Model comparison
Llama-3.3-70B-Instruct vs Qwen2.5 72B Instruct
Qwen2.5 72B Instruct is the stronger model overall, scoring 31.5 to 30.3 on the Noometry Index. Llama-3.3-70B-Instruct costs 16× less per token, which makes it the better buy when Qwen2.5 72B Instruct's lead doesn't matter for your workload.
Last verified . 30 shared benchmarks.
Meta
Alibaba (Qwen)Summary
- They share 30 benchmarks with published results for both. Llama-3.3-70B-Instruct scores higher in 4 categories and Qwen2.5 72B Instruct in 5 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in long context, where Qwen2.5 72B Instruct leads 38.9 to 26.4.
- The biggest single-benchmark swing is MATH Level 5: 41.6% for Llama-3.3-70B-Instruct and 63.2% for Qwen2.5 72B Instruct.
- Llama-3.3-70B-Instruct is cheaper at $0.10 / $0.32 per million input/output tokens, against $1.40 / $5.60 for Qwen2.5 72B Instruct.
- Qwen2.5 72B Instruct accepts more context: 131K tokens versus 128K.
Side by side
| Llama-3.3-70B-Instruct | Qwen2.5 72B Instruct | |
|---|---|---|
| Provider | Meta | Alibaba (Qwen) |
| Noometry Index | 30.3 | 31.5 |
| Released | 2024-12-06 | 2024-09 |
| Weights | Open | Open |
| Context window | 128K | 131K |
| Max output | 4K | 8K |
| Input $ / M tokens | $0.10 | $1.40 |
| Output $ / M tokens | $0.32 | $5.60 |
| Results tracked | 43 | 43 |
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Category by category
Coding Qwen2.5 72B Instruct leads
Llama-3.3-70B-Instruct: 30.6 (#284), Qwen2.5 72B Instruct: 32.8 (#256)
| Benchmark | Llama-3.3-70B-Instruct | Qwen2.5 72B Instruct |
|---|---|---|
| WeirdML | 14.4% | 16% |
| BigCodeBench Instruct | 46.9% | 45.8% |
| LMArena Coding | 1268 | 1293 |
| BigCodeBench Complete | 57.5% | 55.9% |
| SciCode | 26% | — |
| LiveBench Coding | 36.6% | — |
Agentic & Tool Use Llama-3.3-70B-Instruct leads
Llama-3.3-70B-Instruct: 25.6 (#100), Qwen2.5 72B Instruct: 22.0 (#124)
| Benchmark | Llama-3.3-70B-Instruct | Qwen2.5 72B Instruct |
|---|---|---|
| BALROG | 23% | 16.2% |
| Berkeley Function Calling Leaderboard | 31.9% | — |
| TheAgentCompany | — | 5.7% |
| METR Time Horizons | — | 35.8% |
Reasoning Qwen2.5 72B Instruct leads
Llama-3.3-70B-Instruct: 13.7 (#325), Qwen2.5 72B Instruct: 21.4 (#197)
| Benchmark | Llama-3.3-70B-Instruct | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Hard Prompts | 1257 | 1271 |
| DTBench | 59.5% | 62.9% |
| LMCA | 17.5% | 13.4% |
| Epoch Capabilities Index | 127.32 | 129.01 |
| ForecastBench | 58.6 | 57.5 |
| SimpleBench | 19.9% | — |
| CritPt | 0% | — |
| LiveBench Reasoning | 50.8% | — |
| LiveBench Data Analysis | 49.5% | — |
| BIG-Bench Hard | — | 79.8% |
| HellaSwag | — | 84.8% |
| LiveBench | 50.2% | — |
| PIQA | — | 82.6% |
| WinoGrande | — | 82.3% |
Math Qwen2.5 72B Instruct leads
Llama-3.3-70B-Instruct: 14.3 (#292), Qwen2.5 72B Instruct: 18.1 (#281)
| Benchmark | Llama-3.3-70B-Instruct | Qwen2.5 72B Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 5.1% | 8.1% |
| LMArena Math | 1268 | 1283 |
