Llama 3.2 3B Instruct vs Qwen3.6 Plus
Qwen3.6 Plus leads Llama 3.2 3B Instruct on the Artificial Analysis Intelligence Index, 27.0 to 5.7, and Llama 3.2 3B Instruct costs 5.9x less per output token ($0.3325 vs $1.95 per million). Updated October 21, 2026; 29 benchmarks have a result for both.
Llama 3.2 3B Instruct or Qwen3.6 Plus: which to pick
Only Qwen3.6 Plus has a coding score, 44.1 on the Vibe Coding Index; Llama 3.2 3B Instruct has none, so the two cannot be ranked on coding.
Pick Llama 3.2 3B Instruct for output tokens 5.9x cheaper ($0.3325 vs $1.95 per million).
- Shared results
- 29
- Llama 3.2 3B Instruct only
- 6
- Qwen3.6 Plus only
- 54
- 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
Only Qwen3.6 Plus has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Qwen3.6 Plus
Qwen3.6 Plus leads, 27.0 against 5.7.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only Qwen3.6 Plus has a GPQA Diamond result, 87.4%.
Directional only
Math
AIME
No call
Only Qwen3.6 Plus has a AIME result, 94.6%.
Directional only
Knowledge
MMLU Pro
No call
Only Qwen3.6 Plus has a MMLU Pro result, 87.7%.
Directional only
Web design
Design Arena: websites
No call
Only Qwen3.6 Plus has a Design Arena: websites result, 1243.
Directional only
Long documents
Listed context window
Qwen3.6 Plus
Qwen3.6 Plus takes 1M tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Llama 3.2 3B Instruct
Llama 3.2 3B Instruct $0.0002, Qwen3.6 Plus $0.0013 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Qwen3.6 Plus has the number, $1.19.
Directional only
At a glance
- Coding index
- Llama 3.2 3B Instruct not benchmarked, Qwen3.6 Plus 44.1 from 3 benchmarks.
- Widest category
- Agentic work, 76.6 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: Llama 3.2 3B Instruct 21.1% against Qwen3.6 Plus 97.7%, 76.6 points apart on a 0 to 100 scale.
- Output price
- Llama 3.2 3B Instruct $0.3325 against Qwen3.6 Plus $1.95 per million output tokens, 5.9x apart.
- Context window
- Llama 3.2 3B Instruct takes 131K tokens, Qwen3.6 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 3B Instruct | Bars | Qwen3.6 Plus | Ties |
|---|---|---|---|---|
| Coding | 0 | 2 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 5 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 4 | ||
| General | 0 | 14 | ||
| All shared boards | 0 | 29 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
| Benchmark | Llama 3.2 3B Instruct | Qwen3.6 Plus | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1461 | Only Qwen3.6 Plus measured |
| AA Coding Index | Not measured | 54.5 | Only Qwen3.6 Plus measured |
| LiveCodeBench (Vals AI) | Not measured | 86.0% | Only Qwen3.6 Plus measured |
| SWE-bench Verified (Vals AI) | Not measured | 73.4% | Only Qwen3.6 Plus measured |
| Vibe Code Bench (Vals AI) | Not measured | 25.6% | Only Qwen3.6 Plus measured |
| Code migration (Vals AI) | Not measured | 11.1% | Only Qwen3.6 Plus measured |
| ProgramBench (Vals AI) | Not measured | 0.0% | Only Qwen3.6 Plus measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 53.2% | Only Qwen3.6 Plus measured |
| LiveCodeBench (Artificial Analysis run) | 8.3% | Not measured | Only Llama 3.2 3B Instruct measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 43.9% | Only Qwen3.6 Plus measured |
| ALE-Bench | Not measured | 670.1 | Only Qwen3.6 Plus measured |
| LMArena Text: coding | 1098 | 1467 | Qwen3.6 Plus |
| LMArena Text: software and IT | 1103 | 1462 | Qwen3.6 Plus |
| LMArena WebDev: React | Not measured | 1454 | Only Qwen3.6 Plus measured |
| LMArena WebDev: HTML | Not measured | 1463 | Only Qwen3.6 Plus measured |
| LMArena WebDev: from an image | Not measured | 1472 | Only Qwen3.6 Plus measured |
| Neither model has a result on the other 23 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Llama 3.2 3B Instruct | Qwen3.6 Plus | Leader |
|---|---|---|---|
| AA Intelligence Index | 5.7 | 27.0 | Qwen3.6 Plus |
