Model comparison
Llama-3.3-70B-Instruct vs Qwen3 Max
Qwen3 Max is the stronger model overall, scoring 43.5 to 30.3 on the Noometry Index. Llama-3.3-70B-Instruct costs 15× less per token, which makes it the better buy when Qwen3 Max's lead doesn't matter for your workload.
Last verified . 24 shared benchmarks.
Meta
Alibaba (Qwen)Summary
- They share 24 benchmarks with published results for both. Llama-3.3-70B-Instruct scores higher in 0 categories and Qwen3 Max in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where Qwen3 Max leads 37.6 to 14.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 5.1% for Llama-3.3-70B-Instruct and 73.3% for Qwen3 Max.
- Llama-3.3-70B-Instruct is cheaper at $0.10 / $0.32 per million input/output tokens, against $1.20 / $6 for Qwen3 Max.
- Qwen3 Max accepts more context: 262K tokens versus 128K.
- Llama-3.3-70B-Instruct has downloadable open weights; the other is API-only.
Side by side
| Llama-3.3-70B-Instruct | Qwen3 Max | |
|---|---|---|
| Provider | Meta | Alibaba (Qwen) |
| Noometry Index | 30.3 | 43.5 |
| Released | 2024-12-06 | 2025-09-23 |
| Weights | Open | Proprietary |
| Context window | 128K | 262K |
| Max output | 4K | 66K |
| Input $ / M tokens | $0.10 | $1.20 |
| Output $ / M tokens | $0.32 | $6 |
| Results tracked | 43 | 33 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding Qwen3 Max leads
Llama-3.3-70B-Instruct: 30.6 (#284), Qwen3 Max: 42.4 (#93)
| Benchmark | Llama-3.3-70B-Instruct | Qwen3 Max |
|---|---|---|
| LMArena Coding | 1268 | 1456 |
| SciCode | 26% | — |
| WeirdML | 14.4% | — |
| BigCodeBench Instruct | 46.9% | — |
| LiveBench Coding | 36.6% | — |
| BigCodeBench Complete | 57.5% | — |
| ALE-Bench | — | 370.45 |
Agentic & Tool Use Not comparable
Llama-3.3-70B-Instruct: 25.6 (#100), Qwen3 Max: —
| Benchmark | Llama-3.3-70B-Instruct | Qwen3 Max |
|---|---|---|
| Berkeley Function Calling Leaderboard | 31.9% | — |
| BALROG | 23% | — |
| Vending-Bench 2 | — | 71.56 |
Reasoning Qwen3 Max leads
Llama-3.3-70B-Instruct: 13.7 (#325), Qwen3 Max: 22.0 (#186)
| Benchmark | Llama-3.3-70B-Instruct | Qwen3 Max |
|---|---|---|
| LMArena Hard Prompts | 1257 | 1448 |
| DTBench | 59.5% | 82.1% |
| LMCA | 17.5% | 28.3% |
| Epoch Capabilities Index | 127.32 | 142.41 |
| SimpleBench | 19.9% | — |
| Kagi LLM Benchmark | — | 72.5% |
| NYT Connections (extended) | — | 30.1% |
| CritPt | 0% | — |
| Chess Puzzles | — | 4% |
| LiveBench Reasoning | 50.8% | — |
| Mystery Game Puzzles | — | 5% |
| LiveBench Data Analysis | 49.5% | — |
| ForecastBench | 58.6 | — |
| LiveBench | 50.2% | — |
Math Qwen3 Max leads
Llama-3.3-70B-Instruct: 14.3 (#292), Qwen3 Max: 37.6 (#94)
| Benchmark | Llama-3.3-70B-Instruct | Qwen3 Max |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 5.1% | 73.3% |
| LMArena Math | 1268 | 1447 |
| MATH Level 5 | 41.6% | 97.1% |
| FrontierMath (Tiers 1-3) | — | 18.9% |
| LiveBench Math | 42.2% | — |
Knowledge Qwen3 Max leads
Llama-3.3-70B-Instruct: 30.5 (#220), Qwen3 Max: 48.1 (#72)
| Benchmark | Llama-3.3-70B-Instruct | Qwen3 Max |
|---|---|---|
| GPQA Diamond | 47.4% | 72.6% |
| LMArena Expert | 1225 | 1457 |
| SimpleQA Verified | — | 48.7% |
| Confabulations | 22.8% | — |
| Vectara Hallucination Rate | 4.1% | — |
| MMLU | 86.3% | — |
Multilingual Qwen3 Max leads
Llama-3.3-70B-Instruct: 40.1 (#215), Qwen3 Max: 53.9 (#58)
| Benchmark | Llama-3.3-70B-Instruct | Qwen3 Max |
|---|---|---|
| LMArena Non-English | 1236 | 1429 |
| LMArena Chinese | 1217 | 1479 |
| LMArena French | 1280 | 1450 |
| LMArena German | 1251 | 1463 |
| LMArena Japanese | 1149 | 1399 |
| LMArena Korean | 1142 | 1397 |
| LMArena Russian | 1252 | 1428 |
| LMArena Spanish | 1269 | 1463 |
Instruction Following Qwen3 Max leads
Llama-3.3-70B-Instruct: 71.3 (#152), Qwen3 Max: 75.0 (#81)
| Benchmark | Llama-3.3-70B-Instruct | Qwen3 Max |
|---|---|---|
| LMArena Instruction Following | 1242 | 1420 |
| LiveBench Instruction Following | 82.7% | — |
Long Context Qwen3 Max leads
Llama-3.3-70B-Instruct: 26.4 (#290), Qwen3 Max: 41.6 (#129)
| Benchmark | Llama-3.3-70B-Instruct | Qwen3 Max |
|---|---|---|
| Fiction.LiveBench | 33.3% | 66.7% |
| LMArena Longer Query | 1256 | 1439 |
| CL-bench | — | 14.5% |
Writing & Preference Qwen3 Max leads
Llama-3.3-70B-Instruct: 47.8 (#203), Qwen3 Max: 62.6 (#72)
| Benchmark | Llama-3.3-70B-Instruct | Qwen3 Max |
|---|---|---|
| LMArena Text | 1274 | 1439 |
| LMArena Creative Writing | 1250 | 1401 |
| LMArena Multi-Turn | 1279 | 1446 |
| LiveBench Language | 39.2% | — |
Frequently asked questions
Is Llama-3.3-70B-Instruct better than Qwen3 Max?
Qwen3 Max is the stronger model overall, scoring 43.5 to 30.3 on the Noometry Index. Llama-3.3-70B-Instruct costs 15× less per token, which makes it the better buy when Qwen3 Max's lead doesn't matter for your workload.
Which is cheaper, Llama-3.3-70B-Instruct or Qwen3 Max?
Llama-3.3-70B-Instruct is cheaper. It lists at $0.10 per million input tokens and $0.32 per million output tokens; Qwen3 Max lists at $1.20 and $6.
Is Llama-3.3-70B-Instruct or Qwen3 Max better for coding?
Qwen3 Max scores higher on coding benchmarks: 42.4 versus 30.6 in the Noometry coding category.
Which has the bigger context window?
Qwen3 Max does, with 262K tokens against 128K.
How many benchmarks do Llama-3.3-70B-Instruct and Qwen3 Max share?
24 benchmarks have published results for both models. Llama-3.3-70B-Instruct has 43 scored results on Noometry and Qwen3 Max has 33.