Llama 3.2 1B Instruct vs Qwen-Plus
Updated October 21, 2026. 24 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Neither model has a coding score yet, so the two cannot be ranked on coding.
Llama 3.2 1B Instruct has the edge where output tokens cost 3.9x less, and where what matters is LMArena Text: coding or LMArena Text: software and IT, where it leads.
- Shared results
- 24
- Llama 3.2 1B Instruct only
- 13
- Qwen-Plus only
- 4
- Like-for-like categories
- 5 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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
No call
Only Llama 3.2 1B Instruct has a AA Intelligence Index result, 4.8.
Directional only
Long documents
Listed context window
Qwen-Plus
Qwen-Plus takes 1M 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, Qwen-Plus $0.0006 per turn.
List prices
At a glance
- Coding index
- Llama 3.2 1B Instruct not benchmarked, Qwen-Plus not benchmarked.
- Widest category
- Knowledge, 50.8 points apart on average over 4 shared benchmarks.
- Widest single gap
- LMArena Text: Chinese: Llama 3.2 1B Instruct 959 against Qwen-Plus 1347, 57.8 points apart on a 0 to 100 scale.
- Output price
- Llama 3.2 1B Instruct $0.201 against Qwen-Plus $0.78 per million output tokens, 3.9x apart.
- Context window
- Llama 3.2 1B Instruct takes 60K tokens, Qwen-Plus 1M.
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 | Qwen-Plus | Ties |
|---|---|---|---|---|
| Coding | 0 | 2 | ||
| Reasoning | 0 | 4 | ||
| Math | 0 | 3 | ||
| Knowledge | 0 | 4 | ||
| General | 0 | 11 | ||
| All shared boards | 0 | 24 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
| Benchmark | Llama 3.2 1B Instruct | Qwen-Plus | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | 1.9% | Not measured | Only Llama 3.2 1B Instruct measured |
| Terminal-Bench Hard (Artificial Analysis run) | 0.0% | Not measured | Only Llama 3.2 1B Instruct measured |
| LMArena Text: coding | 1070 | 1328 | Qwen-Plus |
| LMArena Text: software and IT | 1056 | 1343 | Qwen-Plus |
| Neither model has a result on the other 34 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Llama 3.2 1B Instruct | Qwen-Plus | Leader |
|---|---|---|---|
| AA Intelligence Index | 4.8 | Not measured | Only Llama 3.2 1B Instruct measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Llama 3.2 1B Instruct | Qwen-Plus | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 0.0% | Not measured | Only Llama 3.2 1B Instruct measured |
| BALROG | 6.6% | Not measured | Only Llama 3.2 1B Instruct measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | Llama 3.2 1B Instruct | Qwen-Plus | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 19.6% | Not measured | Only Llama 3.2 1B Instruct measured |
| Humanity's Last Exam (Artificial Analysis run) | 5.5% | Not measured | Only Llama 3.2 1B Instruct measured |
| GPQA Diamond (Epoch AI run) | 23.9% | 48.1% | Qwen-Plus |
| LMArena Text: hard prompts | 1044 | 1317 | Qwen-Plus |
| LMArena Text: expert | 1007 | 1328 | Qwen-Plus |
| LMArena Text: hard prompts in English | 1087 | 1333 | Qwen-Plus |
| Neither model has a result on the other 15 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | Llama 3.2 1B Instruct | Qwen-Plus | Leader |
|---|---|---|---|
| AA Math Index | 0.0 | Not measured | Only Llama 3.2 1B Instruct measured |
| AIME 2025 (Artificial Analysis run) | 0.0% | Not measured | Only Llama 3.2 1B Instruct measured |
| OTIS Mock AIME | 0.6% | 17.8% | Qwen-Plus |
| MATH level 5 | Not measured | 65.3% | Only Qwen-Plus measured |
| LMArena Text: math | 1086 | 1326 | Qwen-Plus |
| LMArena Text: mathematical work | 1098 | 1332 | Qwen-Plus |
| 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 3.2 1B Instruct | Qwen-Plus | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 20.0% | Not measured | Only Llama 3.2 1B Instruct measured |
| LMArena Text: science | 1055 | 1352 | Qwen-Plus |
| LMArena Text: medicine and healthcare | 1009 | 1350 | Qwen-Plus |
| LMArena Text: legal and government | 1071 | 1349 | Qwen-Plus |
| LMArena Text: business and finance | 1000 | 1303 | Qwen-Plus |
| 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 | Qwen-Plus | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index011
| Benchmark | Llama 3.2 1B Instruct | Qwen-Plus | Leader |
|---|---|---|---|
| IFBench | 22.8% | Not measured | Only Llama 3.2 1B Instruct measured |
| Long context reasoning (AA-LCR) | 6.7% | Not measured | Only Llama 3.2 1B Instruct measured |
| DTBench | Not measured | 81.1% | Only Qwen-Plus measured |
| LMCA | Not measured | 24.0% | Only Qwen-Plus measured |
| LMArena Text | 1055 | 1326 | Qwen-Plus |
| LMArena Text: instruction following | 1031 | 1303 | Qwen-Plus |
| LMArena Text: creative writing | 1033 | 1292 | Qwen-Plus |
| LMArena Text: English | 1117 | 1339 | Qwen-Plus |
| LMArena Text: non-English | 973 | 1310 | Qwen-Plus |
| LMArena Text: Chinese | 959 | 1347 | Qwen-Plus |
| LMArena Text: German | 1014 | Not measured | Only Llama 3.2 1B Instruct measured |
| LMArena Text: Japanese | Not measured | 1251 | Only Qwen-Plus measured |
| LMArena Text: Russian | 941 | 1323 | Qwen-Plus |
| LMArena Text: multi-turn | 1030 | 1336 | Qwen-Plus |
| LMArena Text: longer queries | 1050 | 1324 | Qwen-Plus |
| LMArena Text: writing and language | 1030 | 1296 | Qwen-Plus |
| LMArena Text: entertainment and media | 1025 | 1270 | Qwen-Plus |
| Neither model has a result on the other 29 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Llama 3.2 1B Instruct | Qwen-Plus | 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 | Qwen-Plus |
|---|---|---|
| Maker | Meta Llama | Qwen |
| Weights | Open weights | No public weights listed |
| Released | September 25, 2024 | February 1, 2025 |
| Context window | 60K | 1M |
| Input $/Mtok | $0.027 | $0.26 |
| Output $/Mtok | $0.201 | $0.78 |
| 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 Qwen-Plus for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Llama 3.2 1B Instruct has the edge where output tokens cost 3.9x less, and where what matters is LMArena Text: coding or LMArena Text: software and IT, where it leads.
Which is cheaper, Llama 3.2 1B Instruct or Qwen-Plus?
Llama 3.2 1B Instruct costs $0.027 per million input tokens and $0.201 per million output tokens; Qwen-Plus costs $0.26 and $0.78. Cost per solved task is known for neither of them.
Which has the bigger context window, Llama 3.2 1B Instruct or Qwen-Plus?
Qwen-Plus, with 1M tokens against 60K.
How many benchmarks is the Llama 3.2 1B Instruct vs Qwen-Plus comparison based on?
24 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 Qwen-Plus's on 0. Where only one model has a result, the page says so instead of naming a winner.