| Epoch Capabilities Index | Not measured | 147.3 | Only Qwen3.6 Plus measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Llama 3.2 3B Instruct | Qwen3.6 Plus | Leader |
|---|---|---|---|
| Finance Agent (Vals AI) | Not measured | 40.9% | Only Qwen3.6 Plus measured |
| Tax Agent Bench (Vals AI) | Not measured | 7.7% | Only Qwen3.6 Plus measured |
| Legal Research Bench (Vals AI) | Not measured | 14.9% | Only Qwen3.6 Plus measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 32.9% | Only Qwen3.6 Plus measured |
| τ²-Bench Telecom | 21.1% | 97.7% | Qwen3.6 Plus |
| BALROG | 10.1% | Not measured | Only Llama 3.2 3B Instruct measured |
| Vending-Bench 2 | Not measured | $5,115 | Only Qwen3.6 Plus measured |
| Berkeley Function Calling Leaderboard (BFCL) | 21.9% | Not measured | Only Llama 3.2 3B Instruct measured |
| Neither model has a result on the other 33 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | Llama 3.2 3B Instruct | Qwen3.6 Plus | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 87.4% | Only Qwen3.6 Plus measured |
| MMMU Pro (Vals AI) | Not measured | 84.2% | Only Qwen3.6 Plus measured |
| GPQA Diamond (Artificial Analysis run) | 25.5% | 88.2% | Qwen3.6 Plus |
| Humanity's Last Exam (Artificial Analysis run) | 5.3% | 27.8% | Qwen3.6 Plus |
| GPQA Diamond (Epoch AI run) | Not measured | 88.4% | Only Qwen3.6 Plus measured |
| Chess puzzles | Not measured | 14.0% | Only Qwen3.6 Plus measured |
| Mystery game puzzles | Not measured | 12.0% | Only Qwen3.6 Plus measured |
| LMArena Text: hard prompts | 1095 | 1448 | Qwen3.6 Plus |
| LMArena Text: expert | 1090 | 1454 | Qwen3.6 Plus |
| LMArena Text: hard prompts in English | 1141 | 1455 | Qwen3.6 Plus |
| Neither model has a result on the other 12 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Llama 3.2 3B Instruct | Qwen3.6 Plus | Leader |
|---|---|---|---|
| AIME | Not measured | 94.6% | Only Qwen3.6 Plus measured |
| AA Math Index | 3.3 | Not measured | Only Llama 3.2 3B Instruct measured |
| AIME 2025 (Artificial Analysis run) | 3.3% | Not measured | Only Llama 3.2 3B Instruct measured |
| OTIS Mock AIME | Not measured | 93.3% | Only Qwen3.6 Plus measured |
| FrontierMath, tiers 1 to 3 | Not measured | 38.2% | Only Qwen3.6 Plus measured |
| LMArena Text: math | 1126 | 1449 | Qwen3.6 Plus |
| LMArena Text: mathematical work | 1128 | 1448 | Qwen3.6 Plus |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | Llama 3.2 3B Instruct | Qwen3.6 Plus | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 87.7% | Only Qwen3.6 Plus measured |
| LegalBench (Vals AI) | Not measured | 84.2% | Only Qwen3.6 Plus measured |
| CaseLaw (Vals AI) | Not measured | 51.5% | Only Qwen3.6 Plus measured |
| CorpFin (Vals AI) | Not measured | 61.9% | Only Qwen3.6 Plus measured |
| TaxEval (Vals AI) | Not measured | 74.7% | Only Qwen3.6 Plus measured |
| MortgageTax (Vals AI) | Not measured | 68.0% | Only Qwen3.6 Plus measured |
| MedCode (Vals AI) | Not measured | 36.9% | Only Qwen3.6 Plus measured |
| MMLU Pro (Artificial Analysis run) | 34.7% | Not measured | Only Llama 3.2 3B Instruct measured |
| SimpleQA Verified | Not measured | 44.1% | Only Qwen3.6 Plus measured |
| LMArena Text: science | 1106 | 1439 | Qwen3.6 Plus |
| LMArena Text: medicine and healthcare | 1104 | 1442 | Qwen3.6 Plus |
| LMArena Text: legal and government | 1115 | 1437 | Qwen3.6 Plus |
| LMArena Text: business and finance | 1074 | 1430 | Qwen3.6 Plus |
| Neither model has a result on the other 5 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | Llama 3.2 3B Instruct | Qwen3.6 Plus | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1243 | Only Qwen3.6 Plus measured |
| Design Arena: UI components | Not measured | 1233 | Only Qwen3.6 Plus measured |
| Design Arena: game dev | Not measured | 1211 | Only Qwen3.6 Plus measured |
| Design Arena: data viz | Not measured | 1228 | Only Qwen3.6 Plus measured |
