Llama 3.2 1B Instruct vs Qwen2.5 Coder 32B Instruct
Updated October 21, 2026. 27 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 5.0x less, and where the context window is larger, 60K against 33K tokens.
- Shared results
- 27
- Llama 3.2 1B Instruct only
- 10
- Qwen2.5 Coder 32B Instruct only
- 8
- Like-for-like categories
- 6 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
Qwen2.5 Coder 32B Instruct
Qwen2.5 Coder 32B Instruct 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 33K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Llama 3.2 1B Instruct
Llama 3.2 1B Instruct $0.0001, Qwen2.5 Coder 32B Instruct $0.0012 per turn.
List prices
At a glance
- Coding index
- Llama 3.2 1B Instruct not benchmarked, Qwen2.5 Coder 32B Instruct not benchmarked.
- Widest category
- Knowledge, 33.8 points apart on average over 5 shared benchmarks.
- Widest single gap
- MMLU Pro (Artificial Analysis run): Llama 3.2 1B Instruct 20.0% against Qwen2.5 Coder 32B Instruct 63.5%, 43.5 points apart on a 0 to 100 scale.
- Output price
- Llama 3.2 1B Instruct $0.201 against Qwen2.5 Coder 32B Instruct $1.00 per million output tokens, 5.0x apart.
- Context window
- Llama 3.2 1B Instruct takes 60K tokens, Qwen2.5 Coder 32B Instruct 33K.
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 | Qwen2.5 Coder 32B Instruct | Ties |
|---|---|---|---|---|
| Coding | 0 | 3 | ||
| Intelligence | 0 | 1 | ||
| Reasoning | 1 | 4 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 5 | ||
| General | 0 | 11 | ||
| All shared boards | 1 | 26 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
| Benchmark | Llama 3.2 1B Instruct | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 16.4% | Only Qwen2.5 Coder 32B Instruct measured |
| SWE-bench Verified | Not measured | 47.0% | Only Qwen2.5 Coder 32B Instruct measured |
| LiveCodeBench (Artificial Analysis run) | 1.9% | 29.5% | Qwen2.5 Coder 32B Instruct |
| Terminal-Bench Hard (Artificial Analysis run) | 0.0% | Not measured | Only Llama 3.2 1B Instruct measured |
| LMArena Text: coding | 1070 | 1276 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: software and IT | 1056 | 1261 | Qwen2.5 Coder 32B Instruct |
| Neither model has a result on the other 32 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Llama 3.2 1B Instruct | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 4.8 | 6.7 | Qwen2.5 Coder 32B Instruct |
| Epoch Capabilities Index | Not measured | 119.5 | Only Qwen2.5 Coder 32B Instruct measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Llama 3.2 1B Instruct | Qwen2.5 Coder 32B Instruct | 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 index14
| Benchmark | Llama 3.2 1B Instruct | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 19.6% | 41.7% | Qwen2.5 Coder 32B Instruct |
| Humanity's Last Exam (Artificial Analysis run) | 5.5% | 3.5% | Llama 3.2 1B Instruct |
| GPQA Diamond (Epoch AI run) | 23.9% | Not measured | Only Llama 3.2 1B Instruct measured |
| LMArena Text: hard prompts | 1044 | 1251 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: expert | 1007 | 1221 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: hard prompts in English | 1087 | 1269 | Qwen2.5 Coder 32B Instruct |
| Neither model has a result on the other 15 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Llama 3.2 1B Instruct | Qwen2.5 Coder 32B Instruct | 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% | Not measured | Only Llama 3.2 1B Instruct measured |
| GSM8K | Not measured | 93.0% | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: math | 1086 | 1251 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: mathematical work | 1098 | 1243 | Qwen2.5 Coder 32B 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 | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 20.0% | 63.5% | Qwen2.5 Coder 32B Instruct |
| MMLU | Not measured | 79.1% | Only Qwen2.5 Coder 32B Instruct measured |
| AI2 Reasoning Challenge | Not measured | 70.5% | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: science | 1055 | 1221 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: medicine and healthcare | 1009 | 1187 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: legal and government | 1071 | 1257 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: business and finance | 1000 | 1224 | Qwen2.5 Coder 32B Instruct |
| Neither model has a result on the other 10 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Llama 3.2 1B Instruct | Qwen2.5 Coder 32B Instruct | 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 | Qwen2.5 Coder 32B Instruct | 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 |
| HellaSwag | Not measured | 83.0% | Only Qwen2.5 Coder 32B Instruct measured |
| WinoGrande | Not measured | 80.8% | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text | 1055 | 1230 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: instruction following | 1031 | 1223 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: creative writing | 1033 | 1174 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: English | 1117 | 1248 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: non-English | 973 | 1205 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: Chinese | 959 | 1222 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: German | 1014 | Not measured | Only Llama 3.2 1B Instruct measured |
| LMArena Text: Russian | 941 | 1228 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: multi-turn | 1030 | 1222 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: longer queries | 1050 | 1251 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: writing and language | 1030 | 1191 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: entertainment and media | 1025 | 1150 | Qwen2.5 Coder 32B Instruct |
| Neither model has a result on the other 30 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Llama 3.2 1B Instruct | Qwen2.5 Coder 32B 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 | Qwen2.5 Coder 32B Instruct |
|---|---|---|
| Maker | Meta Llama | Qwen |
| Weights | Open weights | Open weights |
| Released | September 25, 2024 | November 11, 2024 |
| Context window | 60K | 33K |
| Input $/Mtok | $0.027 | $0.66 |
| Output $/Mtok | $0.201 | $1.00 |
| 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 Qwen2.5 Coder 32B Instruct 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 5.0x less, and where the context window is larger, 60K against 33K tokens.
Which is cheaper, Llama 3.2 1B Instruct or Qwen2.5 Coder 32B Instruct?
Llama 3.2 1B Instruct costs $0.027 per million input tokens and $0.201 per million output tokens; Qwen2.5 Coder 32B Instruct costs $0.66 and $1.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Llama 3.2 1B Instruct or Qwen2.5 Coder 32B Instruct?
Llama 3.2 1B Instruct, with 60K tokens against 33K.
How many benchmarks is the Llama 3.2 1B Instruct vs Qwen2.5 Coder 32B Instruct comparison based on?
27 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 Qwen2.5 Coder 32B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.