Model comparison
Llama 3.2 1B vs Qwen3-Coder 480B-A35B Instruct
Qwen3-Coder 480B-A35B Instruct is the stronger model overall, scoring 38.1 to 20.1 on the Noometry Index. Llama 3.2 1B costs 43× less per token, which makes it the better buy when Qwen3-Coder 480B-A35B Instruct's lead doesn't matter for your workload.
Last verified . 13 shared benchmarks.
Meta
Alibaba (Qwen)Summary
- They share 13 benchmarks with published results for both. Llama 3.2 1B scores higher in 0 categories and Qwen3-Coder 480B-A35B Instruct in 9 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where Qwen3-Coder 480B-A35B Instruct leads 55.3 to 21.3.
- Llama 3.2 1B is cheaper at $0.027 / $0.20 per million input/output tokens, against $1.50 / $7.50 for Qwen3-Coder 480B-A35B Instruct.
- Qwen3-Coder 480B-A35B Instruct accepts more context: 262K tokens versus 60K.
Side by side
| Llama 3.2 1B | Qwen3-Coder 480B-A35B Instruct | |
|---|---|---|
| Provider | Meta | Alibaba (Qwen) |
| Noometry Index | 20.1 | 38.1 |
| Released | 2024-09-24 | 2025-04 |
| Weights | Open | Open |
| Context window | 60K | 262K |
| Max output | 54K | 66K |
| Input $ / M tokens | $0.027 | $1.50 |
| Output $ / M tokens | $0.20 | $7.50 |
| Results tracked | 22 | 25 |
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Category by category
Coding Qwen3-Coder 480B-A35B Instruct leads
Llama 3.2 1B: 21.1 (#338), Qwen3-Coder 480B-A35B Instruct: 35.5 (#223)
| Benchmark | Llama 3.2 1B | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Coding | 1070 | 1412 |
| SWE-bench Verified (bash only) | — | 55.4% |
| LMArena WebDev | — | 1275 |
| GSO | — | 4.9% |
| WeirdML | — | 41.2% |
| BigCodeBench Instruct | 8.2% | — |
| BigCodeBench Complete | 11.3% | — |
| ALE-Bench | — | 461.45 |
| AlgoTune | — | 1.44 |
Agentic & Tool Use Qwen3-Coder 480B-A35B Instruct leads
Llama 3.2 1B: 14.6 (#150), Qwen3-Coder 480B-A35B Instruct: 23.9 (#123)
| Benchmark | Llama 3.2 1B | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| Terminal-Bench | — | 27.2% |
| Berkeley Function Calling Leaderboard | 10.8% | — |
| BALROG | 6.6% | — |
Reasoning Qwen3-Coder 480B-A35B Instruct leads
Llama 3.2 1B: 16.2 (#308), Qwen3-Coder 480B-A35B Instruct: 25.5 (#149)
| Benchmark | Llama 3.2 1B | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Hard Prompts | 1044 | 1372 |
| Kagi LLM Benchmark | — | 49.5% |
| Chess Puzzles | 0% | — |
| Epoch Capabilities Index | 101.99 | — |
Math Qwen3-Coder 480B-A35B Instruct leads
Llama 3.2 1B: 10.4 (#313), Qwen3-Coder 480B-A35B Instruct: 37.6 (#150)
| Benchmark | Llama 3.2 1B | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Math | 1086 | 1365 |
| OTIS Mock AIME 2024-2025 | 0.6% | — |
Knowledge Qwen3-Coder 480B-A35B Instruct leads
Llama 3.2 1B: 7.2 (#312), Qwen3-Coder 480B-A35B Instruct: 37.0 (#162)
| Benchmark | Llama 3.2 1B | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Expert | 1007 | 1338 |
| GPQA Diamond | 23.9% | — |
Multilingual Qwen3-Coder 480B-A35B Instruct leads
Llama 3.2 1B: 23.8 (#292), Qwen3-Coder 480B-A35B Instruct: 47.7 (#148)
| Benchmark | Llama 3.2 1B | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Non-English | 973 | 1346 |
| LMArena Chinese | 959 | 1357 |
| LMArena German | 1014 | 1325 |
| LMArena Russian | 941 | 1366 |
| LMArena French | — | 1398 |
| LMArena Japanese | — | 1310 |
| LMArena Korean | — | 1305 |
| LMArena Spanish | — | 1360 |
Instruction Following Qwen3-Coder 480B-A35B Instruct leads
Llama 3.2 1B: 52.4 (#290), Qwen3-Coder 480B-A35B Instruct: 71.6 (#147)
| Benchmark | Llama 3.2 1B | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Instruction Following | 1031 | 1355 |
Long Context Qwen3-Coder 480B-A35B Instruct leads
Llama 3.2 1B: 31.9 (#274), Qwen3-Coder 480B-A35B Instruct: 42.0 (#131)
| Benchmark | Llama 3.2 1B | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Longer Query | 1050 | 1378 |
Writing & Preference Qwen3-Coder 480B-A35B Instruct leads
Llama 3.2 1B: 21.3 (#310), Qwen3-Coder 480B-A35B Instruct: 55.3 (#147)
| Benchmark | Llama 3.2 1B | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Text | 1055 | 1357 |
| LMArena Creative Writing | 1033 | 1333 |
| LMArena Multi-Turn | 1030 | 1365 |
| EQ-Bench Creative Writing | 200 | — |
Frequently asked questions
Is Llama 3.2 1B better than Qwen3-Coder 480B-A35B Instruct?
Qwen3-Coder 480B-A35B Instruct is the stronger model overall, scoring 38.1 to 20.1 on the Noometry Index. Llama 3.2 1B costs 43× less per token, which makes it the better buy when Qwen3-Coder 480B-A35B Instruct's lead doesn't matter for your workload.
Which is cheaper, Llama 3.2 1B or Qwen3-Coder 480B-A35B Instruct?
Llama 3.2 1B is cheaper. It lists at $0.027 per million input tokens and $0.20 per million output tokens; Qwen3-Coder 480B-A35B Instruct lists at $1.50 and $7.50.
Is Llama 3.2 1B or Qwen3-Coder 480B-A35B Instruct better for coding?
Qwen3-Coder 480B-A35B Instruct scores higher on coding benchmarks: 35.5 versus 21.1 in the Noometry coding category.
Which has the bigger context window?
Qwen3-Coder 480B-A35B Instruct does, with 262K tokens against 60K.
How many benchmarks do Llama 3.2 1B and Qwen3-Coder 480B-A35B Instruct share?
13 benchmarks have published results for both models. Llama 3.2 1B has 22 scored results on Noometry and Qwen3-Coder 480B-A35B Instruct has 25.