Model comparison
DeepSeek-V3 vs Llama 4 Maverick
DeepSeek-V3 is the stronger model overall, scoring 39.5 to 30.9 on the Noometry Index.
Last verified . 43 shared benchmarks.
DeepSeek
MetaSummary
- They share 43 benchmarks with published results for both. DeepSeek-V3 scores higher in 8 categories and Llama 4 Maverick in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where DeepSeek-V3 leads 57.4 to 38.8.
- The biggest single-benchmark swing is Aider Polyglot: 55.1% for DeepSeek-V3 and 15.6% for Llama 4 Maverick.
- Llama 4 Maverick is cheaper at $0.19 / $0.65 per million input/output tokens, against $0.24 / $0.90 for DeepSeek-V3.
- DeepSeek-V3 accepts more context: 164K tokens versus 128K.
Side by side
| DeepSeek-V3 | Llama 4 Maverick | |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 39.5 | 30.9 |
| Released | 2024-12-26 | 2025-04-05 |
| Weights | Open | Open |
| Context window | 164K | 128K |
| Max output | 164K | 4K |
| Input $ / M tokens | $0.24 | $0.19 |
| Output $ / M tokens | $0.90 | $0.65 |
| Results tracked | 60 | 54 |
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Category by category
Coding DeepSeek-V3 leads
DeepSeek-V3: 42.3 (#106), Llama 4 Maverick: 26.6 (#324)
| Benchmark | DeepSeek-V3 | Llama 4 Maverick |
|---|---|---|
| Aider Polyglot | 55.1% | 15.6% |
| SciCode | 35.8% | 33.1% |
| WeirdML | 36.1% | 24.5% |
| BigCodeBench Instruct | 50% | 49.7% |
| LMArena Coding | 1368 | 1302 |
| BigCodeBench Complete | 62.2% | 61.4% |
| SWE-bench Verified (bash only) | — | 21% |
| LiveBench Coding | 70.9% | — |
| ALE-Bench | — | 172.97 |
| HumanEval+ | 86.6% | — |
| MBPP+ | 73% | — |
Agentic & Tool Use Not comparable
DeepSeek-V3: —, Llama 4 Maverick: 28.2 (#91)
| Benchmark | DeepSeek-V3 | Llama 4 Maverick |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 37.3% |
| METR Time Horizons | 49.6% | — |
Reasoning DeepSeek-V3 leads
DeepSeek-V3: 20.5 (#236), Llama 4 Maverick: 10.1 (#342)
| Benchmark | DeepSeek-V3 | Llama 4 Maverick |
|---|---|---|
| SimpleBench | 27.2% | 27.7% |
| Kagi LLM Benchmark | 52.3% | 55.9% |
| CritPt | 0% | 0% |
| LMArena Hard Prompts | 1365 | 1281 |
| DTBench | 64.8% | 61.9% |
| LMCA | 15.5% | 15.9% |
| Epoch Capabilities Index | 135.94 | 132.2 |
| ForecastBench | 59.1 | 57.5 |
| ARC-AGI-2 | — | 0% |
| NYT Connections (extended) | — | 8% |
| ARC-AGI-1 | — | 4.4% |
| EnigmaEval | — | 0.6% |
| LiveBench Reasoning | 65.8% | — |
| LiveBench Data Analysis | 60.9% | — |
| BIG-Bench Hard | 87.5% | — |
| HellaSwag | 88.9% | — |
| LiveBench | 66.9% | — |
| PIQA | 84.7% | — |
| WinoGrande | 85.2% | — |
Math DeepSeek-V3 leads
DeepSeek-V3: 32.1 (#219), Llama 4 Maverick: 26.0 (#262)
| Benchmark | DeepSeek-V3 | Llama 4 Maverick |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 37.8% | 20.6% |
| Omni-MATH | 40.3% | 42.2% |
| LMArena Math | 1373 | 1299 |
| MATH Level 5 | 75.5% | 73% |
