Llama 3.2 1B Instruct vs Qwen3 14B
Qwen3 14B leads Llama 3.2 1B Instruct on the Artificial Analysis Intelligence Index, 6.7 to 4.8, at about the same output price ($0.23 vs $0.201 per million). Updated October 21, 2026; 14 benchmarks have a result for both.
Llama 3.2 1B Instruct or Qwen3 14B: 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 1.1x cheaper ($0.201 vs $0.23 per million) and a larger context window (60K against 41K tokens).
- Shared results
- 14
- Llama 3.2 1B Instruct only
- 24
- Qwen3 14B only
- 6
- 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
Qwen3 14B
Qwen3 14B leads, 6.7 against 4.8.
Same board, both measured
Long documents
Listed context window
Llama 3.2 1B Instruct
Llama 3.2 1B Instruct takes 60K tokens against 41K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Llama 3.2 1B Instruct
Llama 3.2 1B Instruct $0.0001, Qwen3 14B $0.0002 per turn.
List prices
At a glance
- Coding index
- Llama 3.2 1B Instruct not benchmarked, Qwen3 14B not benchmarked.
- Widest category
- Math, 60.6 points apart on average over 3 shared benchmarks.
- Widest single gap
- OTIS Mock AIME: Llama 3.2 1B Instruct 0.6% against Qwen3 14B 66.4%, 65.8 points apart on a 0 to 100 scale.
- Output price
- Llama 3.2 1B Instruct $0.201 against Qwen3 14B $0.23 per million output tokens, 1.1x apart.
- Context window
- Llama 3.2 1B Instruct takes 60K tokens, Qwen3 14B 41K.
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 | Qwen3 14B | Ties |
|---|---|---|---|---|
| Coding | 0 | 2 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 2 | ||
| Reasoning | 1 | 2 | ||
| Math | 0 | 3 | ||
| Knowledge | 0 | 1 | ||
| General | 1 | 1 | ||
| All shared boards | 2 | 12 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
| Benchmark | Llama 3.2 1B Instruct | Qwen3 14B | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | 1.9% | 52.3% | Qwen3 14B |
| SciCode | Not measured | 30.7% | Only Qwen3 14B measured |
| Terminal-Bench Hard (Artificial Analysis run) | 0.0% | 5.3% | Qwen3 14B |
| LMArena Text: coding | 1070 | Not measured | Only Llama 3.2 1B Instruct measured |
| LMArena Text: software and IT | 1056 | Not measured | Only Llama 3.2 1B 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 1B Instruct | Qwen3 14B | Leader |
|---|---|---|---|
| AA Intelligence Index | 4.8 | 6.7 | Qwen3 14B |
| Epoch Capabilities Index | Not measured | 138.2 | Only Qwen3 14B measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Llama 3.2 1B Instruct | Qwen3 14B | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 0.0% | 34.5% | Qwen3 14B |
| BALROG | 6.6% | Not measured | Only Llama 3.2 1B Instruct measured |
| Berkeley Function Calling Leaderboard (BFCL) | 10.8% | 41.0% | Qwen3 14B |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index12
| Benchmark | Llama 3.2 1B Instruct | Qwen3 14B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 19.6% | 60.4% | Qwen3 14B |
| Humanity's Last Exam (Artificial Analysis run) | 5.5% | 4.5% | Llama 3.2 1B Instruct |
| GPQA Diamond (Epoch AI run) | 23.9% | 63.8% | Qwen3 14B |
| LMArena Text: hard prompts | 1044 | Not measured | Only Llama 3.2 1B Instruct measured |
| LMArena Text: expert | 1007 | Not measured | Only Llama 3.2 1B Instruct measured |
| LMArena Text: hard prompts in English | 1087 | Not measured | Only Llama 3.2 1B Instruct measured |
| Kagi LLM Benchmark | Not measured | 49.1% | Only Qwen3 14B measured |
| 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 | Qwen3 14B | Leader |
|---|---|---|---|
| AA Math Index | 0.0 | 58.0 | Qwen3 14B |
| AIME 2025 (Artificial Analysis run) | 0.0% | 58.0% | Qwen3 14B |
