GPT-4.1 Nano vs Llama 3.2 1B Instruct
GPT-4.1 Nano leads Llama 3.2 1B Instruct on the Artificial Analysis Intelligence Index, 7.8 to 4.8, and Llama 3.2 1B Instruct costs 2.0x less per output token ($0.201 vs $0.40 per million). Updated October 22, 2026; 37 benchmarks have a result for both.
GPT-4.1 Nano or Llama 3.2 1B Instruct: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-4.1 Nano for a larger context window (1.05M against 60K tokens) and work measured by Humanity's Last Exam (Artificial Analysis run), where it leads.
- Shared results
- 37
- GPT-4.1 Nano only
- 34
- Llama 3.2 1B Instruct only
- 1
- 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
GPT-4.1 Nano
GPT-4.1 Nano leads, 7.8 against 4.8.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only GPT-4.1 Nano has a GPQA Diamond result, 50.8%.
Directional only
Math
AIME
No call
Only GPT-4.1 Nano has a AIME result, 26.5%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-4.1 Nano has a MMLU Pro result, 63.5%.
Directional only
Web design
Design Arena: websites
No call
Only GPT-4.1 Nano has a Design Arena: websites result, 973.
Directional only
Long documents
Listed context window
GPT-4.1 Nano
GPT-4.1 Nano takes 1.05M tokens against 60K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Llama 3.2 1B Instruct
GPT-4.1 Nano $0.0003, Llama 3.2 1B Instruct $0.0001 per turn.
List prices
At a glance
- Coding index
- GPT-4.1 Nano not benchmarked, Llama 3.2 1B Instruct not benchmarked.
- Widest category
- Knowledge, 42.7 points apart on average over 5 shared benchmarks.
- Widest single gap
- MMLU Pro (Artificial Analysis run): GPT-4.1 Nano 65.7% against Llama 3.2 1B Instruct 20.0%, 45.7 points apart on a 0 to 100 scale.
- Output price
- GPT-4.1 Nano $0.40 against Llama 3.2 1B Instruct $0.201 per million output tokens, 2.0x apart.
- Context window
- GPT-4.1 Nano takes 1.05M tokens, Llama 3.2 1B Instruct 60K.
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 | GPT-4.1 Nano | Bars | Llama 3.2 1B Instruct | Ties |
|---|---|---|---|---|
| Coding | 4 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 2 | 0 | ||
| Reasoning | 5 | 1 | ||
| Math | 5 | 0 | ||
| Knowledge | 5 | 0 | ||
| General | 14 | 0 | ||
| All shared boards | 36 | 1 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index40
| Benchmark | GPT-4.1 Nano | Llama 3.2 1B Instruct | Leader |
|---|---|---|---|
| Aider polyglot | 8.9% | Not measured | Only GPT-4.1 Nano measured |
| AA Coding Index | 11.1 | Not measured | Only GPT-4.1 Nano measured |
| LiveCodeBench (Vals AI) | 42.7% | Not measured | Only GPT-4.1 Nano measured |
| LiveCodeBench (Artificial Analysis run) | 32.6% | 1.9% | GPT-4.1 Nano |
| Terminal-Bench Hard (Artificial Analysis run) | 3.8% | 0.0% | GPT-4.1 Nano |
| WeirdML | 19.0% | Not measured | Only GPT-4.1 Nano measured |
| LMArena Text: coding | 1306 | 1070 | GPT-4.1 Nano |
| LMArena Text: software and IT | 1305 | 1056 | GPT-4.1 Nano |
| Neither model has a result on the other 31 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-4.1 Nano | Llama 3.2 1B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.8 | 4.8 | GPT-4.1 Nano |
| Epoch Capabilities Index | 129.5 | Not measured | Only GPT-4.1 Nano measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-4.1 Nano | Llama 3.2 1B Instruct | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 17.3% | 0.0% | GPT-4.1 Nano |
