Llama 3.2 3B Instruct vs Nemotron 3.5 Super VL
Nemotron 3.5 Super VL leads Llama 3.2 3B Instruct on the Artificial Analysis Intelligence Index, 26.5 to 5.7, and Llama 3.2 3B Instruct costs 1.5x less per output token ($0.3325 vs $0.50 per million). Updated October 22, 2026; 3 benchmarks have a result for both.
Llama 3.2 3B Instruct or Nemotron 3.5 Super VL: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Llama 3.2 3B Instruct for output tokens 1.5x cheaper ($0.3325 vs $0.50 per million) and work measured by AA Intelligence Index or Humanity's Last Exam (Artificial Analysis run), where it leads.
- Shared results
- 3
- Llama 3.2 3B Instruct only
- 32
- Nemotron 3.5 Super VL only
- 13
- Like-for-like categories
- 3 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 3.5 Super VL
Nemotron 3.5 Super VL leads, 26.5 against 5.7.
Same board, both measured
Web design
Design Arena: websites
No call
Only Nemotron 3.5 Super VL has a Design Arena: websites result, 1173.
Directional only
Long documents
Listed context window
Nemotron 3.5 Super VL
Nemotron 3.5 Super VL takes 1.05M tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Llama 3.2 3B Instruct
Llama 3.2 3B Instruct $0.0002, Nemotron 3.5 Super VL $0.0004 per turn.
List prices
At a glance
- Coding index
- Llama 3.2 3B Instruct not benchmarked, Nemotron 3.5 Super VL not benchmarked.
- Widest category
- General, 64.7 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): Llama 3.2 3B Instruct 4.3% against Nemotron 3.5 Super VL 69.0%, 64.7 points apart on a 0 to 100 scale.
- Output price
- Llama 3.2 3B Instruct $0.3325 against Nemotron 3.5 Super VL $0.50 per million output tokens, 1.5x apart.
- Context window
- Llama 3.2 3B Instruct takes 131K tokens, Nemotron 3.5 Super VL 1.05M.
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 3.2 3B Instruct | Bars | Nemotron 3.5 Super VL | Ties |
|---|---|---|---|---|
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 1 | ||
| General | 0 | 1 | ||
| All shared boards | 0 | 3 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index5 boards, none shared
| Benchmark | Llama 3.2 3B Instruct | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 65.5% | Only Nemotron 3.5 Super VL measured |
| LiveCodeBench (Artificial Analysis run) | 8.3% | Not measured | Only Llama 3.2 3B Instruct measured |
| SciCode | Not measured | 47.8% | Only Nemotron 3.5 Super VL measured |
| LMArena Text: coding | 1098 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: software and IT | 1103 | Not measured | Only Llama 3.2 3B Instruct measured |
| Neither model has a result on the other 34 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Llama 3.2 3B Instruct | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| AA Intelligence Index | 5.7 | 26.5 | Nemotron 3.5 Super VL |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Llama 3.2 3B Instruct | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| LiveBench research | Not measured | 70.9% | Only Nemotron 3.5 Super VL measured |
| LiveBench browser | Not measured | 4.0% | Only Nemotron 3.5 Super VL measured |
| LiveBench long agentic | Not measured | 15.8% | Only Nemotron 3.5 Super VL measured |
| τ²-Bench Telecom | 21.1% | Not measured | Only Llama 3.2 3B Instruct measured |
| BALROG | 10.1% | Not measured | Only Llama 3.2 3B Instruct measured |
| Berkeley Function Calling Leaderboard (BFCL) | 21.9% | Not measured | Only Llama 3.2 3B Instruct measured |
| Neither model has a result on the other 35 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Llama 3.2 3B Instruct | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| LiveBench reasoning | Not measured | 78.8% | Only Nemotron 3.5 Super VL measured |
| GPQA Diamond (Artificial Analysis run) | 25.5% | Not measured | Only Llama 3.2 3B Instruct measured |
| Humanity's Last Exam (Artificial Analysis run) | 5.3% | 30.7% | Nemotron 3.5 Super VL |
| LMArena Text: hard prompts | 1095 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: expert | 1090 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: hard prompts in English | 1141 | Not measured | Only Llama 3.2 3B Instruct measured |
| Neither model has a result on the other 16 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | Llama 3.2 3B Instruct | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| AA Math Index | 3.3 | Not measured | Only Llama 3.2 3B Instruct measured |
