Model comparison
Llama 4 Scout vs Qwen3.8 27B
Qwen3.8 27B is the stronger model overall, scoring 46.0 to 27.7 on the Noometry Index. Llama 4 Scout costs 7.4× less per token, which makes it the better buy when Qwen3.8 27B's lead doesn't matter for your workload.
Last verified . 26 shared benchmarks.
Meta
Alibaba (Qwen)Summary
- They share 26 benchmarks with published results for both. Llama 4 Scout scores higher in 0 categories and Qwen3.8 27B in 10 categories; 10 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Qwen3.8 27B leads 41.0 to 9.1.
- The biggest single-benchmark swing is ARC-AGI-1: 0.5% for Llama 4 Scout and 87.5% for Qwen3.8 27B.
- Llama 4 Scout is cheaper at $0.10 / $0.30 per million input/output tokens, against $0.99 / $1.49 for Qwen3.8 27B.
- Qwen3.8 27B accepts more context: 262K tokens versus 128K.
Side by side
| Llama 4 Scout | Qwen3.8 27B | |
|---|---|---|
| Provider | Meta | Alibaba (Qwen) |
| Noometry Index | 27.7 | 46.0 |
| Released | 2025-04-05 | 2026-08-14 |
| Weights | Open | Open |
| Context window | 128K | 262K |
| Max output | 4K | 33K |
| Input $ / M tokens | $0.10 | $0.99 |
| Output $ / M tokens | $0.30 | $1.49 |
| Results tracked | 43 | 31 |
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Category by category
Coding Qwen3.8 27B leads
Llama 4 Scout: 20.2 (#339), Qwen3.8 27B: 50.5 (#44)
| Benchmark | Llama 4 Scout | Qwen3.8 27B |
|---|---|---|
| SciCode | 17% | 46.6% |
| LMArena Coding | 1286 | 1482 |
| SWE-bench Verified (bash only) | 9.1% | — |
| LMArena WebDev | — | 1593 |
| BigCodeBench Complete | 43.1% | — |
Agentic & Tool Use Qwen3.8 27B leads
Llama 4 Scout: 24.6 (#119), Qwen3.8 27B: 32.9 (#57)
| Benchmark | Llama 4 Scout | Qwen3.8 27B |
|---|---|---|
| APEX-Agents | — | 47.5% |
| Berkeley Function Calling Leaderboard | 28.1% | — |
Reasoning Qwen3.8 27B leads
Llama 4 Scout: 9.1 (#345), Qwen3.8 27B: 41.0 (#54)
| Benchmark | Llama 4 Scout | Qwen3.8 27B |
|---|---|---|
| ARC-AGI-2 | 0% | 42.4% |
| ARC-AGI-1 | 0.5% | 87.5% |
| CritPt | 0% | 5.4% |
| LMArena Hard Prompts | 1266 | 1460 |
| DTBench | 57.9% | 88% |
| LMCA | 12% | 41.4% |
| Epoch Capabilities Index | 129.64 | 149.38 |
| Kagi LLM Benchmark | 36.9% | — |
| NYT Connections (extended) | — | 54.5% |
| Surface Evolver Bench | — | 45% |
| ForecastBench | 57.5 | — |
Math Qwen3.8 27B leads
Llama 4 Scout: 19.6 (#286), Qwen3.8 27B: 37.1 (#161)
| Benchmark | Llama 4 Scout | Qwen3.8 27B |
|---|---|---|
| LMArena Math | 1287 | 1456 |
| OTIS Mock AIME 2024-2025 | 7.8% | — |
| ProofBench | — | 16% |
| Omni-MATH | 37.3% | — |
| MATH Level 5 | 62.3% | — |
| FrontierMath (Feb 2025 set) | 0% | — |
Knowledge Qwen3.8 27B leads
Llama 4 Scout: 31.9 (#217), Qwen3.8 27B: 41.6 (#109)
| Benchmark | Llama 4 Scout | Qwen3.8 27B |
|---|---|---|
| LMArena Expert | 1235 | 1482 |
| GPQA Diamond | 51.8% | — |
| MMLU-Pro | 74.2% | — |
| Vectara Hallucination Rate | 7.7% | — |
| GPQA (HELM) | 50.7% | — |
Multimodal Qwen3.8 27B leads
Llama 4 Scout: 32.2 (#102), Qwen3.8 27B: 41.3 (#37)
| Benchmark | Llama 4 Scout | Qwen3.8 27B |
|---|---|---|
| LMArena Vision | 1118 | 1271 |
| SpatialViz-Bench | 34.2% | — |
Multilingual Qwen3.8 27B leads
Llama 4 Scout: 41.0 (#212), Qwen3.8 27B: 53.7 (#60)
| Benchmark | Llama 4 Scout | Qwen3.8 27B |
|---|---|---|
| LMArena Non-English | 1252 | 1430 |
| LMArena Chinese | 1255 | 1504 |
| LMArena French | 1282 | 1465 |
| LMArena German | 1272 | 1438 |
| LMArena Japanese | 1206 | 1384 |
| LMArena Korean | 1207 | 1393 |
| LMArena Russian | 1263 | 1415 |
| LMArena Spanish | 1278 | 1448 |
Instruction Following Qwen3.8 27B leads
Llama 4 Scout: 65.8 (#217), Qwen3.8 27B: 75.8 (#53)
| Benchmark | Llama 4 Scout | Qwen3.8 27B |
|---|---|---|
| LMArena Instruction Following | 1248 | 1439 |
| IFEval | 81.8% | — |
Long Context Qwen3.8 27B leads
Llama 4 Scout: 27.5 (#294), Qwen3.8 27B: 44.3 (#70)
| Benchmark | Llama 4 Scout | Qwen3.8 27B |
|---|---|---|
| LMArena Longer Query | 1265 | 1450 |
| Fiction.LiveBench | 36% | — |
Writing & Preference Qwen3.8 27B leads
Llama 4 Scout: 37.0 (#261), Qwen3.8 27B: 65.8 (#43)
| Benchmark | Llama 4 Scout | Qwen3.8 27B |
|---|---|---|
| LMArena Text | 1279 | 1441 |
| LMArena Creative Writing | 1249 | 1384 |
| EQ-Bench Creative Writing | 783 | 1671 |
| LMArena Multi-Turn | 1280 | 1441 |
| WildBench | 78% | — |
Frequently asked questions
Is Llama 4 Scout better than Qwen3.8 27B?
Qwen3.8 27B is the stronger model overall, scoring 46.0 to 27.7 on the Noometry Index. Llama 4 Scout costs 7.4× less per token, which makes it the better buy when Qwen3.8 27B's lead doesn't matter for your workload.
Which is cheaper, Llama 4 Scout or Qwen3.8 27B?
Llama 4 Scout is cheaper. It lists at $0.10 per million input tokens and $0.30 per million output tokens; Qwen3.8 27B lists at $0.99 and $1.49.
Is Llama 4 Scout or Qwen3.8 27B better for coding?
Qwen3.8 27B scores higher on coding benchmarks: 50.5 versus 20.2 in the Noometry coding category.
Which has the bigger context window?
Qwen3.8 27B does, with 262K tokens against 128K.
How many benchmarks do Llama 4 Scout and Qwen3.8 27B share?
26 benchmarks have published results for both models. Llama 4 Scout has 43 scored results on Noometry and Qwen3.8 27B has 31.