Model comparison
DeepSeek-V3.1 vs Llama 4 Scout
DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 27.7 on the Noometry Index. Llama 4 Scout costs 2.8× less per token, which makes it the better buy when DeepSeek-V3.1's lead doesn't matter for your workload.
Last verified . 25 shared benchmarks.
DeepSeek
MetaSummary
- They share 25 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 8 categories and Llama 4 Scout in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where DeepSeek-V3.1 leads 60.3 to 37.0.
- The biggest single-benchmark swing is DTBench: 82.7% for DeepSeek-V3.1 and 57.9% for Llama 4 Scout.
- Llama 4 Scout is cheaper at $0.10 / $0.30 per million input/output tokens, against $0.25 / $0.95 for DeepSeek-V3.1.
- DeepSeek-V3.1 accepts more context: 164K tokens versus 128K.
Side by side
| DeepSeek-V3.1 | Llama 4 Scout | |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 42.8 | 27.7 |
| Released | 2025-08-21 | 2025-04-05 |
| Weights | Open | Open |
| Context window | 164K | 128K |
| Max output | 8K | 4K |
| Input $ / M tokens | $0.25 | $0.10 |
| Output $ / M tokens | $0.95 | $0.30 |
| Results tracked | 27 | 43 |
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Category by category
Coding DeepSeek-V3.1 leads
DeepSeek-V3.1: 40.3 (#144), Llama 4 Scout: 20.2 (#339)
| Benchmark | DeepSeek-V3.1 | Llama 4 Scout |
|---|---|---|
| LMArena Coding | 1417 | 1286 |
| SWE-bench Verified (bash only) | — | 9.1% |
| SciCode | — | 17% |
| WeirdML | 38.4% | — |
| BigCodeBench Complete | — | 43.1% |
Agentic & Tool Use Not comparable
DeepSeek-V3.1: —, Llama 4 Scout: 24.6 (#119)
| Benchmark | DeepSeek-V3.1 | Llama 4 Scout |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 28.1% |
Reasoning DeepSeek-V3.1 leads
DeepSeek-V3.1: 27.9 (#110), Llama 4 Scout: 9.1 (#345)
| Benchmark | DeepSeek-V3.1 | Llama 4 Scout |
|---|---|---|
| Kagi LLM Benchmark | 53.2% | 36.9% |
| LMArena Hard Prompts | 1417 | 1266 |
| DTBench | 82.7% | 57.9% |
| LMCA | 24.3% | 12% |
| Epoch Capabilities Index | 139.92 | 129.64 |
| ForecastBench | 58 | 57.5 |
| ARC-AGI-2 | — | 0% |
| SimpleBench | 40% | — |
| ARC-AGI-1 | — | 0.5% |
| CritPt | — | 0% |
Math DeepSeek-V3.1 leads
DeepSeek-V3.1: 38.9 (#122), Llama 4 Scout: 19.6 (#286)
| Benchmark | DeepSeek-V3.1 | Llama 4 Scout |
|---|---|---|
| LMArena Math | 1420 | 1287 |
| OTIS Mock AIME 2024-2025 | — | 7.8% |
| Omni-MATH | — | 37.3% |
| MATH Level 5 | — | 62.3% |
| FrontierMath (Feb 2025 set) | — | 0% |
Knowledge DeepSeek-V3.1 leads
DeepSeek-V3.1: 43.7 (#90), Llama 4 Scout: 31.9 (#217)
| Benchmark | DeepSeek-V3.1 | Llama 4 Scout |
|---|---|---|
| Vectara Hallucination Rate | 5.5% | 7.7% |
| LMArena Expert | 1405 | 1235 |
| GPQA Diamond | — | 51.8% |
| MMLU-Pro | — | 74.2% |
| GPQA (HELM) | — | 50.7% |
Multimodal Not comparable
DeepSeek-V3.1: —, Llama 4 Scout: 32.2 (#102)
| Benchmark | DeepSeek-V3.1 | Llama 4 Scout |
|---|---|---|
| LMArena Vision | — | 1118 |
| SpatialViz-Bench | — | 34.2% |
Multilingual DeepSeek-V3.1 leads
DeepSeek-V3.1: 51.6 (#106), Llama 4 Scout: 41.0 (#212)
| Benchmark | DeepSeek-V3.1 | Llama 4 Scout |
|---|---|---|
| LMArena Non-English | 1400 | 1252 |
| LMArena Chinese | 1469 | 1255 |
| LMArena French | 1447 | 1282 |
| LMArena German | 1411 | 1272 |
| LMArena Japanese | 1378 | 1206 |
| LMArena Korean | 1337 | 1207 |
| LMArena Russian | 1405 | 1263 |
| LMArena Spanish | 1431 | 1278 |
Instruction Following DeepSeek-V3.1 leads
DeepSeek-V3.1: 73.9 (#110), Llama 4 Scout: 65.8 (#217)
| Benchmark | DeepSeek-V3.1 | Llama 4 Scout |
|---|---|---|
| LMArena Instruction Following | 1400 | 1248 |
| IFEval | — | 81.8% |
Long Context DeepSeek-V3.1 leads
DeepSeek-V3.1: 36.3 (#232), Llama 4 Scout: 27.5 (#294)
| Benchmark | DeepSeek-V3.1 | Llama 4 Scout |
|---|---|---|
| Fiction.LiveBench | 52.8% | 36% |
| LMArena Longer Query | 1422 | 1265 |
Writing & Preference DeepSeek-V3.1 leads
DeepSeek-V3.1: 60.3 (#98), Llama 4 Scout: 37.0 (#261)
| Benchmark | DeepSeek-V3.1 | Llama 4 Scout |
|---|---|---|
| LMArena Text | 1420 | 1279 |
| LMArena Creative Writing | 1401 | 1249 |
| EQ-Bench Creative Writing | 1436 | 783 |
| LMArena Multi-Turn | 1408 | 1280 |
| WildBench | — | 78% |
Frequently asked questions
Is DeepSeek-V3.1 better than Llama 4 Scout?
DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 27.7 on the Noometry Index. Llama 4 Scout costs 2.8× less per token, which makes it the better buy when DeepSeek-V3.1's lead doesn't matter for your workload.
Which is cheaper, DeepSeek-V3.1 or Llama 4 Scout?
Llama 4 Scout is cheaper. It lists at $0.10 per million input tokens and $0.30 per million output tokens; DeepSeek-V3.1 lists at $0.25 and $0.95.
Is DeepSeek-V3.1 or Llama 4 Scout better for coding?
DeepSeek-V3.1 scores higher on coding benchmarks: 40.3 versus 20.2 in the Noometry coding category.
Which has the bigger context window?
DeepSeek-V3.1 does, with 164K tokens against 128K.
How many benchmarks do DeepSeek-V3.1 and Llama 4 Scout share?
25 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Llama 4 Scout has 43.