Model comparison
Llama 4 Scout vs Qwen3.5-9B
Qwen3.5-9B is the stronger model overall, scoring 33.3 to 27.4 on the Noometry Index.
Last verified . 7 shared benchmarks.
Meta
Alibaba (Qwen)Summary
- They share 7 benchmarks with published results for both. Llama 4 Scout scores higher in 1 category and Qwen3.5-9B in 4 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in coding, where Qwen3.5-9B leads 35.3 to 19.9.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 7.8% for Llama 4 Scout 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 / $0.30 for Llama 4 Scout.
- Qwen3.5-9B accepts more context: 262K tokens versus 128K.
Side by side
| Llama 4 Scout | Qwen3.5-9B | |
|---|---|---|
| Provider | Meta | Alibaba (Qwen) |
| Noometry Index | 27.4 | 33.3 |
| Released | 2025-04-05 | 2026-02-23 |
| Weights | Open | Open |
| Context window | 128K | 262K |
| Max output | 4K | 66K |
| Input $ / M tokens | $0.10 | $0.10 |
| Output $ / M tokens | $0.30 | $0.15 |
| Results tracked | 43 | 10 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding Qwen3.5-9B leads
Llama 4 Scout: 19.9 (#334), Qwen3.5-9B: 35.3 (#213)
| Benchmark | Llama 4 Scout | Qwen3.5-9B |
|---|---|---|
| SciCode | 17% | 27.5% |
| SWE-bench Verified (bash only) | 9.1% | — |
| LMArena Coding | 1286 | — |
| BigCodeBench Complete | 43.1% | — |
Agentic & Tool Use Llama 4 Scout leads
Llama 4 Scout: 24.4 (#109), Qwen3.5-9B: 14.4 (#143)
| Benchmark | Llama 4 Scout | Qwen3.5-9B |
|---|---|---|
| Terminal-Bench | — | 9.2% |
| Berkeley Function Calling Leaderboard | 28.1% | — |
Reasoning Qwen3.5-9B leads
Llama 4 Scout: 9.2 (#339), Qwen3.5-9B: 22.5 (#178)
| Benchmark | Llama 4 Scout | Qwen3.5-9B |
|---|---|---|
| CritPt | 0% | 0.3% |
| DTBench | 57.9% | 71.2% |
| LMCA | 12% | 24.5% |
| Epoch Capabilities Index | 129.64 | 139.45 |
| ARC-AGI-2 | 0% | — |
| Kagi LLM Benchmark | 36.9% | — |
| ARC-AGI-1 | 0.5% | — |
| Chess Puzzles | — | 12% |
| LMArena Hard Prompts | 1266 | — |
| ForecastBench | 57.5 | — |
Math Qwen3.5-9B leads
Llama 4 Scout: 18.4 (#280), Qwen3.5-9B: 33.5 (#174)
| Benchmark | Llama 4 Scout | Qwen3.5-9B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 7.8% | 61.7% |
| MathArena Final-Answer Competitions | — | 48.5% |
| Omni-MATH | 37.3% | — |
| LMArena Math | 1286 | — |
| MATH Level 5 | 62.3% | — |
| FrontierMath (Feb 2025 set) | 0% | — |
Knowledge Qwen3.5-9B leads
Llama 4 Scout: 31.8 (#208), Qwen3.5-9B: 46.0 (#77)
| Benchmark | Llama 4 Scout | Qwen3.5-9B |
|---|---|---|
| GPQA Diamond | 51.8% | 79% |
| MMLU-Pro | 74.2% | — |
| Vectara Hallucination Rate | 7.7% | — |
| GPQA (HELM) | 50.7% | — |
| LMArena Expert | 1236 | — |
Multimodal Not comparable
Llama 4 Scout: 31.6 (#98), Qwen3.5-9B: —
| Benchmark | Llama 4 Scout | Qwen3.5-9B |
|---|---|---|
| LMArena Vision | 1118 | — |
| SpatialViz-Bench | 34.2% | — |
Multilingual Not comparable
Llama 4 Scout: 41.3 (#207), Qwen3.5-9B: —
| Benchmark | Llama 4 Scout | Qwen3.5-9B |
|---|---|---|
| LMArena Non-English | 1253 | — |
| LMArena Chinese | 1256 | — |
| LMArena French | 1283 | — |
| LMArena German | 1274 | — |
| LMArena Japanese | 1209 | — |
| LMArena Korean | 1207 | — |
| LMArena Russian | 1263 | — |
| LMArena Spanish | 1277 | — |
Instruction Following Not comparable
Llama 4 Scout: 66.0 (#212), Qwen3.5-9B: —
| Benchmark | Llama 4 Scout | Qwen3.5-9B |
|---|---|---|
| IFEval | 81.8% | — |
| LMArena Instruction Following | 1248 | — |
Long Context Not comparable
Llama 4 Scout: 27.5 (#289), Qwen3.5-9B: —
| Benchmark | Llama 4 Scout | Qwen3.5-9B |
|---|---|---|
| Fiction.LiveBench | 36% | — |
| LMArena Longer Query | 1265 | — |
Writing & Preference Not comparable
Llama 4 Scout: 37.1 (#256), Qwen3.5-9B: —
| Benchmark | Llama 4 Scout | Qwen3.5-9B |
|---|---|---|
| LMArena Text | 1279 | — |
| LMArena Creative Writing | 1249 | — |
| EQ-Bench Creative Writing | 783 | — |
| WildBench | 78% | — |
| LMArena Multi-Turn | 1281 | — |
Frequently asked questions
Is Llama 4 Scout better than Qwen3.5-9B?
Qwen3.5-9B is the stronger model overall, scoring 33.3 to 27.4 on the Noometry Index.
Which is cheaper, Llama 4 Scout 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 4 Scout lists at $0.10 and $0.30.
Is Llama 4 Scout or Qwen3.5-9B better for coding?
Qwen3.5-9B scores higher on coding benchmarks: 35.3 versus 19.9 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 4 Scout and Qwen3.5-9B share?
7 benchmarks have published results for both models. Llama 4 Scout has 43 scored results on Noometry and Qwen3.5-9B has 10.