| Design Arena: 3D | Not measured | 1202 | Only Qwen3.6 Plus measured |
| Design Arena: all code categories | Not measured | 1236 | Only Qwen3.6 Plus measured |
| Design Arena: SVG | Not measured | 1161 | Only Qwen3.6 Plus measured |
| Design Arena: ASCII art | Not measured | 1113 | Only Qwen3.6 Plus measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index014
| Benchmark | Llama 3.2 3B Instruct | Qwen3.6 Plus | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 44.9% | Only Qwen3.6 Plus measured |
| MedScribe (Vals AI) | Not measured | 77.0% | Only Qwen3.6 Plus measured |
| Vals Multimodal Index | Not measured | 51.5% | Only Qwen3.6 Plus measured |
| IFBench | 26.2% | 75.2% | Qwen3.6 Plus |
| Long context reasoning (AA-LCR) | 4.3% | 78.3% | Qwen3.6 Plus |
| DTBench | Not measured | 81.9% | Only Qwen3.6 Plus measured |
| LMCA | Not measured | 33.1% | Only Qwen3.6 Plus measured |
| CL-bench | Not measured | 20.3% | Only Qwen3.6 Plus measured |
| LMArena Text | 1110 | 1436 | Qwen3.6 Plus |
| LMArena Text: instruction following | 1089 | 1424 | Qwen3.6 Plus |
| LMArena Text: creative writing | 1094 | 1404 | Qwen3.6 Plus |
| LMArena Text: English | 1180 | 1445 | Qwen3.6 Plus |
| LMArena Text: non-English | 1019 | 1424 | Qwen3.6 Plus |
| LMArena Text: Chinese | 1017 | 1477 | Qwen3.6 Plus |
| LMArena Text: German | 1056 | 1451 | Qwen3.6 Plus |
| LMArena Text: Japanese | Not measured | 1390 | Only Qwen3.6 Plus measured |
| LMArena Text: Korean | Not measured | 1379 | Only Qwen3.6 Plus measured |
| LMArena Text: French | Not measured | 1455 | Only Qwen3.6 Plus measured |
| LMArena Text: Spanish | Not measured | 1432 | Only Qwen3.6 Plus measured |
| LMArena Text: Russian | 949 | 1434 | Qwen3.6 Plus |
| LMArena Text: Polish | Not measured | 1433 | Only Qwen3.6 Plus measured |
| LMArena Text: multi-turn | 1105 | 1438 | Qwen3.6 Plus |
| LMArena Text: longer queries | 1100 | 1439 | Qwen3.6 Plus |
| LMArena Text: writing and language | 1096 | 1418 | Qwen3.6 Plus |
| LMArena Text: entertainment and media | 1083 | 1402 | Qwen3.6 Plus |
| Neither model has a result on the other 21 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Llama 3.2 3B Instruct | Qwen3.6 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 3B Instruct costs less
Repository review
50K input + 3K output tokens
Llama 3.2 3B Instruct costs less
Agent loop
200K input + 10K output tokens
Llama 3.2 3B 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 3B Instruct | Qwen3.6 Plus |
|---|---|---|
| Maker | Meta Llama | Qwen |
| Weights | Open weights | No public weights listed |
| Released | September 25, 2024 | April 2, 2026 |
| Context window | 131K | 1M |
| Input $/Mtok | $0.0505 | $0.325 |
| Output $/Mtok | $0.3325 | $1.95 |
| Coding index | Not benchmarked | 44.1 |
| Independent measurements behind it | 0 | 3 |
| Cost per solved task | Not computable | $1.19 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Llama 3.2 3B Instruct better than Qwen3.6 Plus for coding?
Only Qwen3.6 Plus has a coding score, 44.1 on the Vibe Coding Index; Llama 3.2 3B Instruct has none, so the two cannot be ranked on coding. Pick Llama 3.2 3B Instruct for output tokens 5.9x cheaper ($0.3325 vs $1.95 per million).
Which is cheaper, Llama 3.2 3B Instruct or Qwen3.6 Plus?
Llama 3.2 3B Instruct costs $0.0505 per million input tokens and $0.3325 per million output tokens; Qwen3.6 Plus costs $0.325 and $1.95. Cost per solved task is known for Qwen3.6 Plus only.
Which has the bigger context window, Llama 3.2 3B Instruct or Qwen3.6 Plus?
Qwen3.6 Plus, with 1M tokens against 131K.
How many benchmarks is the Llama 3.2 3B Instruct vs Qwen3.6 Plus comparison based on?
29 benchmarks have a result for both models as of October 21, 2026. Llama 3.2 3B Instruct's coding index rests on 0 independent benchmarks and Qwen3.6 Plus's on 3. Where only one model has a result, the page says so instead of naming a winner.