Model comparison
Llama-3.3-70B-Instruct vs Qwen3.6 Flash
Qwen3.6 Flash is the stronger model overall, scoring 38.4 to 30.3 on the Noometry Index. Llama-3.3-70B-Instruct costs 2.7× less per token, which makes it the better buy when Qwen3.6 Flash's lead doesn't matter for your workload.
Last verified . 6 shared benchmarks.
Meta
Alibaba (Qwen)Summary
- They share 6 benchmarks with published results for both. Llama-3.3-70B-Instruct scores higher in 0 categories and Qwen3.6 Flash in 3 categories; 3 gaps are clear of the uncertainty.
- The widest gap is in math, where Qwen3.6 Flash leads 38.0 to 14.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 5.1% for Llama-3.3-70B-Instruct and 84.4% for Qwen3.6 Flash.
- Llama-3.3-70B-Instruct is cheaper at $0.10 / $0.32 per million input/output tokens, against $0.19 / $1.13 for Qwen3.6 Flash.
- Qwen3.6 Flash accepts more context: 1M 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 Flash | |
|---|---|---|
| Provider | Meta | Alibaba (Qwen) |
| Noometry Index | 30.3 | 38.4 |
| Released | 2024-12-06 | 2026-04-27 |
| Weights | Open | Proprietary |
| Context window | 128K | 1M |
| Max output | 4K | 66K |
| Input $ / M tokens | $0.10 | $0.19 |
| Output $ / M tokens | $0.32 | $1.13 |
| Results tracked | 43 | 13 |
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Category by category
Coding Not comparable
Llama-3.3-70B-Instruct: 30.6 (#284), Qwen3.6 Flash: —
| Benchmark | Llama-3.3-70B-Instruct | Qwen3.6 Flash |
|---|---|---|
| SciCode | 26% | — |
| WeirdML | 14.4% | — |
| BigCodeBench Instruct | 46.9% | — |
| LiveBench Coding | 36.6% | — |
| LMArena Coding | 1268 | — |
| BigCodeBench Complete | 57.5% | — |
| ALE-Bench | — | 326.4 |
Agentic & Tool Use Not comparable
Llama-3.3-70B-Instruct: 25.6 (#100), Qwen3.6 Flash: —
| Benchmark | Llama-3.3-70B-Instruct | Qwen3.6 Flash |
|---|---|---|
| Berkeley Function Calling Leaderboard | 31.9% | — |
| BALROG | 23% | — |
Reasoning Qwen3.6 Flash leads
Llama-3.3-70B-Instruct: 13.7 (#325), Qwen3.6 Flash: 28.4 (#90)
| Benchmark | Llama-3.3-70B-Instruct | Qwen3.6 Flash |
|---|---|---|
| SimpleBench | 19.9% | 35.2% |
| DTBench | 59.5% | 77.1% |
| LMCA | 17.5% | 31% |
| Epoch Capabilities Index | 127.32 | 143.26 |
| CritPt | 0% | — |
| Chess Puzzles | — | 20% |
| LiveBench Reasoning | 50.8% | — |
| LMArena Hard Prompts | 1257 | — |
| Mystery Game Puzzles | — | 18% |
| LiveBench Data Analysis | 49.5% | — |
| ForecastBench | 58.6 | — |
| LiveBench | 50.2% | — |
Math Qwen3.6 Flash leads
Llama-3.3-70B-Instruct: 14.3 (#292), Qwen3.6 Flash: 38.0 (#88)
| Benchmark | Llama-3.3-70B-Instruct | Qwen3.6 Flash |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 5.1% | 84.4% |
| FrontierMath (Tiers 1-3) | — | 22.5% |
| LiveBench Math | 42.2% | — |
| LMArena Math | 1268 | — |
| MATH Level 5 | 41.6% | — |
| FrontierMath (Feb 2025 set) | — | 10.3% |
| FrontierMath Tier 4 (v1) | — | 0% |
Knowledge Qwen3.6 Flash leads
Llama-3.3-70B-Instruct: 30.5 (#220), Qwen3.6 Flash: 42.2 (#90)
| Benchmark | Llama-3.3-70B-Instruct | Qwen3.6 Flash |
|---|---|---|
| GPQA Diamond | 47.4% | 83.3% |
| SimpleQA Verified | — | 15.9% |
| Confabulations | 22.8% | — |
| Vectara Hallucination Rate | 4.1% | — |
| LMArena Expert | 1225 | — |
| MMLU | 86.3% | — |
Multilingual Not comparable
Llama-3.3-70B-Instruct: 40.1 (#215), Qwen3.6 Flash: —
| Benchmark | Llama-3.3-70B-Instruct | Qwen3.6 Flash |
|---|---|---|
| LMArena Non-English | 1236 | — |
| LMArena Chinese | 1217 | — |
| LMArena French | 1280 | — |
| LMArena German | 1251 | — |
| LMArena Japanese | 1149 | — |
| LMArena Korean | 1142 | — |
| LMArena Russian | 1252 | — |
| LMArena Spanish | 1269 | — |
Instruction Following Not comparable
Llama-3.3-70B-Instruct: 71.3 (#152), Qwen3.6 Flash: —
| Benchmark | Llama-3.3-70B-Instruct | Qwen3.6 Flash |
|---|---|---|
| LiveBench Instruction Following | 82.7% | — |
| LMArena Instruction Following | 1242 | — |
Long Context Not comparable
Llama-3.3-70B-Instruct: 26.4 (#290), Qwen3.6 Flash: —
| Benchmark | Llama-3.3-70B-Instruct | Qwen3.6 Flash |
|---|---|---|
| Fiction.LiveBench | 33.3% | — |
| LMArena Longer Query | 1256 | — |
Writing & Preference Not comparable
Llama-3.3-70B-Instruct: 47.8 (#203), Qwen3.6 Flash: —
| Benchmark | Llama-3.3-70B-Instruct | Qwen3.6 Flash |
|---|---|---|
| LMArena Text | 1274 | — |
| LMArena Creative Writing | 1250 | — |
| LMArena Multi-Turn | 1279 | — |
| LiveBench Language | 39.2% | — |
Frequently asked questions
Is Llama-3.3-70B-Instruct better than Qwen3.6 Flash?
Qwen3.6 Flash is the stronger model overall, scoring 38.4 to 30.3 on the Noometry Index. Llama-3.3-70B-Instruct costs 2.7× less per token, which makes it the better buy when Qwen3.6 Flash's lead doesn't matter for your workload.
Which is cheaper, Llama-3.3-70B-Instruct or Qwen3.6 Flash?
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 Flash lists at $0.19 and $1.13.
Which has the bigger context window?
Qwen3.6 Flash does, with 1M tokens against 128K.
How many benchmarks do Llama-3.3-70B-Instruct and Qwen3.6 Flash share?
6 benchmarks have published results for both models. Llama-3.3-70B-Instruct has 43 scored results on Noometry and Qwen3.6 Flash has 13.