| BALROG | Not measured | 6.6% | Only Llama 3.2 1B Instruct measured |
| Berkeley Function Calling Leaderboard (BFCL) | 33.1% | 10.8% | GPT-4.1 Nano |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index51
| Benchmark | GPT-4.1 Nano | Llama 3.2 1B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond | 50.8% | Not measured | Only GPT-4.1 Nano measured |
| MMMU Pro (Vals AI) | 55.0% | Not measured | Only GPT-4.1 Nano measured |
| GPQA Diamond (Artificial Analysis run) | 51.2% | 19.6% | GPT-4.1 Nano |
| Humanity's Last Exam (Artificial Analysis run) | 3.8% | 5.5% | Llama 3.2 1B Instruct |
| GPQA Diamond (Epoch AI run) | 48.9% | 23.9% | GPT-4.1 Nano |
| ARC-AGI | 0.0% | Not measured | Only GPT-4.1 Nano measured |
| ARC-AGI-2 | 0.0% | Not measured | Only GPT-4.1 Nano measured |
| Chess puzzles | 0.0% | Not measured | Only GPT-4.1 Nano measured |
| LMArena Text: hard prompts | 1286 | 1044 | GPT-4.1 Nano |
| LMArena Text: expert | 1271 | 1007 | GPT-4.1 Nano |
| LMArena Text: hard prompts in English | 1293 | 1087 | GPT-4.1 Nano |
| Kagi LLM Benchmark | 33.3% | Not measured | Only GPT-4.1 Nano measured |
| Neither model has a result on the other 10 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GPT-4.1 Nano | Llama 3.2 1B Instruct | Leader |
|---|---|---|---|
| AIME | 26.5% | Not measured | Only GPT-4.1 Nano measured |
| MATH 500 (Vals AI) | 80.2% | Not measured | Only GPT-4.1 Nano measured |
| MGSM (Vals AI) | 69.3% | Not measured | Only GPT-4.1 Nano measured |
| AA Math Index | 24.0 | 0.0 | GPT-4.1 Nano |
| AIME 2025 (Artificial Analysis run) | 24.0% | 0.0% | GPT-4.1 Nano |
| OTIS Mock AIME | 28.9% | 0.6% | GPT-4.1 Nano |
| MATH level 5 | 70.0% | Not measured | Only GPT-4.1 Nano measured |
| LMArena Text: math | 1274 | 1086 | GPT-4.1 Nano |
| LMArena Text: mathematical work | 1274 | 1098 | GPT-4.1 Nano |
| Neither model has a result on the other 4 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GPT-4.1 Nano | Llama 3.2 1B Instruct | Leader |
|---|---|---|---|
| MMLU Pro | 63.5% | Not measured | Only GPT-4.1 Nano measured |
| LegalBench (Vals AI) | 61.1% | Not measured | Only GPT-4.1 Nano measured |
| CorpFin (Vals AI) | 42.1% | Not measured | Only GPT-4.1 Nano measured |
| TaxEval (Vals AI) | 60.8% | Not measured | Only GPT-4.1 Nano measured |
| MortgageTax (Vals AI) | 52.8% | Not measured | Only GPT-4.1 Nano measured |
| MedQA (Vals AI) | 68.2% | Not measured | Only GPT-4.1 Nano measured |
| MMLU Pro (Artificial Analysis run) | 65.7% | 20.0% | GPT-4.1 Nano |
| SimpleQA Verified | 6.0% | Not measured | Only GPT-4.1 Nano measured |
| LMArena Text: science | 1297 | 1055 | GPT-4.1 Nano |
| LMArena Text: medicine and healthcare | 1276 | 1009 | GPT-4.1 Nano |
| LMArena Text: legal and government | 1300 | 1071 | GPT-4.1 Nano |
| LMArena Text: business and finance | 1267 | 1000 | GPT-4.1 Nano |
| Neither model has a result on the other 6 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-4.1 Nano | Llama 3.2 1B Instruct | Leader |
|---|---|---|---|
| Design Arena: websites | 973 | Not measured | Only GPT-4.1 Nano measured |
| Design Arena: UI components | 913 | Not measured | Only GPT-4.1 Nano measured |
