# Llama-3.3-70B-Instruct vs Qwen3 32B

> Qwen3 32B is the stronger model overall, scoring 39.2 to 30.6 on the Noometry Index. Llama-3.3-70B-Instruct costs 7.9× less per token, which makes it the better buy when Qwen3 32B's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/llama-3-3-70b-instruct-vs-qwen3-32b
- Last updated: 2026-10-12
- Shared benchmarks: 23

## Summary

- They share 23 benchmarks with published results for both. Llama-3.3-70B-Instruct scores higher in 1 category and Qwen3 32B in 8 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where Qwen3 32B leads 39.7 to 15.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 5.1% for Llama-3.3-70B-Instruct and 66.9% for Qwen3 32B.
- Llama-3.3-70B-Instruct is cheaper at $0.10 / $0.32 per million input/output tokens, against $0.70 / $2.80 for Qwen3 32B.
- Qwen3 32B accepts more context: 131K tokens versus 128K.

## Snapshot

| | Llama-3.3-70B-Instruct | Qwen3 32B |
|---|---|---|
| Provider | Meta | Alibaba (Qwen) |
| Noometry Index | 30.6 | 39.2 |
| Rank | 291 | 172 |
| Context | 128K | 131K |
| Input $/M | $0.10 | $0.70 |
| Output $/M | $0.32 | $2.80 |
| Weights | Open | Open |

## Coding

- Llama-3.3-70B-Instruct: 31.0 (#290)
- Qwen3 32B: 37.7 (#190)

| Benchmark | Llama-3.3-70B-Instruct | Qwen3 32B |
|---|---|---|
| SciCode | 26% | 35.4% |
| LMArena Coding | 1268 | 1358 |
| Aider Polyglot | — | 40% |
| WeirdML | 14.4% | — |
| BigCodeBench Instruct | 46.9% | — |
| LiveBench Coding | 36.6% | — |
| BigCodeBench Complete | 57.5% | — |

## Agentic & Tool Use

- Llama-3.3-70B-Instruct: 25.8 (#105)
- Qwen3 32B: 32.6 (#62)

| Benchmark | Llama-3.3-70B-Instruct | Qwen3 32B |
|---|---|---|
| Berkeley Function Calling Leaderboard | 31.9% | 48.7% |
| BALROG | 23% | — |

## Reasoning

- Llama-3.3-70B-Instruct: 14.1 (#327)
- Qwen3 32B: 20.2 (#241)

| Benchmark | Llama-3.3-70B-Instruct | Qwen3 32B |
|---|---|---|
| CritPt | 0% | 0.3% |
| LMArena Hard Prompts | 1257 | 1334 |
| DTBench | 59.5% | 67.5% |
| LMCA | 17.5% | 17.3% |
| Epoch Capabilities Index | 127.33 | 138.51 |
| SimpleBench | 19.9% | — |
| Kagi LLM Benchmark | — | 54.9% |
| Chess Puzzles | — | 5% |
| LiveBench Reasoning | 50.8% | — |
| LiveBench Data Analysis | 49.5% | — |
| ForecastBench | 58.6 | — |
| LiveBench | 50.2% | — |

## Math

- Llama-3.3-70B-Instruct: 15.3 (#298)
- Qwen3 32B: 39.7 (#99)

| Benchmark | Llama-3.3-70B-Instruct | Qwen3 32B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 5.1% | 66.9% |
| LMArena Math | 1267 | 1399 |
| LiveBench Math | 42.2% | — |
| MATH Level 5 | 41.6% | — |

## Knowledge

- Llama-3.3-70B-Instruct: 30.6 (#226)
- Qwen3 32B: 40.0 (#125)

| Benchmark | Llama-3.3-70B-Instruct | Qwen3 32B |
|---|---|---|
| GPQA Diamond | 47.4% | 65.7% |
| Vectara Hallucination Rate | 4.1% | 5.9% |
| LMArena Expert | 1225 | 1362 |
| Confabulations | 22.8% | — |
| MMLU | 86.3% | — |

## Multilingual

- Llama-3.3-70B-Instruct: 39.9 (#220)
- Qwen3 32B: 45.6 (#167)

| Benchmark | Llama-3.3-70B-Instruct | Qwen3 32B |
|---|---|---|
| LMArena Non-English | 1236 | 1317 |
| LMArena Chinese | 1217 | 1357 |
| LMArena German | 1251 | 1341 |
| LMArena Russian | 1252 | 1311 |
| LMArena French | 1281 | — |
| LMArena Japanese | 1150 | — |
| LMArena Korean | 1143 | — |
| LMArena Spanish | 1270 | — |

## Instruction Following

- Llama-3.3-70B-Instruct: 71.1 (#157)
- Qwen3 32B: 68.9 (#179)

| Benchmark | Llama-3.3-70B-Instruct | Qwen3 32B |
|---|---|---|
| LMArena Instruction Following | 1242 | 1305 |
| LiveBench Instruction Following | 82.7% | — |

## Long Context

- Llama-3.3-70B-Instruct: 26.4 (#295)
- Qwen3 32B: 43.8 (#87)

| Benchmark | Llama-3.3-70B-Instruct | Qwen3 32B |
|---|---|---|
| Fiction.LiveBench | 33.3% | 74.2% |
| LMArena Longer Query | 1256 | 1327 |

## Writing & Preference

- Llama-3.3-70B-Instruct: 47.6 (#207)
- Qwen3 32B: 52.9 (#163)

| Benchmark | Llama-3.3-70B-Instruct | Qwen3 32B |
|---|---|---|
| LMArena Text | 1274 | 1340 |
| LMArena Creative Writing | 1250 | 1297 |
| LMArena Multi-Turn | 1280 | 1331 |
| LiveBench Language | 39.2% | — |

## FAQ

### Is Llama-3.3-70B-Instruct better than Qwen3 32B?

Qwen3 32B is the stronger model overall, scoring 39.2 to 30.6 on the Noometry Index. Llama-3.3-70B-Instruct costs 7.9× less per token, which makes it the better buy when Qwen3 32B's lead doesn't matter for your workload.

### Which is cheaper, Llama-3.3-70B-Instruct or Qwen3 32B?

Llama-3.3-70B-Instruct is cheaper. It lists at $0.10 per million input tokens and $0.32 per million output tokens; Qwen3 32B lists at $0.70 and $2.80.

### Is Llama-3.3-70B-Instruct or Qwen3 32B better for coding?

Qwen3 32B scores higher on coding benchmarks: 37.7 versus 31.0 in the Noometry coding category.

### Which has the bigger context window?

Qwen3 32B does, with 131K tokens against 128K.

### How many benchmarks do Llama-3.3-70B-Instruct and Qwen3 32B share?

23 benchmarks have published results for both models. Llama-3.3-70B-Instruct has 43 scored results on Noometry and Qwen3 32B has 26.