Model comparison
Llama-3.3-70B-Instruct vs Qwen3.6 Max Preview
Qwen3.6 Max Preview is the stronger model overall, scoring 51.5 to 30.6 on the Noometry Index. Llama-3.3-70B-Instruct costs 19× less per token, which makes it the better buy when Qwen3.6 Max Preview's lead doesn't matter for your workload.
Last verified . 20 shared benchmarks.
Meta
Alibaba (Qwen)Summary
- They share 20 benchmarks with published results for both. Llama-3.3-70B-Instruct scores higher in 0 categories and Qwen3.6 Max Preview in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where Qwen3.6 Max Preview leads 54.1 to 15.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 5.1% for Llama-3.3-70B-Instruct and 91.1% for Qwen3.6 Max Preview.
- Llama-3.3-70B-Instruct is cheaper at $0.10 / $0.32 per million input/output tokens, against $1.30 / $7.80 for Qwen3.6 Max Preview.
- Qwen3.6 Max Preview 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 | Qwen3.6 Max Preview | |
|---|---|---|
| Provider | Meta | Alibaba (Qwen) |
| Noometry Index | 30.6 | 51.5 |
| Released | 2024-12-06 | 2026-04-20 |
| Weights | Open | Proprietary |
| Context window | 128K | 262K |
| Max output | 4K | 66K |
| Input $ / M tokens | $0.10 | $1.30 |
| Output $ / M tokens | $0.32 | $7.80 |
| Results tracked | 43 | 29 |
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Category by category
Coding Qwen3.6 Max Preview leads
Llama-3.3-70B-Instruct: 31.0 (#290), Qwen3.6 Max Preview: 48.7 (#54)
| Benchmark | Llama-3.3-70B-Instruct | Qwen3.6 Max Preview |
|---|---|---|
| LMArena Coding | 1268 | 1471 |
| SWE-bench Verified | — | 76.7% |
| LMArena WebDev | — | 1482 |
| 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), Qwen3.6 Max Preview: —
| Benchmark | Llama-3.3-70B-Instruct | Qwen3.6 Max Preview |
|---|---|---|
| Berkeley Function Calling Leaderboard | 31.9% | — |
| BALROG | 23% | — |
| Vending-Bench 2 | — | 4,254 |
Reasoning Qwen3.6 Max Preview leads
Llama-3.3-70B-Instruct: 14.1 (#327), Qwen3.6 Max Preview: 41.7 (#53)
| Benchmark | Llama-3.3-70B-Instruct | Qwen3.6 Max Preview |
|---|---|---|
| SimpleBench | 19.9% | 63% |
| LMArena Hard Prompts | 1257 | 1457 |
| DTBench | 59.5% | 87.2% |
| LMCA | 17.5% | 42.5% |
| Epoch Capabilities Index | 127.33 | 149.24 |
| NYT Connections (extended) | — | 74.1% |
| CritPt | 0% | — |
| Chess Puzzles | — | 20% |
| LiveBench Reasoning | 50.8% | — |
| Mystery Game Puzzles | — | 19% |
| LiveBench Data Analysis | 49.5% | — |
| ForecastBench | 58.6 | — |
| LiveBench | 50.2% | — |
Math Qwen3.6 Max Preview leads
Llama-3.3-70B-Instruct: 15.3 (#298), Qwen3.6 Max Preview: 54.1 (#46)
| Benchmark | Llama-3.3-70B-Instruct | Qwen3.6 Max Preview |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 5.1% | 91.1% |
| LMArena Math | 1267 | 1465 |
| LiveBench Math | 42.2% | — |
| MATH Level 5 | 41.6% | — |
| FrontierMath (Feb 2025 set) | — | 23.1% |
| FrontierMath Tier 4 (v1) | — | 4.2% |
Knowledge Qwen3.6 Max Preview leads
Llama-3.3-70B-Instruct: 30.6 (#226), Qwen3.6 Max Preview: 57.6 (#39)
| Benchmark | Llama-3.3-70B-Instruct | Qwen3.6 Max Preview |
|---|---|---|
| GPQA Diamond | 47.4% | 87.4% |
| LMArena Expert | 1225 | 1478 |
| SimpleQA Verified | — | 52% |
| Confabulations | 22.8% | — |
| Vectara Hallucination Rate | 4.1% | — |
| MMLU | 86.3% | — |
Multilingual Qwen3.6 Max Preview leads
Llama-3.3-70B-Instruct: 39.9 (#220), Qwen3.6 Max Preview: 54.2 (#48)
| Benchmark | Llama-3.3-70B-Instruct | Qwen3.6 Max Preview |
|---|---|---|
| LMArena Non-English | 1236 | 1437 |
| LMArena Chinese | 1217 | 1487 |
| LMArena French | 1281 | 1449 |
| LMArena Russian | 1252 | 1445 |
| LMArena Spanish | 1270 | 1454 |
| LMArena German | 1251 | — |
| LMArena Japanese | 1150 | — |
| LMArena Korean | 1143 | — |
Instruction Following Qwen3.6 Max Preview leads
Llama-3.3-70B-Instruct: 71.1 (#157), Qwen3.6 Max Preview: 75.7 (#55)
| Benchmark | Llama-3.3-70B-Instruct | Qwen3.6 Max Preview |
|---|---|---|
| LMArena Instruction Following | 1242 | 1438 |
| LiveBench Instruction Following | 82.7% | — |
Long Context Qwen3.6 Max Preview leads
Llama-3.3-70B-Instruct: 26.4 (#295), Qwen3.6 Max Preview: 44.6 (#61)
| Benchmark | Llama-3.3-70B-Instruct | Qwen3.6 Max Preview |
|---|---|---|
| LMArena Longer Query | 1256 | 1457 |
| Fiction.LiveBench | 33.3% | — |
Writing & Preference Qwen3.6 Max Preview leads
Llama-3.3-70B-Instruct: 47.6 (#207), Qwen3.6 Max Preview: 63.8 (#60)
| Benchmark | Llama-3.3-70B-Instruct | Qwen3.6 Max Preview |
|---|---|---|
| LMArena Text | 1274 | 1447 |
| LMArena Creative Writing | 1250 | 1435 |
| LMArena Multi-Turn | 1280 | 1456 |
| LiveBench Language | 39.2% | — |
Frequently asked questions
Is Llama-3.3-70B-Instruct better than Qwen3.6 Max Preview?
Qwen3.6 Max Preview is the stronger model overall, scoring 51.5 to 30.6 on the Noometry Index. Llama-3.3-70B-Instruct costs 19× less per token, which makes it the better buy when Qwen3.6 Max Preview's lead doesn't matter for your workload.
Which is cheaper, Llama-3.3-70B-Instruct or Qwen3.6 Max Preview?
Llama-3.3-70B-Instruct is cheaper. It lists at $0.10 per million input tokens and $0.32 per million output tokens; Qwen3.6 Max Preview lists at $1.30 and $7.80.
Is Llama-3.3-70B-Instruct or Qwen3.6 Max Preview better for coding?
Qwen3.6 Max Preview scores higher on coding benchmarks: 48.7 versus 31.0 in the Noometry coding category.
Which has the bigger context window?
Qwen3.6 Max Preview does, with 262K tokens against 128K.
How many benchmarks do Llama-3.3-70B-Instruct and Qwen3.6 Max Preview share?
20 benchmarks have published results for both models. Llama-3.3-70B-Instruct has 43 scored results on Noometry and Qwen3.6 Max Preview has 29.