| FrontierMath (Feb 2025 set) | 1.7% | 0.7% |
| LiveBench Math | 73.5% | — |
Knowledge DeepSeek-V3 leads
DeepSeek-V3: 37.5 (#155), Llama 4 Maverick: 33.4 (#204)
| Benchmark | DeepSeek-V3 | Llama 4 Maverick |
|---|---|---|
| GPQA Diamond | 67.6% | 67% |
| MMLU-Pro | 72.3% | 81% |
| Confabulations | 26.1% | 22.6% |
| Vectara Hallucination Rate | 6.1% | 8.2% |
| GPQA (HELM) | 53.8% | 65% |
| LMArena Expert | 1351 | 1259 |
| Humanity's Last Exam | — | 5.7% |
| ARC (AI2) Challenge | 95.3% | — |
| MMLU | 87.2% | — |
| TriviaQA | 82.9% | — |
Multimodal Not comparable
DeepSeek-V3: —, Llama 4 Maverick: 31.6 (#105)
| Benchmark | DeepSeek-V3 | Llama 4 Maverick |
|---|---|---|
| LMArena Vision | — | 1142 |
| GeoBench | — | 52% |
| SpatialViz-Bench | — | 31.8% |
Multilingual DeepSeek-V3 leads
DeepSeek-V3: 48.5 (#143), Llama 4 Maverick: 42.2 (#195)
| Benchmark | DeepSeek-V3 | Llama 4 Maverick |
|---|---|---|
| LMArena Non-English | 1358 | 1269 |
| LMArena Chinese | 1391 | 1277 |
| LMArena French | 1385 | 1259 |
| LMArena German | 1374 | 1291 |
| LMArena Japanese | 1333 | 1207 |
| LMArena Korean | 1319 | 1203 |
| LMArena Russian | 1373 | 1286 |
| LMArena Spanish | 1358 | 1293 |
Instruction Following DeepSeek-V3 leads
DeepSeek-V3: 72.8 (#130), Llama 4 Maverick: 71.7 (#146)
| Benchmark | DeepSeek-V3 | Llama 4 Maverick |
|---|---|---|
| IFEval | 83.2% | 90.8% |
| LMArena Instruction Following | 1345 | 1267 |
| LiveBench Instruction Following | 81.5% | — |
Long Context DeepSeek-V3 leads
DeepSeek-V3: 34.0 (#253), Llama 4 Maverick: 31.4 (#279)
| Benchmark | DeepSeek-V3 | Llama 4 Maverick |
|---|---|---|
| Fiction.LiveBench | 50% | 46.2% |
| LMArena Longer Query | 1352 | 1280 |
Writing & Preference DeepSeek-V3 leads
DeepSeek-V3: 57.4 (#130), Llama 4 Maverick: 38.8 (#252)
| Benchmark | DeepSeek-V3 | Llama 4 Maverick |
|---|---|---|
| LMArena Text | 1375 | 1287 |
| LMArena Creative Writing | 1364 | 1267 |
| Short-Story Creative Writing | 77% | 62% |
| EQ-Bench Creative Writing | 1472 | 860 |
| WildBench | 83% | 80% |
| LMArena Multi-Turn | 1389 | 1289 |
| LiveBench Language | 49.1% | — |
Frequently asked questions
Is DeepSeek-V3 better than Llama 4 Maverick?
DeepSeek-V3 is the stronger model overall, scoring 39.5 to 30.9 on the Noometry Index.
Which is cheaper, DeepSeek-V3 or Llama 4 Maverick?
Llama 4 Maverick is cheaper. It lists at $0.19 per million input tokens and $0.65 per million output tokens; DeepSeek-V3 lists at $0.24 and $0.90.
Is DeepSeek-V3 or Llama 4 Maverick better for coding?
DeepSeek-V3 scores higher on coding benchmarks: 42.3 versus 26.6 in the Noometry coding category.
Which has the bigger context window?
DeepSeek-V3 does, with 164K tokens against 128K.
How many benchmarks do DeepSeek-V3 and Llama 4 Maverick share?
43 benchmarks have published results for both models. DeepSeek-V3 has 60 scored results on Noometry and Llama 4 Maverick has 54.