Model comparison
Llama-3.3-70B-Instruct vs Qwen Max
Qwen Max is the stronger model overall, scoring 34.7 to 30.6 on the Noometry Index. Llama-3.3-70B-Instruct costs 18× less per token, which makes it the better buy when Qwen Max's lead doesn't matter for your workload.
Last verified . 21 shared benchmarks.
Meta
Alibaba (Qwen)Summary
- They share 21 benchmarks with published results for both. Llama-3.3-70B-Instruct scores higher in 3 categories and Qwen Max in 5 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in long context, where Qwen Max leads 39.4 to 26.4.
- The biggest single-benchmark swing is Fiction.LiveBench: 33.3% for Llama-3.3-70B-Instruct and 66.7% for Qwen Max.
- Llama-3.3-70B-Instruct is cheaper at $0.10 / $0.32 per million input/output tokens, against $1.60 / $6.40 for Qwen Max.
- Llama-3.3-70B-Instruct accepts more context: 128K tokens versus 33K.
- Llama-3.3-70B-Instruct has downloadable open weights; the other is API-only.
Side by side
| Llama-3.3-70B-Instruct | Qwen Max | |
|---|---|---|
| Provider | Meta | Alibaba (Qwen) |
| Noometry Index | 30.6 | 34.7 |
| Released | 2024-12-06 | 2024-04-03 |
| Weights | Open | Proprietary |
| Context window | 128K | 33K |
| Max output | 4K | 8K |
| Input $ / M tokens | $0.10 | $1.60 |
| Output $ / M tokens | $0.32 | $6.40 |
| Results tracked | 43 | 23 |
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Category by category
Coding Too close to call
Llama-3.3-70B-Instruct: 31.0 (#290), Qwen Max: 30.7 (#292)
| Benchmark | Llama-3.3-70B-Instruct | Qwen Max |
|---|---|---|
| LMArena Coding | 1268 | 1288 |
| Aider Polyglot | — | 21.8% |
| SciCode | 26% | — |
| WeirdML | 14.4% | — |
| BigCodeBench Instruct | 46.9% | — |
| LiveBench Coding | 36.6% | — |
| BigCodeBench Complete | 57.5% | — |
Agentic & Tool Use Not comparable
Llama-3.3-70B-Instruct: 25.8 (#105), Qwen Max: —
| Benchmark | Llama-3.3-70B-Instruct | Qwen Max |
|---|---|---|
| Berkeley Function Calling Leaderboard | 31.9% | — |
| BALROG | 23% | — |
Reasoning Qwen Max leads
Llama-3.3-70B-Instruct: 14.1 (#327), Qwen Max: 25.1 (#151)
| Benchmark | Llama-3.3-70B-Instruct | Qwen Max |
|---|---|---|
| LMArena Hard Prompts | 1257 | 1269 |
| SimpleBench | 19.9% | — |
| CritPt | 0% | — |
| LiveBench Reasoning | 50.8% | — |
| DTBench | 59.5% | — |
| LiveBench Data Analysis | 49.5% | — |
| LMCA | 17.5% | — |
| Epoch Capabilities Index | 127.33 | — |
| ForecastBench | 58.6 | — |
| LiveBench | 50.2% | — |
Math Qwen Max leads
Llama-3.3-70B-Instruct: 15.3 (#298), Qwen Max: 22.3 (#276)
| Benchmark | Llama-3.3-70B-Instruct | Qwen Max |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 5.1% | 16.1% |
| LMArena Math | 1267 | 1275 |
| MATH Level 5 | 41.6% | 67.2% |
| LiveBench Math | 42.2% | — |
| FrontierMath (Feb 2025 set) | — | 1% |
Knowledge Too close to call
Llama-3.3-70B-Instruct: 30.6 (#226), Qwen Max: 30.3 (#228)
| Benchmark | Llama-3.3-70B-Instruct | Qwen Max |
|---|---|---|
| GPQA Diamond | 47.4% | 56.1% |
| LMArena Expert | 1225 | 1248 |
| Confabulations | 22.8% | — |
| Vectara Hallucination Rate | 4.1% | — |
| MMLU | 86.3% | — |
Multilingual Qwen Max leads
Llama-3.3-70B-Instruct: 39.9 (#220), Qwen Max: 41.8 (#202)
| Benchmark | Llama-3.3-70B-Instruct | Qwen Max |
|---|---|---|
| LMArena Non-English | 1236 | 1263 |
| LMArena Chinese | 1217 | 1254 |
| LMArena French | 1281 | 1330 |
| LMArena German | 1251 | 1254 |
| LMArena Japanese | 1150 | 1205 |
| LMArena Korean | 1143 | 1142 |
| LMArena Russian | 1252 | 1274 |
| LMArena Spanish | 1270 | 1290 |
Instruction Following Llama-3.3-70B-Instruct leads
Llama-3.3-70B-Instruct: 71.1 (#157), Qwen Max: 66.5 (#208)
| Benchmark | Llama-3.3-70B-Instruct | Qwen Max |
|---|---|---|
| LMArena Instruction Following | 1242 | 1262 |
| LiveBench Instruction Following | 82.7% | — |
Long Context Qwen Max leads
Llama-3.3-70B-Instruct: 26.4 (#295), Qwen Max: 39.4 (#180)
| Benchmark | Llama-3.3-70B-Instruct | Qwen Max |
|---|---|---|
| Fiction.LiveBench | 33.3% | 66.7% |
| LMArena Longer Query | 1256 | 1288 |
Writing & Preference Too close to call
Llama-3.3-70B-Instruct: 47.6 (#207), Qwen Max: 47.8 (#205)
| Benchmark | Llama-3.3-70B-Instruct | Qwen Max |
|---|---|---|
| LMArena Text | 1274 | 1282 |
| LMArena Creative Writing | 1250 | 1248 |
| LMArena Multi-Turn | 1280 | 1277 |
| LiveBench Language | 39.2% | — |
Frequently asked questions
Is Llama-3.3-70B-Instruct better than Qwen Max?
Qwen Max is the stronger model overall, scoring 34.7 to 30.6 on the Noometry Index. Llama-3.3-70B-Instruct costs 18× less per token, which makes it the better buy when Qwen Max's lead doesn't matter for your workload.
Which is cheaper, Llama-3.3-70B-Instruct or Qwen Max?
Llama-3.3-70B-Instruct is cheaper. It lists at $0.10 per million input tokens and $0.32 per million output tokens; Qwen Max lists at $1.60 and $6.40.
Is Llama-3.3-70B-Instruct or Qwen Max better for coding?
They score almost the same on coding (31.0 vs 30.7); test both on your own repository before choosing.
Which has the bigger context window?
Llama-3.3-70B-Instruct does, with 128K tokens against 33K.
How many benchmarks do Llama-3.3-70B-Instruct and Qwen Max share?
21 benchmarks have published results for both models. Llama-3.3-70B-Instruct has 43 scored results on Noometry and Qwen Max has 23.