| Design Arena: game dev | 970 | Not measured | Only GPT-4.1 Nano measured |
| Design Arena: data viz | 891 | Not measured | Only GPT-4.1 Nano measured |
| Design Arena: 3D | 924 | Not measured | Only GPT-4.1 Nano measured |
| Design Arena: all code categories | 962 | Not measured | Only GPT-4.1 Nano measured |
| Neither model has a result on the other 3 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index140
| Benchmark | GPT-4.1 Nano | Llama 3.2 1B Instruct | Leader |
|---|---|---|---|
| IFBench | 32.0% | 22.8% | GPT-4.1 Nano |
| Long context reasoning (AA-LCR) | 20.3% | 6.7% | GPT-4.1 Nano |
| DTBench | 52.5% | Not measured | Only GPT-4.1 Nano measured |
| LMCA | 5.5% | Not measured | Only GPT-4.1 Nano measured |
| Fiction.LiveBench | 25.0% | Not measured | Only GPT-4.1 Nano measured |
| LMArena Text | 1285 | 1055 | GPT-4.1 Nano |
| LMArena Text: instruction following | 1266 | 1031 | GPT-4.1 Nano |
| LMArena Text: creative writing | 1260 | 1033 | GPT-4.1 Nano |
| LMArena Vision | 1063 | Not measured | Only GPT-4.1 Nano measured |
| LMArena Text: English | 1303 | 1117 | GPT-4.1 Nano |
| LMArena Text: non-English | 1260 | 973 | GPT-4.1 Nano |
| LMArena Text: Chinese | 1270 | 959 | GPT-4.1 Nano |
| LMArena Text: German | 1288 | 1014 | GPT-4.1 Nano |
| LMArena Text: Japanese | 1198 | Not measured | Only GPT-4.1 Nano measured |
| LMArena Text: Russian | 1261 | 941 | GPT-4.1 Nano |
| LMArena Text: multi-turn | 1277 | 1030 | GPT-4.1 Nano |
| LMArena Text: longer queries | 1283 | 1050 | GPT-4.1 Nano |
| LMArena Text: writing and language | 1261 | 1030 | GPT-4.1 Nano |
| LMArena Text: entertainment and media | 1243 | 1025 | GPT-4.1 Nano |
| LMArena Vision: English | 1101 | Not measured | Only GPT-4.1 Nano measured |
| 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 | GPT-4.1 Nano | Llama 3.2 1B 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 3.2 1B Instruct costs less
Repository review
50K input + 3K output tokens
Llama 3.2 1B Instruct costs less
Agent loop
200K input + 10K output tokens
Llama 3.2 1B 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 | OpenAI GPT-4.1 Nano | Meta Llama 3.2 1B Instruct |
|---|---|---|
| Maker | OpenAI | Meta Llama |
| Weights | No public weights listed | Open weights |
| Released | April 14, 2025 | September 25, 2024 |
| Context window | 1.05M | 60K |
| Input $/Mtok | $0.10 | $0.027 |
| Output $/Mtok | $0.40 | $0.201 |
| 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 GPT-4.1 Nano better than Llama 3.2 1B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-4.1 Nano for a larger context window (1.05M against 60K tokens) and work measured by Humanity's Last Exam (Artificial Analysis run), where it leads.
Which is cheaper, GPT-4.1 Nano or Llama 3.2 1B Instruct?
GPT-4.1 Nano costs $0.10 per million input tokens and $0.40 per million output tokens; Llama 3.2 1B Instruct costs $0.027 and $0.201. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-4.1 Nano or Llama 3.2 1B Instruct?
GPT-4.1 Nano, with 1.05M tokens against 60K.
How many benchmarks is the GPT-4.1 Nano vs Llama 3.2 1B Instruct comparison based on?
37 benchmarks have a result for both models as of October 22, 2026. GPT-4.1 Nano's coding index rests on 0 independent benchmarks and Llama 3.2 1B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.