Model comparison
Llama 4 Scout vs Qwen3 Coder Next
Qwen3 Coder Next is the stronger model overall, scoring 34.3 to 27.7 on the Noometry Index. Llama 4 Scout costs 1.9× less per token, which makes it the better buy when Qwen3 Coder Next's lead doesn't matter for your workload.
Last verified . 2 shared benchmarks.
Meta
Alibaba (Qwen)Summary
- They share 2 benchmarks with published results for both. Llama 4 Scout scores higher in 0 categories and Qwen3 Coder Next in 2 categories; 2 gaps are clear of the uncertainty.
- The widest gap is in coding, where Qwen3 Coder Next leads 36.3 to 20.2.
- The biggest single-benchmark swing is SciCode: 17% for Llama 4 Scout and 32.3% for Qwen3 Coder Next.
- Llama 4 Scout is cheaper at $0.10 / $0.30 per million input/output tokens, against $0.12 / $0.80 for Qwen3 Coder Next.
- Qwen3 Coder Next accepts more context: 262K tokens versus 128K.
Side by side
| Llama 4 Scout | Qwen3 Coder Next | |
|---|---|---|
| Provider | Meta | Alibaba (Qwen) |
| Noometry Index | 27.7 | 34.3 |
| Released | 2025-04-05 | 2026-02-02 |
| Weights | Open | Open |
| Context window | 128K | 262K |
| Max output | 4K | 66K |
| Input $ / M tokens | $0.10 | $0.12 |
| Output $ / M tokens | $0.30 | $0.80 |
| Results tracked | 43 | 3 |
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Category by category
Coding Qwen3 Coder Next leads
Llama 4 Scout: 20.2 (#339), Qwen3 Coder Next: 36.3 (#210)
| Benchmark | Llama 4 Scout | Qwen3 Coder Next |
|---|---|---|
| SciCode | 17% | 32.3% |
| SWE-bench Verified (bash only) | 9.1% | — |
| WeirdML | — | 34.4% |
| LMArena Coding | 1286 | — |
| BigCodeBench Complete | 43.1% | — |
Agentic & Tool Use Not comparable
Llama 4 Scout: 24.6 (#119), Qwen3 Coder Next: —
| Benchmark | Llama 4 Scout | Qwen3 Coder Next |
|---|---|---|
| Berkeley Function Calling Leaderboard | 28.1% | — |
Reasoning Qwen3 Coder Next leads
Llama 4 Scout: 9.1 (#345), Qwen3 Coder Next: 22.4 (#196)
| Benchmark | Llama 4 Scout | Qwen3 Coder Next |
|---|---|---|
| CritPt | 0% | 0% |
| ARC-AGI-2 | 0% | — |
| Kagi LLM Benchmark | 36.9% | — |
| ARC-AGI-1 | 0.5% | — |
| LMArena Hard Prompts | 1266 | — |
| DTBench | 57.9% | — |
| LMCA | 12% | — |
| Epoch Capabilities Index | 129.64 | — |
| ForecastBench | 57.5 | — |
Math Not comparable
Llama 4 Scout: 19.6 (#286), Qwen3 Coder Next: —
| Benchmark | Llama 4 Scout | Qwen3 Coder Next |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 7.8% | — |
| Omni-MATH | 37.3% | — |
| LMArena Math | 1287 | — |
| MATH Level 5 | 62.3% | — |
| FrontierMath (Feb 2025 set) | 0% | — |
Knowledge Not comparable
Llama 4 Scout: 31.9 (#217), Qwen3 Coder Next: —
| Benchmark | Llama 4 Scout | Qwen3 Coder Next |
|---|---|---|
| GPQA Diamond | 51.8% | — |
| MMLU-Pro | 74.2% | — |
| Vectara Hallucination Rate | 7.7% | — |
| GPQA (HELM) | 50.7% | — |
| LMArena Expert | 1235 | — |
Multimodal Not comparable
Llama 4 Scout: 32.2 (#102), Qwen3 Coder Next: —
| Benchmark | Llama 4 Scout | Qwen3 Coder Next |
|---|---|---|
| LMArena Vision | 1118 | — |
| SpatialViz-Bench | 34.2% | — |
Multilingual Not comparable
Llama 4 Scout: 41.0 (#212), Qwen3 Coder Next: —
| Benchmark | Llama 4 Scout | Qwen3 Coder Next |
|---|---|---|
| LMArena Non-English | 1252 | — |
| LMArena Chinese | 1255 | — |
| LMArena French | 1282 | — |
| LMArena German | 1272 | — |
| LMArena Japanese | 1206 | — |
| LMArena Korean | 1207 | — |
| LMArena Russian | 1263 | — |
| LMArena Spanish | 1278 | — |
Instruction Following Not comparable
Llama 4 Scout: 65.8 (#217), Qwen3 Coder Next: —
| Benchmark | Llama 4 Scout | Qwen3 Coder Next |
|---|---|---|
| IFEval | 81.8% | — |
| LMArena Instruction Following | 1248 | — |
Long Context Not comparable
Llama 4 Scout: 27.5 (#294), Qwen3 Coder Next: —
| Benchmark | Llama 4 Scout | Qwen3 Coder Next |
|---|---|---|
| Fiction.LiveBench | 36% | — |
| LMArena Longer Query | 1265 | — |
Writing & Preference Not comparable
Llama 4 Scout: 37.0 (#261), Qwen3 Coder Next: —
| Benchmark | Llama 4 Scout | Qwen3 Coder Next |
|---|---|---|
| LMArena Text | 1279 | — |
| LMArena Creative Writing | 1249 | — |
| EQ-Bench Creative Writing | 783 | — |
| WildBench | 78% | — |
| LMArena Multi-Turn | 1280 | — |
Frequently asked questions
Is Llama 4 Scout better than Qwen3 Coder Next?
Qwen3 Coder Next is the stronger model overall, scoring 34.3 to 27.7 on the Noometry Index. Llama 4 Scout costs 1.9× less per token, which makes it the better buy when Qwen3 Coder Next's lead doesn't matter for your workload.
Which is cheaper, Llama 4 Scout or Qwen3 Coder Next?
Llama 4 Scout is cheaper. It lists at $0.10 per million input tokens and $0.30 per million output tokens; Qwen3 Coder Next lists at $0.12 and $0.80.
Is Llama 4 Scout or Qwen3 Coder Next better for coding?
Qwen3 Coder Next scores higher on coding benchmarks: 36.3 versus 20.2 in the Noometry coding category.
Which has the bigger context window?
Qwen3 Coder Next does, with 262K tokens against 128K.
How many benchmarks do Llama 4 Scout and Qwen3 Coder Next share?
2 benchmarks have published results for both models. Llama 4 Scout has 43 scored results on Noometry and Qwen3 Coder Next has 3.