn | 1246 | 1172 | Nova Pro 1.0 |
| LMArena Text: longer queries | 1255 | 1181 | Nova Pro 1.0 |
| LMArena Text: writing and language | 1247 | 1162 | Nova Pro 1.0 |
| LMArena Text: entertainment and media | 1210 | 1145 | Nova Pro 1.0 |
| 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 | Llama 3.1 8B 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
Llama 3.1 8B Instruct costs less
Repository review
50K input + 3K output tokens
Llama 3.1 8B Instruct costs less
Agent loop
200K input + 10K output tokens
Llama 3.1 8B 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 | Meta Llama 3.1 8B Instruct |
|---|---|---|
| Maker | Amazon | Meta Llama |
| Weights | No public weights listed | Open weights |
| Released | December 5, 2024 | July 23, 2024 |
| Context window | 300K | 131K |
| Input $/Mtok | $0.80 | $0.05 |
| Output $/Mtok | $3.20 | $0.08 |
| 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 Llama 3.1 8B Instruct 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 τ²-Bench Telecom or Humanity's Last Exam (Artificial Analysis run), where it leads.
Which is cheaper, Nova Pro 1.0 or Llama 3.1 8B Instruct?
Nova Pro 1.0 costs $0.80 per million input tokens and $3.20 per million output tokens; Llama 3.1 8B Instruct costs $0.05 and $0.08. Cost per solved task is known for neither of them.
Which has the bigger context window, Nova Pro 1.0 or Llama 3.1 8B Instruct?
Nova Pro 1.0, with 300K tokens against 131K.
How many benchmarks is the Nova Pro 1.0 vs Llama 3.1 8B Instruct comparison based on?
39 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 Llama 3.1 8B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.