Llama 3.3 70B Instruct vs Nemotron 3 Super
Nemotron 3 Super leads Llama 3.3 70B Instruct on the Artificial Analysis Intelligence Index, 12.8 to 7.7, and Nemotron 3 Super costs 1.6x less per output token ($0.45 vs $0.72 per million). Updated October 22, 2026; 35 benchmarks have a result for both.
Llama 3.3 70B Instruct or Nemotron 3 Super: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Llama 3.3 70B Instruct for work measured by AA Coding Index or Terminal-Bench Hard (Artificial Analysis run), where it leads.
- Shared results
- 35
- Llama 3.3 70B Instruct only
- 17
- Nemotron 3 Super only
- 4
- 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
Nemotron 3 Super
Nemotron 3 Super leads, 12.8 against 7.7.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Nemotron 3 Super has a AA Agentic Index result, 1.7.
Directional only
Long documents
Listed context window
Nemotron 3 Super
Nemotron 3 Super takes 262K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Nemotron 3 Super
Llama 3.3 70B Instruct $0.0008, Nemotron 3 Super $0.0003 per turn.
List prices
At a glance
- Coding index
- Llama 3.3 70B Instruct not benchmarked, Nemotron 3 Super not benchmarked.
- Widest category
- Agentic work, 41.2 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): Llama 3.3 70B Instruct 15.7% against Nemotron 3 Super 65.7%, 50.0 points apart on a 0 to 100 scale.
- Output price
- Llama 3.3 70B Instruct $0.72 against Nemotron 3 Super $0.45 per million output tokens, 1.6x apart.
- Context window
- Llama 3.3 70B Instruct takes 131K tokens, Nemotron 3 Super 262K.
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 | Llama 3.3 70B Instruct | Bars | Nemotron 3 Super | Ties |
|---|---|---|---|---|
| Coding | 0 | 5 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 5 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 4 | ||
| General | 0 | 17 | ||
| All shared boards | 0 | 35 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index05
| Benchmark | Llama 3.3 70B Instruct | Nemotron 3 Super | Leader |
|---|---|---|---|
| AA Coding Index | 11.9 | 37.7 | Nemotron 3 Super |
| LiveCodeBench (Artificial Analysis run) | 28.8% | Not measured | Only Llama 3.3 70B Instruct measured |
| SciCode | Not measured | 36.2% | Only Nemotron 3 Super measured |
| Terminal-Bench Hard (Artificial Analysis run) | 3.0% | 28.8% | Nemotron 3 Super |
| WeirdML | 14.4% | 38.0% | Nemotron 3 Super |
| ALE-Bench | Not measured | 213.9 | Only Nemotron 3 Super measured |
| LMArena Text: coding | 1268 | 1403 | Nemotron 3 Super |
| LMArena Text: software and IT | 1273 | 1409 | Nemotron 3 Super |
| Neither model has a result on the other 35 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Llama 3.3 70B Instruct | Nemotron 3 Super | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.7 | 12.8 | Nemotron 3 Super |
| Epoch Capabilities Index | 127.3 | Not measured | Only Llama 3.3 70B Instruct measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Llama 3.3 70B Instruct | Nemotron 3 Super | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 1.7 | Only Nemotron 3 Super measured |
| τ²-Bench Telecom | 26.6% | 67.8% | Nemotron 3 Super |
| BALROG | 23.0% | Not measured | Only Llama 3.3 70B Instruct measured |
| Berkeley Function Calling Leaderboard (BFCL) | 31.9% | Not measured | Only Llama 3.3 70B Instruct measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | Llama 3.3 70B Instruct | Nemotron 3 Super | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 49.8% | 80.0% | Nemotron 3 Super |
| Humanity's Last Exam (Artificial Analysis run) | 3.6% | 20.8% | Nemotron 3 Super |
| GPQA Diamond (Epoch AI run) | 47.4% | Not measured | Only Llama 3.3 70B Instruct measured |
| SimpleBench | 19.9% | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: hard prompts | 1257 | 1386 | Nemotron 3 Super |
| LMArena Text: expert | 1225 | 1398 | Nemotron 3 Super |
| LMArena Text: hard prompts in English | 1277 | 1397 | Nemotron 3 Super |
| Neither model has a result on the other 16 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Llama 3.3 70B Instruct | Nemotron 3 Super | Leader |
|---|---|---|---|
| AA Math Index | 7.7 | Not measured | Only Llama 3.3 70B Instruct measured |
| AIME 2025 (Artificial Analysis run) | 7.7% | Not measured | Only Llama 3.3 70B Instruct measured |
