Model comparison
Llama-3.3-70B-Instruct vs Qwen Turbo
Llama-3.3-70B-Instruct is the stronger model overall, scoring 30.6 to 27.1 on the Noometry Index. Qwen Turbo costs 1.8× less per token, which makes it the better buy when Llama-3.3-70B-Instruct's lead doesn't matter for your workload.
Last verified . 3 shared benchmarks.
Meta
Alibaba (Qwen)Summary
- They share 3 benchmarks with published results for both. Llama-3.3-70B-Instruct scores higher in 1 category and Qwen Turbo in 1 category; one gap is clear of the uncertainty.
- The widest gap is in knowledge, where Llama-3.3-70B-Instruct leads 30.6 to 22.2.
- The biggest single-benchmark swing is MATH Level 5: 41.6% for Llama-3.3-70B-Instruct and 56.2% for Qwen Turbo.
- Qwen Turbo is cheaper at $0.05 / $0.20 per million input/output tokens, against $0.10 / $0.32 for Llama-3.3-70B-Instruct.
- Qwen Turbo 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 | Qwen Turbo | |
|---|---|---|
| Provider | Meta | Alibaba (Qwen) |
| Noometry Index | 30.6 | 27.1 |
| Released | 2024-12-06 | 2024-11-01 |
| Weights | Open | Proprietary |
| Context window | 128K | 1M |
| Max output | 4K | 16K |
| Input $ / M tokens | $0.10 | $0.05 |
| Output $ / M tokens | $0.32 | $0.20 |
| Results tracked | 43 | 3 |
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Category by category
Coding Not comparable
Llama-3.3-70B-Instruct: 31.0 (#290), Qwen Turbo: —
| Benchmark | Llama-3.3-70B-Instruct | Qwen Turbo |
|---|---|---|
| SciCode | 26% | — |
| WeirdML | 14.4% | — |
| BigCodeBench Instruct | 46.9% | — |
| LiveBench Coding | 36.6% | — |
| LMArena Coding | 1268 | — |
| BigCodeBench Complete | 57.5% | — |
Agentic & Tool Use Not comparable
Llama-3.3-70B-Instruct: 25.8 (#105), Qwen Turbo: —
| Benchmark | Llama-3.3-70B-Instruct | Qwen Turbo |
|---|---|---|
| Berkeley Function Calling Leaderboard | 31.9% | — |
| BALROG | 23% | — |
Reasoning Not comparable
Llama-3.3-70B-Instruct: 14.1 (#327), Qwen Turbo: —
| Benchmark | Llama-3.3-70B-Instruct | Qwen Turbo |
|---|---|---|
| SimpleBench | 19.9% | — |
| CritPt | 0% | — |
| LiveBench Reasoning | 50.8% | — |
| LMArena Hard Prompts | 1257 | — |
| DTBench | 59.5% | — |
| LiveBench Data Analysis | 49.5% | — |
| LMCA | 17.5% | — |
| Epoch Capabilities Index | 127.33 | — |
| ForecastBench | 58.6 | — |
| LiveBench | 50.2% | — |
Math Too close to call
Llama-3.3-70B-Instruct: 15.3 (#298), Qwen Turbo: 15.3 (#297)
| Benchmark | Llama-3.3-70B-Instruct | Qwen Turbo |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 5.1% | 6.1% |
| MATH Level 5 | 41.6% | 56.2% |
| LiveBench Math | 42.2% | — |
| LMArena Math | 1267 | — |
Knowledge Llama-3.3-70B-Instruct leads
Llama-3.3-70B-Instruct: 30.6 (#226), Qwen Turbo: 22.2 (#272)
| Benchmark | Llama-3.3-70B-Instruct | Qwen Turbo |
|---|---|---|
| GPQA Diamond | 47.4% | 41.8% |
| Confabulations | 22.8% | — |
| Vectara Hallucination Rate | 4.1% | — |
| LMArena Expert | 1225 | — |
| MMLU | 86.3% | — |
Multilingual Not comparable
Llama-3.3-70B-Instruct: 39.9 (#220), Qwen Turbo: —
| Benchmark | Llama-3.3-70B-Instruct | Qwen Turbo |
|---|---|---|
| LMArena Non-English | 1236 | — |
| LMArena Chinese | 1217 | — |
| LMArena French | 1281 | — |
| LMArena German | 1251 | — |
| LMArena Japanese | 1150 | — |
| LMArena Korean | 1143 | — |
| LMArena Russian | 1252 | — |
| LMArena Spanish | 1270 | — |
Instruction Following Not comparable
Llama-3.3-70B-Instruct: 71.1 (#157), Qwen Turbo: —
| Benchmark | Llama-3.3-70B-Instruct | Qwen Turbo |
|---|---|---|
| LiveBench Instruction Following | 82.7% | — |
| LMArena Instruction Following | 1242 | — |
Long Context Not comparable
Llama-3.3-70B-Instruct: 26.4 (#295), Qwen Turbo: —
| Benchmark | Llama-3.3-70B-Instruct | Qwen Turbo |
|---|---|---|
| Fiction.LiveBench | 33.3% | — |
| LMArena Longer Query | 1256 | — |
Writing & Preference Not comparable
Llama-3.3-70B-Instruct: 47.6 (#207), Qwen Turbo: —
| Benchmark | Llama-3.3-70B-Instruct | Qwen Turbo |
|---|---|---|
| LMArena Text | 1274 | — |
| LMArena Creative Writing | 1250 | — |
| LMArena Multi-Turn | 1280 | — |
| LiveBench Language | 39.2% | — |
Frequently asked questions
Is Llama-3.3-70B-Instruct better than Qwen Turbo?
Llama-3.3-70B-Instruct is the stronger model overall, scoring 30.6 to 27.1 on the Noometry Index. Qwen Turbo costs 1.8× less per token, which makes it the better buy when Llama-3.3-70B-Instruct's lead doesn't matter for your workload.
Which is cheaper, Llama-3.3-70B-Instruct or Qwen Turbo?
Qwen Turbo is cheaper. It lists at $0.05 per million input tokens and $0.20 per million output tokens; Llama-3.3-70B-Instruct lists at $0.10 and $0.32.
Which has the bigger context window?
Qwen Turbo does, with 1M tokens against 128K.
How many benchmarks do Llama-3.3-70B-Instruct and Qwen Turbo share?
3 benchmarks have published results for both models. Llama-3.3-70B-Instruct has 43 scored results on Noometry and Qwen Turbo has 3.