Model comparison
DeepSeek-R1 vs Llama 4 Scout
DeepSeek-R1 is the stronger model overall, scoring 42.3 to 27.7 on the Noometry Index. Llama 4 Scout costs 6.1× less per token, which makes it the better buy when DeepSeek-R1's lead doesn't matter for your workload.
Last verified . 35 shared benchmarks.
DeepSeek
MetaSummary
- They share 35 benchmarks with published results for both. DeepSeek-R1 scores higher in 9 categories and Llama 4 Scout in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in coding, where DeepSeek-R1 leads 46.3 to 20.2.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 66.4% for DeepSeek-R1 and 7.8% for Llama 4 Scout.
- Llama 4 Scout is cheaper at $0.10 / $0.30 per million input/output tokens, against $0.50 / $2.15 for DeepSeek-R1.
- DeepSeek-R1 accepts more context: 164K tokens versus 128K.
- Llama 4 Scout has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-R1 | Llama 4 Scout | |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 42.3 | 27.7 |
| Released | 2025-01-20 | 2025-04-05 |
| Weights | Proprietary | Open |
| Context window | 164K | 128K |
| Max output | 64K | 4K |
| Input $ / M tokens | $0.50 | $0.10 |
| Output $ / M tokens | $2.15 | $0.30 |
| Results tracked | 52 | 43 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding DeepSeek-R1 leads
DeepSeek-R1: 46.3 (#68), Llama 4 Scout: 20.2 (#339)
| Benchmark | DeepSeek-R1 | Llama 4 Scout |
|---|---|---|
| SciCode | 35.7% | 17% |
| LMArena Coding | 1427 | 1286 |
| SWE-bench Verified (bash only) | — | 9.1% |
| Aider Polyglot | 71.4% | — |
| WeirdML | 41.6% | — |
| LiveBench Coding | 66.7% | — |
| BigCodeBench Complete | — | 43.1% |
| ALE-Bench | 804.12 | — |
| AlgoTune | 1.7 | — |
Agentic & Tool Use DeepSeek-R1 leads
DeepSeek-R1: 30.7 (#75), Llama 4 Scout: 24.6 (#119)
| Benchmark | DeepSeek-R1 | Llama 4 Scout |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 28.1% |
| DeepResearch Bench | 35.1% | — |
| BALROG | 34.9% | — |
| METR Time Horizons | 53.8% | — |
Reasoning DeepSeek-R1 leads
DeepSeek-R1: 18.6 (#278), Llama 4 Scout: 9.1 (#345)
| Benchmark | DeepSeek-R1 | Llama 4 Scout |
|---|---|---|
| ARC-AGI-2 | 1.3% | 0% |
| Kagi LLM Benchmark | 69.4% | 36.9% |
| ARC-AGI-1 | 21.2% | 0.5% |
| CritPt | 1.1% | 0% |
| LMArena Hard Prompts | 1416 | 1266 |
| Epoch Capabilities Index | 141.29 | 129.64 |
| ForecastBench | 60 | 57.5 |
| SimpleBench | 40.8% | — |
| LiveBench Reasoning | 83.2% | — |
| DTBench | — | 57.9% |
| LiveBench Data Analysis | 69.8% | — |
| LMCA | — | 12% |
| LiveBench | 71.6% | — |
Math DeepSeek-R1 leads
DeepSeek-R1: 43.8 (#79), Llama 4 Scout: 19.6 (#286)
| Benchmark | DeepSeek-R1 | Llama 4 Scout |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 66.4% | 7.8% |
| Omni-MATH | 42.4% | 37.3% |
| LMArena Math | 1400 | 1287 |
| MATH Level 5 | 96.6% | 62.3% |
| LiveBench Math | 80.7% | — |
| FrontierMath (Feb 2025 set) | — | 0% |
Knowledge DeepSeek-R1 leads
DeepSeek-R1: 44.5 (#87), Llama 4 Scout: 31.9 (#217)
| Benchmark | DeepSeek-R1 | Llama 4 Scout |
|---|---|---|
| GPQA Diamond | 76.3% | 51.8% |
| MMLU-Pro | 79.3% | 74.2% |
| Vectara Hallucination Rate | 11.3% | 7.7% |
| GPQA (HELM) | 66.6% | 50.7% |
| LMArena Expert | 1394 | 1235 |
| Confabulations | 12.7% | — |
Multimodal Not comparable
DeepSeek-R1: —, Llama 4 Scout: 32.2 (#102)
| Benchmark | DeepSeek-R1 | Llama 4 Scout |
|---|---|---|
| LMArena Vision | — | 1118 |
| SpatialViz-Bench | — | 34.2% |
Multilingual DeepSeek-R1 leads
DeepSeek-R1: 52.4 (#85), Llama 4 Scout: 41.0 (#212)
| Benchmark | DeepSeek-R1 | Llama 4 Scout |
|---|---|---|
| LMArena Non-English | 1412 | 1252 |
| LMArena Chinese | 1442 | 1255 |
| LMArena French | 1417 | 1282 |
| LMArena German | 1404 | 1272 |
| LMArena Japanese | 1391 | 1206 |
| LMArena Korean | 1360 | 1207 |
| LMArena Russian | 1423 | 1263 |
| LMArena Spanish | 1411 | 1278 |
Instruction Following DeepSeek-R1 leads
DeepSeek-R1: 72.0 (#143), Llama 4 Scout: 65.8 (#217)
| Benchmark | DeepSeek-R1 | Llama 4 Scout |
|---|---|---|
| IFEval | 78.4% | 81.8% |
| LMArena Instruction Following | 1382 | 1248 |
| LiveBench Instruction Following | 80.5% | — |
Long Context DeepSeek-R1 leads
DeepSeek-R1: 45.4 (#36), Llama 4 Scout: 27.5 (#294)
| Benchmark | DeepSeek-R1 | Llama 4 Scout |
|---|---|---|
| Fiction.LiveBench | 75% | 36% |
| LMArena Longer Query | 1391 | 1265 |
Writing & Preference DeepSeek-R1 leads
DeepSeek-R1: 61.4 (#88), Llama 4 Scout: 37.0 (#261)
| Benchmark | DeepSeek-R1 | Llama 4 Scout |
|---|---|---|
| LMArena Text | 1428 | 1279 |
| LMArena Creative Writing | 1405 | 1249 |
| EQ-Bench Creative Writing | 1500 | 783 |
| WildBench | 82.8% | 78% |
| LMArena Multi-Turn | 1405 | 1280 |
| Short-Story Creative Writing | 83% | — |
| LiveBench Language | 48.5% | — |
Frequently asked questions
Is DeepSeek-R1 better than Llama 4 Scout?
DeepSeek-R1 is the stronger model overall, scoring 42.3 to 27.7 on the Noometry Index. Llama 4 Scout costs 6.1× less per token, which makes it the better buy when DeepSeek-R1's lead doesn't matter for your workload.
Which is cheaper, DeepSeek-R1 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-R1 lists at $0.50 and $2.15.
Is DeepSeek-R1 or Llama 4 Scout better for coding?
DeepSeek-R1 scores higher on coding benchmarks: 46.3 versus 20.2 in the Noometry coding category.
Which has the bigger context window?
DeepSeek-R1 does, with 164K tokens against 128K.
How many benchmarks do DeepSeek-R1 and Llama 4 Scout share?
35 benchmarks have published results for both models. DeepSeek-R1 has 52 scored results on Noometry and Llama 4 Scout has 43.