| OTIS Mock AIME | 0.6% | 66.4% | Qwen3 14B |
| LMArena Text: math | 1086 | Not measured | Only Llama 3.2 1B Instruct measured |
| LMArena Text: mathematical work | 1098 | Not measured | Only Llama 3.2 1B Instruct measured |
| Neither model has a result on the other 8 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Llama 3.2 1B Instruct | Qwen3 14B | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 20.0% | 77.4% | Qwen3 14B |
| LMArena Text: science | 1055 | Not measured | Only Llama 3.2 1B Instruct measured |
| LMArena Text: medicine and healthcare | 1009 | Not measured | Only Llama 3.2 1B Instruct measured |
| LMArena Text: legal and government | 1071 | Not measured | Only Llama 3.2 1B Instruct measured |
| LMArena Text: business and finance | 1000 | Not measured | Only Llama 3.2 1B Instruct measured |
| Neither model has a result on the other 13 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Llama 3.2 1B Instruct | Qwen3 14B | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | Llama 3.2 1B Instruct | Qwen3 14B | Leader |
|---|---|---|---|
| IFBench | 22.8% | 40.5% | Qwen3 14B |
| Long context reasoning (AA-LCR) | 6.7% | 0.0% | Llama 3.2 1B Instruct |
| DTBench | Not measured | 64.0% | Only Qwen3 14B measured |
| LMCA | Not measured | 18.2% | Only Qwen3 14B measured |
| Fiction.LiveBench | Not measured | 62.5% | Only Qwen3 14B measured |
| LMArena Text | 1055 | Not measured | Only Llama 3.2 1B Instruct measured |
| LMArena Text: instruction following | 1031 | Not measured | Only Llama 3.2 1B Instruct measured |
| LMArena Text: creative writing | 1033 | Not measured | Only Llama 3.2 1B Instruct measured |
| LMArena Text: English | 1117 | Not measured | Only Llama 3.2 1B Instruct measured |
| LMArena Text: non-English | 973 | Not measured | Only Llama 3.2 1B Instruct measured |
| LMArena Text: Chinese | 959 | Not measured | Only Llama 3.2 1B Instruct measured |
| LMArena Text: German | 1014 | Not measured | Only Llama 3.2 1B Instruct measured |
| LMArena Text: Russian | 941 | Not measured | Only Llama 3.2 1B Instruct measured |
| LMArena Text: multi-turn | 1030 | Not measured | Only Llama 3.2 1B Instruct measured |
| LMArena Text: longer queries | 1050 | Not measured | Only Llama 3.2 1B Instruct measured |
| LMArena Text: writing and language | 1030 | Not measured | Only Llama 3.2 1B Instruct measured |
| LMArena Text: entertainment and media | 1025 | Not measured | Only Llama 3.2 1B Instruct measured |
| 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 | Qwen3 14B | 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 | Qwen3 14B |
|---|---|---|
| Maker | Meta Llama | Qwen |
| Weights | Open weights | Open weights |
| Released | September 25, 2024 | April 28, 2025 |
| Context window | 60K | 41K |
| Input $/Mtok | $0.027 | $0.11 |
| Output $/Mtok | $0.201 | $0.23 |
| 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 Qwen3 14B 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 1.1x cheaper ($0.201 vs $0.23 per million) and a larger context window (60K against 41K tokens).
Which is cheaper, Llama 3.2 1B Instruct or Qwen3 14B?
Llama 3.2 1B Instruct costs $0.027 per million input tokens and $0.201 per million output tokens; Qwen3 14B costs $0.11 and $0.23. Cost per solved task is known for neither of them.
Which has the bigger context window, Llama 3.2 1B Instruct or Qwen3 14B?
Llama 3.2 1B Instruct, with 60K tokens against 41K.
How many benchmarks is the Llama 3.2 1B Instruct vs Qwen3 14B comparison based on?
14 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 Qwen3 14B's on 0. Where only one model has a result, the page says so instead of naming a winner.