| MATH Level 5 | 41.6% | 63.2% |
| Omni-MATH | — | 33% |
| LiveBench Math | 42.2% | — |
Knowledge Llama-3.3-70B-Instruct leads
Llama-3.3-70B-Instruct: 30.5 (#220), Qwen2.5 72B Instruct: 26.9 (#247)
| Benchmark | Llama-3.3-70B-Instruct | Qwen2.5 72B Instruct |
|---|---|---|
| GPQA Diamond | 47.4% | 49.1% |
| Confabulations | 22.8% | 19.1% |
| LMArena Expert | 1225 | 1245 |
| MMLU | 86.3% | 85.3% |
| MMLU-Pro | — | 63.1% |
| Vectara Hallucination Rate | 4.1% | — |
| GPQA (HELM) | — | 42.6% |
| ARC (AI2) Challenge | — | 94.5% |
| TriviaQA | — | 71.9% |
Multilingual Qwen2.5 72B Instruct leads
Llama-3.3-70B-Instruct: 40.1 (#215), Qwen2.5 72B Instruct: 41.2 (#208)
| Benchmark | Llama-3.3-70B-Instruct | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Non-English | 1236 | 1252 |
| LMArena Chinese | 1217 | 1272 |
| LMArena French | 1280 | 1279 |
| LMArena German | 1251 | 1234 |
| LMArena Japanese | 1149 | 1180 |
| LMArena Korean | 1142 | 1188 |
| LMArena Russian | 1252 | 1264 |
| LMArena Spanish | 1269 | 1255 |
Instruction Following Llama-3.3-70B-Instruct leads
Llama-3.3-70B-Instruct: 71.3 (#152), Qwen2.5 72B Instruct: 65.7 (#216)
| Benchmark | Llama-3.3-70B-Instruct | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Instruction Following | 1242 | 1254 |
| LiveBench Instruction Following | 82.7% | — |
| IFEval | — | 80.6% |
Long Context Qwen2.5 72B Instruct leads
Llama-3.3-70B-Instruct: 26.4 (#290), Qwen2.5 72B Instruct: 38.9 (#183)
| Benchmark | Llama-3.3-70B-Instruct | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Longer Query | 1256 | 1282 |
| Fiction.LiveBench | 33.3% | — |
Writing & Preference Too close to call
Llama-3.3-70B-Instruct: 47.8 (#203), Qwen2.5 72B Instruct: 46.9 (#210)
| Benchmark | Llama-3.3-70B-Instruct | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Text | 1274 | 1269 |
| LMArena Creative Writing | 1250 | 1221 |
| LMArena Multi-Turn | 1279 | 1272 |
| WildBench | — | 80.2% |
| LiveBench Language | 39.2% | — |
Frequently asked questions
Is Llama-3.3-70B-Instruct better than Qwen2.5 72B Instruct?
Qwen2.5 72B Instruct is the stronger model overall, scoring 31.5 to 30.3 on the Noometry Index. Llama-3.3-70B-Instruct costs 16× less per token, which makes it the better buy when Qwen2.5 72B Instruct's lead doesn't matter for your workload.
Which is cheaper, Llama-3.3-70B-Instruct or Qwen2.5 72B Instruct?
Llama-3.3-70B-Instruct is cheaper. It lists at $0.10 per million input tokens and $0.32 per million output tokens; Qwen2.5 72B Instruct lists at $1.40 and $5.60.
Is Llama-3.3-70B-Instruct or Qwen2.5 72B Instruct better for coding?
Qwen2.5 72B Instruct scores higher on coding benchmarks: 32.8 versus 30.6 in the Noometry coding category.
Which has the bigger context window?
Qwen2.5 72B Instruct does, with 131K tokens against 128K.
How many benchmarks do Llama-3.3-70B-Instruct and Qwen2.5 72B Instruct share?
30 benchmarks have published results for both models. Llama-3.3-70B-Instruct has 43 scored results on Noometry and Qwen2.5 72B Instruct has 43.