| OTIS Mock AIME | 5.1% | Not measured | Only Llama 3.3 70B Instruct measured |
| MATH level 5 | 41.6% | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: math | 1267 | 1380 | Nemotron 3 Super |
| LMArena Text: mathematical work | 1267 | 1405 | Nemotron 3 Super |
| MathArena | Not measured | 16.6% | Only Nemotron 3 Super measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | Llama 3.3 70B Instruct | Nemotron 3 Super | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 71.3% | Not measured | Only Llama 3.3 70B Instruct measured |
| MMLU | 86.3% | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: science | 1284 | 1387 | Nemotron 3 Super |
| LMArena Text: medicine and healthcare | 1275 | 1383 | Nemotron 3 Super |
| LMArena Text: legal and government | 1284 | 1392 | Nemotron 3 Super |
| LMArena Text: business and finance | 1243 | 1364 | Nemotron 3 Super |
| Neither model has a result on the other 14 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Llama 3.3 70B Instruct | Nemotron 3 Super | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index017
| Benchmark | Llama 3.3 70B Instruct | Nemotron 3 Super | Leader |
|---|---|---|---|
| IFBench | 47.1% | 71.5% | Nemotron 3 Super |
| Long context reasoning (AA-LCR) | 15.7% | 65.7% | Nemotron 3 Super |
| DTBench | 59.5% | Not measured | Only Llama 3.3 70B Instruct measured |
| LMCA | 17.5% | Not measured | Only Llama 3.3 70B Instruct measured |
| Fiction.LiveBench | 33.3% | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text | 1274 | 1378 | Nemotron 3 Super |
| LMArena Text: instruction following | 1242 | 1347 | Nemotron 3 Super |
| LMArena Text: creative writing | 1250 | 1317 | Nemotron 3 Super |
| LMArena Text: English | 1303 | 1400 | Nemotron 3 Super |
| LMArena Text: non-English | 1236 | 1356 | Nemotron 3 Super |
| LMArena Text: Chinese | 1217 | 1431 | Nemotron 3 Super |
| LMArena Text: German | 1251 | 1341 | Nemotron 3 Super |
| LMArena Text: Japanese | 1150 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: Korean | 1142 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: French | 1282 | 1403 | Nemotron 3 Super |
| LMArena Text: Spanish | 1269 | 1418 | Nemotron 3 Super |
| LMArena Text: Russian | 1252 | 1336 | Nemotron 3 Super |
| LMArena Text: Polish | 1259 | 1346 | Nemotron 3 Super |
| LMArena Text: multi-turn | 1280 | 1369 | Nemotron 3 Super |
| LMArena Text: longer queries | 1255 | 1362 | Nemotron 3 Super |
| LMArena Text: writing and language | 1255 | 1332 | Nemotron 3 Super |
| LMArena Text: entertainment and media | 1241 | 1336 | Nemotron 3 Super |
| 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 | Llama 3.3 70B Instruct | Nemotron 3 Super | 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
Nemotron 3 Super costs less
Repository review
50K input + 3K output tokens
Nemotron 3 Super costs less
Agent loop
200K input + 10K output tokens
Nemotron 3 Super 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 | Meta Llama 3.3 70B Instruct | NVIDIA Nemotron 3 Super |
|---|---|---|
| Maker | Meta Llama | NVIDIA |
| Weights | Open weights | Open weights |
| Released | December 6, 2024 | March 11, 2026 |
| Context window | 131K | 262K |
| Input $/Mtok | $0.45 | $0.085 |
| Output $/Mtok | $0.72 | $0.45 |
| 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 Llama 3.3 70B Instruct better than Nemotron 3 Super for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Llama 3.3 70B Instruct for work measured by AA Coding Index or Terminal-Bench Hard (Artificial Analysis run), where it leads.
Which is cheaper, Llama 3.3 70B Instruct or Nemotron 3 Super?
Llama 3.3 70B Instruct costs $0.45 per million input tokens and $0.72 per million output tokens; Nemotron 3 Super costs $0.085 and $0.45. Cost per solved task is known for neither of them.
Which has the bigger context window, Llama 3.3 70B Instruct or Nemotron 3 Super?
Nemotron 3 Super, with 262K tokens against 131K.
How many benchmarks is the Llama 3.3 70B Instruct vs Nemotron 3 Super comparison based on?
35 benchmarks have a result for both models as of October 22, 2026. Llama 3.3 70B Instruct's coding index rests on 0 independent benchmarks and Nemotron 3 Super's on 0. Where only one model has a result, the page says so instead of naming a winner.