Model comparison
Llama 4 Scout vs Qwen3.5-Flash
Qwen3.5-Flash is the stronger model overall, scoring 42.3 to 27.4 on the Noometry Index.
Last verified . 24 shared benchmarks.
Meta
Alibaba (Qwen)Summary
- They share 24 benchmarks with published results for both. Llama 4 Scout scores higher in 0 categories and Qwen3.5-Flash in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Qwen3.5-Flash leads 32.9 to 9.2.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 7.8% for Llama 4 Scout and 84.4% for Qwen3.5-Flash.
- Llama 4 Scout is cheaper at $0.10 / $0.30 per million input/output tokens, against $0.10 / $0.40 for Qwen3.5-Flash.
- Qwen3.5-Flash accepts more context: 1M tokens versus 128K.
- Llama 4 Scout has downloadable open weights; the other is API-only.
Side by side
| Llama 4 Scout | Qwen3.5-Flash | |
|---|---|---|
| Provider | Meta | Alibaba (Qwen) |
| Noometry Index | 27.4 | 42.3 |
| Released | 2025-04-05 | 2026-02-23 |
| Weights | Open | Proprietary |
| Context window | 128K | 1M |
| Max output | 4K | 66K |
| Input $ / M tokens | $0.10 | $0.10 |
| Output $ / M tokens | $0.30 | $0.40 |
| Results tracked | 43 | 32 |
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Category by category
Coding Qwen3.5-Flash leads
Llama 4 Scout: 19.9 (#334), Qwen3.5-Flash: 33.9 (#234)
| Benchmark | Llama 4 Scout | Qwen3.5-Flash |
|---|---|---|
| LMArena Coding | 1286 | 1412 |
| SWE-bench Verified (bash only) | 9.1% | — |
| LMArena WebDev | — | 1243 |
| SciCode | 17% | — |
| BigCodeBench Complete | 43.1% | — |
| ALE-Bench | — | 221.8 |
Agentic & Tool Use Not comparable
Llama 4 Scout: 24.4 (#109), Qwen3.5-Flash: —
| Benchmark | Llama 4 Scout | Qwen3.5-Flash |
|---|---|---|
| Berkeley Function Calling Leaderboard | 28.1% | — |
| Vending-Bench 2 | — | 462.69 |
Reasoning Qwen3.5-Flash leads
Llama 4 Scout: 9.2 (#339), Qwen3.5-Flash: 32.9 (#63)
| Benchmark | Llama 4 Scout | Qwen3.5-Flash |
|---|---|---|
| LMArena Hard Prompts | 1266 | 1404 |
| DTBench | 57.9% | 82.9% |
| LMCA | 12% | 29.1% |
| Epoch Capabilities Index | 129.64 | 143.98 |
| ARC-AGI-2 | 0% | — |
| Kagi LLM Benchmark | 36.9% | — |
| ARC-AGI-1 | 0.5% | — |
| CritPt | 0% | — |
| Chess Puzzles | — | 21% |
| Mystery Game Puzzles | — | 20% |
| ForecastBench | 57.5 | — |
Math Qwen3.5-Flash leads
Llama 4 Scout: 18.4 (#280), Qwen3.5-Flash: 36.3 (#117)
| Benchmark | Llama 4 Scout | Qwen3.5-Flash |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 7.8% | 84.4% |
| LMArena Math | 1286 | 1407 |
| FrontierMath (Feb 2025 set) | 0% | 6.2% |
| FrontierMath (Tiers 1-3) | — | 18.2% |
| Omni-MATH | 37.3% | — |
| MATH Level 5 | 62.3% | — |
| FrontierMath Tier 4 (v1) | — | 0% |
Knowledge Qwen3.5-Flash leads
Llama 4 Scout: 31.8 (#208), Qwen3.5-Flash: 43.2 (#84)
| Benchmark | Llama 4 Scout | Qwen3.5-Flash |
|---|---|---|
| GPQA Diamond | 51.8% | 82.3% |
| Vectara Hallucination Rate | 7.7% | 10.5% |
| LMArena Expert | 1236 | 1408 |
| SimpleQA Verified | — | 20.3% |
| MMLU-Pro | 74.2% | — |
| GPQA (HELM) | 50.7% | — |
Multimodal Not comparable
Llama 4 Scout: 31.6 (#98), Qwen3.5-Flash: —
| Benchmark | Llama 4 Scout | Qwen3.5-Flash |
|---|---|---|
| LMArena Vision | 1118 | — |
| SpatialViz-Bench | 34.2% | — |
Multilingual Qwen3.5-Flash leads
Llama 4 Scout: 41.3 (#207), Qwen3.5-Flash: 50.7 (#116)
| Benchmark | Llama 4 Scout | Qwen3.5-Flash |
|---|---|---|
| LMArena Non-English | 1253 | 1385 |
| LMArena Chinese | 1256 | 1446 |
| LMArena French | 1283 | 1412 |
| LMArena German | 1274 | 1389 |
| LMArena Japanese | 1209 | 1369 |
| LMArena Korean | 1207 | 1344 |
| LMArena Russian | 1263 | 1379 |
| LMArena Spanish | 1277 | 1399 |
Instruction Following Qwen3.5-Flash leads
Llama 4 Scout: 66.0 (#212), Qwen3.5-Flash: 72.8 (#133)
| Benchmark | Llama 4 Scout | Qwen3.5-Flash |
|---|---|---|
| LMArena Instruction Following | 1248 | 1374 |
| IFEval | 81.8% | — |
Long Context Qwen3.5-Flash leads
Llama 4 Scout: 27.5 (#289), Qwen3.5-Flash: 42.4 (#119)
| Benchmark | Llama 4 Scout | Qwen3.5-Flash |
|---|---|---|
| LMArena Longer Query | 1265 | 1392 |
| Fiction.LiveBench | 36% | — |
Writing & Preference Qwen3.5-Flash leads
Llama 4 Scout: 37.1 (#256), Qwen3.5-Flash: 58.2 (#117)
| Benchmark | Llama 4 Scout | Qwen3.5-Flash |
|---|---|---|
| LMArena Text | 1279 | 1397 |
| LMArena Creative Writing | 1249 | 1343 |
| LMArena Multi-Turn | 1281 | 1393 |
| EQ-Bench Creative Writing | 783 | — |
| WildBench | 78% | — |
Frequently asked questions
Is Llama 4 Scout better than Qwen3.5-Flash?
Qwen3.5-Flash is the stronger model overall, scoring 42.3 to 27.4 on the Noometry Index.
Which is cheaper, Llama 4 Scout or Qwen3.5-Flash?
Llama 4 Scout is cheaper. It lists at $0.10 per million input tokens and $0.30 per million output tokens; Qwen3.5-Flash lists at $0.10 and $0.40.
Is Llama 4 Scout or Qwen3.5-Flash better for coding?
Qwen3.5-Flash scores higher on coding benchmarks: 33.9 versus 19.9 in the Noometry coding category.
Which has the bigger context window?
Qwen3.5-Flash does, with 1M tokens against 128K.
How many benchmarks do Llama 4 Scout and Qwen3.5-Flash share?
24 benchmarks have published results for both models. Llama 4 Scout has 43 scored results on Noometry and Qwen3.5-Flash has 32.