Model comparison
Llama 3.2 1B vs Qwen3.5-Flash
Qwen3.5-Flash is the stronger model overall, scoring 42.3 to 19.9 on the Noometry Index. Llama 3.2 1B costs 2.5× less per token, which makes it the better buy when Qwen3.5-Flash'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 Qwen3.5-Flash in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where Qwen3.5-Flash leads 58.2 to 21.4.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 0.6% for Llama 3.2 1B and 84.4% for Qwen3.5-Flash.
- Llama 3.2 1B is cheaper at $0.027 / $0.20 per million input/output tokens, against $0.10 / $0.40 for Qwen3.5-Flash.
- Qwen3.5-Flash 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 | Qwen3.5-Flash | |
|---|---|---|
| Provider | Meta | Alibaba (Qwen) |
| Noometry Index | 19.9 | 42.3 |
| Released | 2024-09-24 | 2026-02-23 |
| Weights | Open | Proprietary |
| Context window | 60K | 1M |
| Max output | 54K | 66K |
| Input $ / M tokens | $0.027 | $0.10 |
| Output $ / M tokens | $0.20 | $0.40 |
| Results tracked | 22 | 32 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding Qwen3.5-Flash leads
Llama 3.2 1B: 20.6 (#333), Qwen3.5-Flash: 33.9 (#234)
| Benchmark | Llama 3.2 1B | Qwen3.5-Flash |
|---|---|---|
| LMArena Coding | 1071 | 1412 |
| LMArena WebDev | — | 1243 |
| BigCodeBench Instruct | 8.2% | — |
| BigCodeBench Complete | 11.3% | — |
| ALE-Bench | — | 221.8 |
Agentic & Tool Use Not comparable
Llama 3.2 1B: 14.5 (#142), Qwen3.5-Flash: —
| Benchmark | Llama 3.2 1B | Qwen3.5-Flash |
|---|---|---|
| Berkeley Function Calling Leaderboard | 10.8% | — |
| BALROG | 6.6% | — |
| Vending-Bench 2 | — | 462.69 |
Reasoning Qwen3.5-Flash leads
Llama 3.2 1B: 15.6 (#304), Qwen3.5-Flash: 32.9 (#63)
| Benchmark | Llama 3.2 1B | Qwen3.5-Flash |
|---|---|---|
| Chess Puzzles | 0% | 21% |
| LMArena Hard Prompts | 1044 | 1404 |
| Epoch Capabilities Index | 101.95 | 143.98 |
| Mystery Game Puzzles | — | 20% |
| DTBench | — | 82.9% |
| LMCA | — | 29.1% |
Math Qwen3.5-Flash leads
Llama 3.2 1B: 9.7 (#308), Qwen3.5-Flash: 36.3 (#117)
| Benchmark | Llama 3.2 1B | Qwen3.5-Flash |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 0.6% | 84.4% |
| LMArena Math | 1086 | 1407 |
| FrontierMath (Tiers 1-3) | — | 18.2% |
| FrontierMath (Feb 2025 set) | — | 6.2% |
| FrontierMath Tier 4 (v1) | — | 0% |
Knowledge Qwen3.5-Flash leads
Llama 3.2 1B: 7.2 (#307), Qwen3.5-Flash: 43.2 (#84)
| Benchmark | Llama 3.2 1B | Qwen3.5-Flash |
|---|---|---|
| GPQA Diamond | 23.9% | 82.3% |
| LMArena Expert | 1007 | 1408 |
| SimpleQA Verified | — | 20.3% |
| Vectara Hallucination Rate | — | 10.5% |
Multilingual Qwen3.5-Flash leads
Llama 3.2 1B: 23.9 (#287), Qwen3.5-Flash: 50.7 (#116)
| Benchmark | Llama 3.2 1B | Qwen3.5-Flash |
|---|---|---|
| LMArena Non-English | 973 | 1385 |
| LMArena Chinese | 958 | 1446 |
| LMArena German | 1014 | 1389 |
| LMArena Russian | 941 | 1379 |
| LMArena French | — | 1412 |
| LMArena Japanese | — | 1369 |
| LMArena Korean | — | 1344 |
| LMArena Spanish | — | 1399 |
Instruction Following Qwen3.5-Flash leads
Llama 3.2 1B: 52.6 (#285), Qwen3.5-Flash: 72.8 (#133)
| Benchmark | Llama 3.2 1B | Qwen3.5-Flash |
|---|---|---|
| LMArena Instruction Following | 1031 | 1374 |
Long Context Qwen3.5-Flash leads
Llama 3.2 1B: 31.9 (#269), Qwen3.5-Flash: 42.4 (#119)
| Benchmark | Llama 3.2 1B | Qwen3.5-Flash |
|---|---|---|
| LMArena Longer Query | 1050 | 1392 |
Writing & Preference Qwen3.5-Flash leads
Llama 3.2 1B: 21.4 (#305), Qwen3.5-Flash: 58.2 (#117)
| Benchmark | Llama 3.2 1B | Qwen3.5-Flash |
|---|---|---|
| LMArena Text | 1055 | 1397 |
| LMArena Creative Writing | 1033 | 1343 |
| LMArena Multi-Turn | 1030 | 1393 |
| EQ-Bench Creative Writing | 200 | — |
Frequently asked questions
Is Llama 3.2 1B better than Qwen3.5-Flash?
Qwen3.5-Flash is the stronger model overall, scoring 42.3 to 19.9 on the Noometry Index. Llama 3.2 1B costs 2.5× less per token, which makes it the better buy when Qwen3.5-Flash's lead doesn't matter for your workload.
Which is cheaper, Llama 3.2 1B or Qwen3.5-Flash?
Llama 3.2 1B is cheaper. It lists at $0.027 per million input tokens and $0.20 per million output tokens; Qwen3.5-Flash lists at $0.10 and $0.40.
Is Llama 3.2 1B or Qwen3.5-Flash better for coding?
Qwen3.5-Flash scores higher on coding benchmarks: 33.9 versus 20.6 in the Noometry coding category.
Which has the bigger context window?
Qwen3.5-Flash does, with 1M tokens against 60K.
How many benchmarks do Llama 3.2 1B and Qwen3.5-Flash share?
17 benchmarks have published results for both models. Llama 3.2 1B has 22 scored results on Noometry and Qwen3.5-Flash has 32.