Llama 3.2 3B Instruct vs Qwen3 Coder Next
Qwen3 Coder Next leads Llama 3.2 3B Instruct on the Artificial Analysis Intelligence Index, 9.2 to 5.7, and Llama 3.2 3B Instruct costs 2.4x less per output token ($0.3325 vs $0.80 per million). Updated October 21, 2026; 6 benchmarks have a result for both.
Llama 3.2 3B Instruct or Qwen3 Coder Next: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Llama 3.2 3B Instruct for output tokens 2.4x cheaper ($0.3325 vs $0.80 per million) and work measured by AA Intelligence Index or τ²-Bench Telecom, where it leads.
- Shared results
- 6
- Llama 3.2 3B Instruct only
- 28
- Qwen3 Coder Next only
- 5
- Like-for-like categories
- 4 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 Coder Next
Qwen3 Coder Next leads, 9.2 against 5.7.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Qwen3 Coder Next has a AA Agentic Index result, 0.9.
Directional only
Long documents
Listed context window
Qwen3 Coder Next
Qwen3 Coder Next takes 262K 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 Coder Next $0.0005 per turn.
List prices
At a glance
- Coding index
- Llama 3.2 3B Instruct not benchmarked, Qwen3 Coder Next not benchmarked.
- Widest category
- Agentic work, 58.5 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: Llama 3.2 3B Instruct 21.1% against Qwen3 Coder Next 79.5%, 58.5 points apart on a 0 to 100 scale.
- Output price
- Llama 3.2 3B Instruct $0.3325 against Qwen3 Coder Next $0.80 per million output tokens, 2.4x apart.
- Context window
- Llama 3.2 3B Instruct takes 131K tokens, Qwen3 Coder Next 262K.
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 Coder Next | Ties |
|---|---|---|---|---|
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 2 | ||
| General | 0 | 2 | ||
| All shared boards | 0 | 6 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index7 boards, none shared
| Benchmark | Llama 3.2 3B Instruct | Qwen3 Coder Next | Leader |
|---|---|---|---|
| AA Coding Index | Not measured | 36.2 | Only Qwen3 Coder Next measured |
| LiveCodeBench (Artificial Analysis run) | 8.3% | Not measured | Only Llama 3.2 3B Instruct measured |
| SciCode | Not measured | 36.2% | Only Qwen3 Coder Next measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 18.2% | Only Qwen3 Coder Next measured |
| WeirdML | Not measured | 34.4% | Only Qwen3 Coder Next measured |
| LMArena Text: coding | 1098 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: software and IT | 1103 | Not measured | Only Llama 3.2 3B Instruct measured |
| Neither model has a result on the other 31 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Llama 3.2 3B Instruct | Qwen3 Coder Next | Leader |
|---|---|---|---|
| AA Intelligence Index | 5.7 | 9.2 | Qwen3 Coder Next |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Llama 3.2 3B Instruct | Qwen3 Coder Next | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 0.9 | Only Qwen3 Coder Next measured |
| τ²-Bench Telecom | 21.1% | 79.5% | Qwen3 Coder Next |
| BALROG | 10.1% | Not measured | Only Llama 3.2 3B Instruct measured |
| Neither model has a result on the other 36 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Llama 3.2 3B Instruct | Qwen3 Coder Next | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 25.5% | 73.7% | Qwen3 Coder Next |
| Humanity's Last Exam (Artificial Analysis run) | 5.3% | 10.1% | Qwen3 Coder Next |
| LMArena Text: hard prompts | 1095 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: expert | 1090 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: hard prompts in English | 1141 | Not measured | Only Llama 3.2 3B Instruct measured |
| Neither model has a result on the other 16 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | Llama 3.2 3B Instruct | Qwen3 Coder Next | Leader |
|---|---|---|---|
| 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 |
| LMArena Text: math | 1126 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: mathematical work | 1128 | Not measured | Only Llama 3.2 3B Instruct measured |
| Neither model has a result on the other 9 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | Llama 3.2 3B Instruct | Qwen3 Coder Next | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 34.7% | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: science | 1106 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: medicine and healthcare | 1104 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: legal and government | 1115 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: business and finance | 1074 | Not measured | Only Llama 3.2 3B Instruct measured |
| 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 3B Instruct | Qwen3 Coder Next | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Llama 3.2 3B Instruct | Qwen3 Coder Next | Leader |
|---|---|---|---|
| IFBench | 26.2% | 35.2% | Qwen3 Coder Next |
| Long context reasoning (AA-LCR) | 4.3% | 47.0% | Qwen3 Coder Next |
| LMArena Text | 1110 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: instruction following | 1089 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: creative writing | 1094 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: English | 1180 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: non-English | 1019 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: Chinese | 1017 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: German | 1056 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: Russian | 949 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: multi-turn | 1105 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: longer queries | 1100 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: writing and language | 1096 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: entertainment and media | 1083 | Not measured | Only Llama 3.2 3B Instruct measured |
| Neither model has a result on the other 32 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Llama 3.2 3B Instruct | Qwen3 Coder Next | 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 Coder Next |
|---|---|---|
| Maker | Meta Llama | Qwen |
| Weights | Open weights | Open weights |
| Released | September 25, 2024 | February 4, 2026 |
| Context window | 131K | 262K |
| Input $/Mtok | $0.0505 | $0.12 |
| Output $/Mtok | $0.3325 | $0.80 |
| 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 3B Instruct better than Qwen3 Coder Next for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Llama 3.2 3B Instruct for output tokens 2.4x cheaper ($0.3325 vs $0.80 per million) and work measured by AA Intelligence Index or τ²-Bench Telecom, where it leads.
Which is cheaper, Llama 3.2 3B Instruct or Qwen3 Coder Next?
Llama 3.2 3B Instruct costs $0.0505 per million input tokens and $0.3325 per million output tokens; Qwen3 Coder Next costs $0.12 and $0.80. Cost per solved task is known for neither of them.
Which has the bigger context window, Llama 3.2 3B Instruct or Qwen3 Coder Next?
Qwen3 Coder Next, with 262K tokens against 131K.
How many benchmarks is the Llama 3.2 3B Instruct vs Qwen3 Coder Next comparison based on?
6 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 Coder Next's on 0. Where only one model has a result, the page says so instead of naming a winner.