Model comparison
Llama 3.2 1B vs Qwen3.8 Max
Qwen3.8 Max is the stronger model overall, scoring 56.0 to 19.9 on the Noometry Index. Llama 3.2 1B costs 43× less per token, which makes it the better buy when Qwen3.8 Max's lead doesn't matter for your workload.
Last verified . 17 shared benchmarks.
Meta
Alibaba (Qwen)Summary
- They share 17 benchmarks with published results for both. Llama 3.2 1B scores higher in 0 categories and Qwen3.8 Max in 9 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where Qwen3.8 Max leads 73.1 to 9.7.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 0.6% for Llama 3.2 1B and 100% for Qwen3.8 Max.
- Llama 3.2 1B is cheaper at $0.027 / $0.20 per million input/output tokens, against $2 / $6 for Qwen3.8 Max.
- Qwen3.8 Max accepts more context: 1M tokens versus 60K.
- Llama 3.2 1B has downloadable open weights; the other is API-only.
Side by side
| Llama 3.2 1B | Qwen3.8 Max | |
|---|---|---|
| Provider | Meta | Alibaba (Qwen) |
| Noometry Index | 19.9 | 56.0 |
| Released | 2024-09-24 | 2026-08-02 |
| Weights | Open | Proprietary |
| Context window | 60K | 1M |
| Max output | 54K | 131K |
| Input $ / M tokens | $0.027 | $2 |
| Output $ / M tokens | $0.20 | $6 |
| Results tracked | 22 | 38 |
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Category by category
Coding Qwen3.8 Max leads
Llama 3.2 1B: 20.6 (#333), Qwen3.8 Max: 52.3 (#30)
| Benchmark | Llama 3.2 1B | Qwen3.8 Max |
|---|---|---|
| LMArena Coding | 1071 | 1499 |
| DeepSWE | — | 57.5% |
| LMArena WebDev | — | 1671 |
| FrontierSWE | — | 15.8% |
| SciCode | — | 52.9% |
| BigCodeBench Instruct | 8.2% | — |
| BigCodeBench Complete | 11.3% | — |
Agentic & Tool Use Qwen3.8 Max leads
Llama 3.2 1B: 14.5 (#142), Qwen3.8 Max: 42.6 (#17)
| Benchmark | Llama 3.2 1B | Qwen3.8 Max |
|---|---|---|
| Berkeley Function Calling Leaderboard | 10.8% | — |
| τ²-bench Banking | — | 55.1% |
| BALROG | 6.6% | — |
| GDP.pdf | — | 23.2% |
Reasoning Qwen3.8 Max leads
Llama 3.2 1B: 15.6 (#304), Qwen3.8 Max: 53.4 (#26)
| Benchmark | Llama 3.2 1B | Qwen3.8 Max |
|---|---|---|
| Chess Puzzles | 0% | 40% |
| LMArena Hard Prompts | 1044 | 1493 |
| Epoch Capabilities Index | 101.95 | 156.69 |
| NYT Connections (extended) | — | 88.3% |
| CritPt | — | 20% |
| Mystery Game Puzzles | — | 38% |
| DTBench | — | 92% |
| LMCA | — | 46.2% |
Math Qwen3.8 Max leads
Llama 3.2 1B: 9.7 (#308), Qwen3.8 Max: 73.1 (#15)
| Benchmark | Llama 3.2 1B | Qwen3.8 Max |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 0.6% | 100% |
| LMArena Math | 1086 | 1498 |
| FrontierMath (Tiers 1-3) | — | 74.7% |
| FrontierMath Tier 4 | — | 46.3% |
| ProofBench | — | 58% |
Knowledge Qwen3.8 Max leads
Llama 3.2 1B: 7.2 (#307), Qwen3.8 Max: 61.7 (#25)
| Benchmark | Llama 3.2 1B | Qwen3.8 Max |
|---|---|---|
| GPQA Diamond | 23.9% | 92.7% |
| LMArena Expert | 1007 | 1504 |
| SimpleQA Verified | — | 47.3% |
Multimodal Not comparable
Llama 3.2 1B: —, Qwen3.8 Max: 36.9 (#70)
| Benchmark | Llama 3.2 1B | Qwen3.8 Max |
|---|---|---|
| LMArena Vision | — | 1314 |
| Furniture Assembly | — | 20% |
Multilingual Qwen3.8 Max leads
Llama 3.2 1B: 23.9 (#287), Qwen3.8 Max: 56.6 (#18)
| Benchmark | Llama 3.2 1B | Qwen3.8 Max |
|---|---|---|
| LMArena Non-English | 973 | 1468 |
| LMArena Chinese | 958 | 1537 |
| LMArena German | 1014 | 1489 |
| LMArena Russian | 941 | 1478 |
| LMArena French | — | 1502 |
| LMArena Japanese | — | 1466 |
| LMArena Korean | — | 1467 |
| LMArena Spanish | — | 1495 |
Instruction Following Qwen3.8 Max leads
Llama 3.2 1B: 52.6 (#285), Qwen3.8 Max: 77.7 (#19)
| Benchmark | Llama 3.2 1B | Qwen3.8 Max |
|---|---|---|
| LMArena Instruction Following | 1031 | 1478 |
Long Context Qwen3.8 Max leads
Llama 3.2 1B: 31.9 (#269), Qwen3.8 Max: 45.5 (#30)
| Benchmark | Llama 3.2 1B | Qwen3.8 Max |
|---|---|---|
| LMArena Longer Query | 1050 | 1486 |
Writing & Preference Qwen3.8 Max leads
Llama 3.2 1B: 21.4 (#305), Qwen3.8 Max: 67.0 (#31)
| Benchmark | Llama 3.2 1B | Qwen3.8 Max |
|---|---|---|
| LMArena Text | 1055 | 1480 |
| LMArena Creative Writing | 1033 | 1478 |
| LMArena Multi-Turn | 1030 | 1487 |
| EQ-Bench Creative Writing | 200 | — |
Frequently asked questions
Is Llama 3.2 1B better than Qwen3.8 Max?
Qwen3.8 Max is the stronger model overall, scoring 56.0 to 19.9 on the Noometry Index. Llama 3.2 1B costs 43× less per token, which makes it the better buy when Qwen3.8 Max's lead doesn't matter for your workload.
Which is cheaper, Llama 3.2 1B or Qwen3.8 Max?
Llama 3.2 1B is cheaper. It lists at $0.027 per million input tokens and $0.20 per million output tokens; Qwen3.8 Max lists at $2 and $6.
Is Llama 3.2 1B or Qwen3.8 Max better for coding?
Qwen3.8 Max scores higher on coding benchmarks: 52.3 versus 20.6 in the Noometry coding category.
Which has the bigger context window?
Qwen3.8 Max does, with 1M tokens against 60K.
How many benchmarks do Llama 3.2 1B and Qwen3.8 Max share?
17 benchmarks have published results for both models. Llama 3.2 1B has 22 scored results on Noometry and Qwen3.8 Max has 38.