Llama 3.2 1B Instruct vs Llama 3.3 70B Instruct
Llama 3.3 70B Instruct leads Llama 3.2 1B Instruct on the Artificial Analysis Intelligence Index, 7.7 to 4.8, and Llama 3.2 1B Instruct costs 3.6x less per output token ($0.201 vs $0.72 per million). Updated October 21, 2026; 38 benchmarks have a result for both.
Llama 3.2 1B Instruct or Llama 3.3 70B Instruct: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Llama 3.2 1B Instruct for output tokens 3.6x cheaper ($0.201 vs $0.72 per million) and work measured by LiveCodeBench (Artificial Analysis run) or Terminal-Bench Hard (Artificial Analysis run), where it leads.
- Shared results
- 38
- Llama 3.2 1B Instruct only
- 0
- Llama 3.3 70B Instruct only
- 14
- 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
Llama 3.3 70B Instruct
Llama 3.3 70B Instruct leads, 7.7 against 4.8.
Same board, both measured
Long documents
Listed context window
Llama 3.3 70B Instruct
Llama 3.3 70B Instruct takes 131K tokens against 60K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Llama 3.2 1B Instruct
Llama 3.2 1B Instruct $0.0001, Llama 3.3 70B Instruct $0.0008 per turn.
List prices
At a glance
- Coding index
- Llama 3.2 1B Instruct not benchmarked, Llama 3.3 70B Instruct not benchmarked.
- Widest category
- Knowledge, 42.0 points apart on average over 5 shared benchmarks.
- Widest single gap
- MMLU Pro (Artificial Analysis run): Llama 3.2 1B Instruct 20.0% against Llama 3.3 70B Instruct 71.3%, 51.3 points apart on a 0 to 100 scale.
- Output price
- Llama 3.2 1B Instruct $0.201 against Llama 3.3 70B Instruct $0.72 per million output tokens, 3.6x apart.
- Context window
- Llama 3.2 1B Instruct takes 60K tokens, Llama 3.3 70B 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 | Llama 3.2 1B Instruct | Bars | Llama 3.3 70B Instruct | Ties |
|---|---|---|---|---|
| Coding | 0 | 4 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 3 | ||
| Reasoning | 1 | 5 | ||
| Math | 0 | 5 | ||
| Knowledge | 0 | 5 | ||
| General | 0 | 14 | ||
| All shared boards | 1 | 37 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index04
| Benchmark | Llama 3.2 1B Instruct | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| AA Coding Index | Not measured | 11.9 | Only Llama 3.3 70B Instruct measured |
| LiveCodeBench (Artificial Analysis run) | 1.9% | 28.8% | Llama 3.3 70B Instruct |
| Terminal-Bench Hard (Artificial Analysis run) | 0.0% | 3.0% | Llama 3.3 70B Instruct |
| WeirdML | Not measured | 14.4% | Only Llama 3.3 70B Instruct measured |
| LMArena Text: coding | 1070 | 1268 | Llama 3.3 70B Instruct |
| LMArena Text: software and IT | 1056 | 1273 | Llama 3.3 70B Instruct |
| Neither model has a result on the other 33 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Llama 3.2 1B Instruct | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 4.8 | 7.7 | Llama 3.3 70B Instruct |
| Epoch Capabilities Index | Not measured | 127.3 | Only Llama 3.3 70B Instruct measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | Llama 3.2 1B Instruct | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 0.0% | 26.6% | Llama 3.3 70B Instruct |
| BALROG | 6.6% | 23.0% | Llama 3.3 70B Instruct |
| Berkeley Function Calling Leaderboard (BFCL) | 10.8% | 31.9% | Llama 3.3 70B Instruct |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index15
| Benchmark | Llama 3.2 1B Instruct | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 19.6% | 49.8% | Llama 3.3 70B Instruct |
| Humanity's Last Exam (Artificial Analysis run) | 5.5% | 3.6% | Llama 3.2 1B Instruct |
| GPQA Diamond (Epoch AI run) | 23.9% | 47.4% | Llama 3.3 70B Instruct |
| SimpleBench | Not measured | 19.9% | Only Llama 3.3 70B Instruct measured |
| LMArena Text: hard prompts | 1044 | 1257 | Llama 3.3 70B Instruct |
| LMArena Text: expert | 1007 | 1225 | Llama 3.3 70B Instruct |
| LMArena Text: hard prompts in English | 1087 | 1277 | Llama 3.3 70B Instruct |
| Neither model has a result on the other 15 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | Llama 3.2 1B Instruct | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| AA Math Index | 0.0 | 7.7 | Llama 3.3 70B Instruct |
| AIME 2025 (Artificial Analysis run) | 0.0% | 7.7% | Llama 3.3 70B Instruct |
| OTIS Mock AIME | 0.6% | 5.1% | Llama 3.3 70B Instruct |
