Model comparison
Llama-3.3-70B-Instruct vs Qwen3.5-9B
Qwen3.5-9B is the stronger model overall, scoring 33.3 to 30.3 on the Noometry Index.
Last verified . 7 shared benchmarks.
Meta
Alibaba (Qwen)Summary
- They share 7 benchmarks with published results for both. Llama-3.3-70B-Instruct scores higher in 1 category and Qwen3.5-9B in 4 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in math, where Qwen3.5-9B leads 33.5 to 14.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 5.1% for Llama-3.3-70B-Instruct and 61.7% for Qwen3.5-9B.
- Qwen3.5-9B is cheaper at $0.10 / $0.15 per million input/output tokens, against $0.10 / $0.32 for Llama-3.3-70B-Instruct.
- Qwen3.5-9B accepts more context: 262K tokens versus 128K.
Side by side
| Llama-3.3-70B-Instruct | Qwen3.5-9B | |
|---|---|---|
| Provider | Meta | Alibaba (Qwen) |
| Noometry Index | 30.3 | 33.3 |
| Released | 2024-12-06 | 2026-02-23 |
| Weights | Open | Open |
| Context window | 128K | 262K |
| Max output | 4K | 66K |
| Input $ / M tokens | $0.10 | $0.10 |
| Output $ / M tokens | $0.32 | $0.15 |
| Results tracked | 43 | 10 |
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Category by category
Coding Qwen3.5-9B leads
Llama-3.3-70B-Instruct: 30.6 (#284), Qwen3.5-9B: 35.3 (#213)
| Benchmark | Llama-3.3-70B-Instruct | Qwen3.5-9B |
|---|---|---|
| SciCode | 26% | 27.5% |
| WeirdML | 14.4% | — |
| BigCodeBench Instruct | 46.9% | — |
| LiveBench Coding | 36.6% | — |
| LMArena Coding | 1268 | — |
| BigCodeBench Complete | 57.5% | — |
Agentic & Tool Use Llama-3.3-70B-Instruct leads
Llama-3.3-70B-Instruct: 25.6 (#100), Qwen3.5-9B: 14.4 (#143)
| Benchmark | Llama-3.3-70B-Instruct | Qwen3.5-9B |
|---|---|---|
| Terminal-Bench | — | 9.2% |
| Berkeley Function Calling Leaderboard | 31.9% | — |
| BALROG | 23% | — |
Reasoning Qwen3.5-9B leads
Llama-3.3-70B-Instruct: 13.7 (#325), Qwen3.5-9B: 22.5 (#178)
| Benchmark | Llama-3.3-70B-Instruct | Qwen3.5-9B |
|---|---|---|
| CritPt | 0% | 0.3% |
| DTBench | 59.5% | 71.2% |
| LMCA | 17.5% | 24.5% |
| Epoch Capabilities Index | 127.32 | 139.45 |
| SimpleBench | 19.9% | — |
| Chess Puzzles | — | 12% |
| LiveBench Reasoning | 50.8% | — |
| LMArena Hard Prompts | 1257 | — |
| LiveBench Data Analysis | 49.5% | — |
| ForecastBench | 58.6 | — |
| LiveBench | 50.2% | — |
Math Qwen3.5-9B leads
Llama-3.3-70B-Instruct: 14.3 (#292), Qwen3.5-9B: 33.5 (#174)
| Benchmark | Llama-3.3-70B-Instruct | Qwen3.5-9B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 5.1% | 61.7% |
| MathArena Final-Answer Competitions | — | 48.5% |
| LiveBench Math | 42.2% | — |
| LMArena Math | 1268 | — |
| MATH Level 5 | 41.6% | — |
Knowledge Qwen3.5-9B leads
Llama-3.3-70B-Instruct: 30.5 (#220), Qwen3.5-9B: 46.0 (#77)
| Benchmark | Llama-3.3-70B-Instruct | Qwen3.5-9B |
|---|---|---|
| GPQA Diamond | 47.4% | 79% |
| 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), Qwen3.5-9B: —
| Benchmark | Llama-3.3-70B-Instruct | Qwen3.5-9B |
|---|---|---|
| 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), Qwen3.5-9B: —
| Benchmark | Llama-3.3-70B-Instruct | Qwen3.5-9B |
|---|---|---|
| LiveBench Instruction Following | 82.7% | — |
| LMArena Instruction Following | 1242 | — |
Long Context Not comparable
Llama-3.3-70B-Instruct: 26.4 (#290), Qwen3.5-9B: —
| Benchmark | Llama-3.3-70B-Instruct | Qwen3.5-9B |
|---|---|---|
| Fiction.LiveBench | 33.3% | — |
| LMArena Longer Query | 1256 | — |
Writing & Preference Not comparable
Llama-3.3-70B-Instruct: 47.8 (#203), Qwen3.5-9B: —
| Benchmark | Llama-3.3-70B-Instruct | Qwen3.5-9B |
|---|---|---|
| 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 Qwen3.5-9B?
Qwen3.5-9B is the stronger model overall, scoring 33.3 to 30.3 on the Noometry Index.
Which is cheaper, Llama-3.3-70B-Instruct or Qwen3.5-9B?
Qwen3.5-9B is cheaper. It lists at $0.10 per million input tokens and $0.15 per million output tokens; Llama-3.3-70B-Instruct lists at $0.10 and $0.32.
Is Llama-3.3-70B-Instruct or Qwen3.5-9B better for coding?
Qwen3.5-9B scores higher on coding benchmarks: 35.3 versus 30.6 in the Noometry coding category.
Which has the bigger context window?
Qwen3.5-9B does, with 262K tokens against 128K.
How many benchmarks do Llama-3.3-70B-Instruct and Qwen3.5-9B share?
7 benchmarks have published results for both models. Llama-3.3-70B-Instruct has 43 scored results on Noometry and Qwen3.5-9B has 10.