| AIME 2025 (Artificial Analysis run) | 3.3% | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: math | 1126 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: mathematical work | 1128 | Not measured | Only Llama 3.2 3B Instruct measured |
| Neither model has a result on the other 9 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | Llama 3.2 3B Instruct | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 34.7% | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: science | 1106 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: medicine and healthcare | 1104 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: legal and government | 1115 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: business and finance | 1074 | Not measured | Only Llama 3.2 3B Instruct measured |
| Neither model has a result on the other 13 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Llama 3.2 3B Instruct | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1173 | Only Nemotron 3.5 Super VL measured |
| Design Arena: UI components | Not measured | 1149 | Only Nemotron 3.5 Super VL measured |
| Design Arena: game dev | Not measured | 1110 | Only Nemotron 3.5 Super VL measured |
| Design Arena: 3D | Not measured | 1127 | Only Nemotron 3.5 Super VL measured |
| Design Arena: all code categories | Not measured | 1155 | Only Nemotron 3.5 Super VL measured |
| LiveBench design | Not measured | 60.7% | Only Nemotron 3.5 Super VL measured |
| Neither model has a result on the other 3 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Llama 3.2 3B Instruct | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| LiveBench data analysis | Not measured | 64.6% | Only Nemotron 3.5 Super VL measured |
| IFBench | 26.2% | Not measured | Only Llama 3.2 3B Instruct measured |
| Long context reasoning (AA-LCR) | 4.3% | 69.0% | Nemotron 3.5 Super VL |
| LMArena Text | 1110 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: instruction following | 1089 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: creative writing | 1094 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: English | 1180 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: non-English | 1019 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: Chinese | 1017 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: German | 1056 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: Russian | 949 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: multi-turn | 1105 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: longer queries | 1100 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: writing and language | 1096 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: entertainment and media | 1083 | Not measured | Only Llama 3.2 3B Instruct measured |
| Neither model has a result on the other 31 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Llama 3.2 3B Instruct | Nemotron 3.5 Super VL | 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 3.2 3B Instruct costs less
Repository review
50K input + 3K output tokens
Llama 3.2 3B Instruct costs less
Agent loop
200K input + 10K output tokens
Llama 3.2 3B 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 | Meta Llama 3.2 3B Instruct | NVIDIA Nemotron 3.5 Super VL |
|---|---|---|
| Maker | Meta Llama | NVIDIA |
| Weights | Open weights | Open weights |
| Released | September 25, 2024 | October 15, 2026 |
| Context window | 131K | 1.05M |
| Input $/Mtok | $0.0505 | $0.15 |
| Output $/Mtok | $0.3325 | $0.50 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Llama 3.2 3B Instruct better than Nemotron 3.5 Super VL for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Llama 3.2 3B Instruct for output tokens 1.5x cheaper ($0.3325 vs $0.50 per million) and work measured by AA Intelligence Index or Humanity's Last Exam (Artificial Analysis run), where it leads.
Which is cheaper, Llama 3.2 3B Instruct or Nemotron 3.5 Super VL?
Llama 3.2 3B Instruct costs $0.0505 per million input tokens and $0.3325 per million output tokens; Nemotron 3.5 Super VL costs $0.15 and $0.50. Cost per solved task is known for neither of them.
Which has the bigger context window, Llama 3.2 3B Instruct or Nemotron 3.5 Super VL?
Nemotron 3.5 Super VL, with 1.05M tokens against 131K.
How many benchmarks is the Llama 3.2 3B Instruct vs Nemotron 3.5 Super VL comparison based on?
3 benchmarks have a result for both models as of October 22, 2026. Llama 3.2 3B Instruct's coding index rests on 0 independent benchmarks and Nemotron 3.5 Super VL's on 1. Where only one model has a result, the page says so instead of naming a winner.