| MATH level 5 | Not measured | 41.6% | Only Llama 3.3 70B Instruct measured |
| LMArena Text: math | 1086 | 1267 | Llama 3.3 70B Instruct |
| LMArena Text: mathematical work | 1098 | 1267 | Llama 3.3 70B Instruct |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | Llama 3.2 1B Instruct | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 20.0% | 71.3% | Llama 3.3 70B Instruct |
| MMLU | Not measured | 86.3% | Only Llama 3.3 70B Instruct measured |
| LMArena Text: science | 1055 | 1284 | Llama 3.3 70B Instruct |
| LMArena Text: medicine and healthcare | 1009 | 1275 | Llama 3.3 70B Instruct |
| LMArena Text: legal and government | 1071 | 1284 | Llama 3.3 70B Instruct |
| LMArena Text: business and finance | 1000 | 1243 | Llama 3.3 70B Instruct |
| Neither model has a result on the other 12 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Llama 3.2 1B Instruct | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index014
| Benchmark | Llama 3.2 1B Instruct | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| IFBench | 22.8% | 47.1% | Llama 3.3 70B Instruct |
| Long context reasoning (AA-LCR) | 6.7% | 15.7% | Llama 3.3 70B Instruct |
| DTBench | Not measured | 59.5% | Only Llama 3.3 70B Instruct measured |
| LMCA | Not measured | 17.5% | Only Llama 3.3 70B Instruct measured |
| Fiction.LiveBench | Not measured | 33.3% | Only Llama 3.3 70B Instruct measured |
| LMArena Text | 1055 | 1274 | Llama 3.3 70B Instruct |
| LMArena Text: instruction following | 1031 | 1242 | Llama 3.3 70B Instruct |
| LMArena Text: creative writing | 1033 | 1250 | Llama 3.3 70B Instruct |
| LMArena Text: English | 1117 | 1303 | Llama 3.3 70B Instruct |
| LMArena Text: non-English | 973 | 1236 | Llama 3.3 70B Instruct |
| LMArena Text: Chinese | 959 | 1217 | Llama 3.3 70B Instruct |
| LMArena Text: German | 1014 | 1251 | Llama 3.3 70B Instruct |
| LMArena Text: Japanese | Not measured | 1150 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: Korean | Not measured | 1142 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: French | Not measured | 1282 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: Spanish | Not measured | 1269 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: Russian | 941 | 1252 | Llama 3.3 70B Instruct |
| LMArena Text: Polish | Not measured | 1259 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: multi-turn | 1030 | 1280 | Llama 3.3 70B Instruct |
| LMArena Text: longer queries | 1050 | 1255 | Llama 3.3 70B Instruct |
| LMArena Text: writing and language | 1030 | 1255 | Llama 3.3 70B Instruct |
| LMArena Text: entertainment and media | 1025 | 1241 | Llama 3.3 70B Instruct |
| Neither model has a result on the other 24 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Llama 3.2 1B Instruct | Llama 3.3 70B 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 | Meta Llama 3.2 1B Instruct | Meta Llama 3.3 70B Instruct |
|---|---|---|
| Maker | Meta Llama | Meta Llama |
| Weights | Open weights | Open weights |
| Released | September 25, 2024 | December 6, 2024 |
| Context window | 60K | 131K |
| Input $/Mtok | $0.027 | $0.45 |
| Output $/Mtok | $0.201 | $0.72 |
| 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 3.2 1B Instruct better than Llama 3.3 70B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Llama 3.2 1B Instruct for output tokens 3.6x cheaper ($0.201 vs $0.72 per million) and work measured by LiveCodeBench (Artificial Analysis run) or Terminal-Bench Hard (Artificial Analysis run), where it leads.
Which is cheaper, Llama 3.2 1B Instruct or Llama 3.3 70B Instruct?
Llama 3.2 1B Instruct costs $0.027 per million input tokens and $0.201 per million output tokens; Llama 3.3 70B Instruct costs $0.45 and $0.72. Cost per solved task is known for neither of them.
Which has the bigger context window, Llama 3.2 1B Instruct or Llama 3.3 70B Instruct?
Llama 3.3 70B Instruct, with 131K tokens against 60K.
How many benchmarks is the Llama 3.2 1B Instruct vs Llama 3.3 70B Instruct comparison based on?
38 benchmarks have a result for both models as of October 21, 2026. Llama 3.2 1B Instruct's coding index rests on 0 independent benchmarks and Llama 3.3 70B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.