Model comparison
DeepSeek-V3 vs Llama 3.2 3B
DeepSeek-V3 is the stronger model overall, scoring 39.5 to 28.9 on the Noometry Index. Llama 3.2 3B costs 3.4× less per token, which makes it the better buy when DeepSeek-V3's lead doesn't matter for your workload.
Last verified . 16 shared benchmarks.
DeepSeek
MetaSummary
- They share 16 benchmarks with published results for both. DeepSeek-V3 scores higher in 6 categories and Llama 3.2 3B in 2 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where DeepSeek-V3 leads 57.4 to 24.7.
- The biggest single-benchmark swing is BigCodeBench Complete: 62.2% for DeepSeek-V3 and 28.3% for Llama 3.2 3B.
- Llama 3.2 3B is cheaper at $0.05 / $0.33 per million input/output tokens, against $0.24 / $0.90 for DeepSeek-V3.
- DeepSeek-V3 accepts more context: 164K tokens versus 131K.
Side by side
| DeepSeek-V3 | Llama 3.2 3B | |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 39.5 | 28.9 |
| Released | 2024-12-26 | 2024-09-24 |
| Weights | Open | Open |
| Context window | 164K | 131K |
| Max output | 164K | 118K |
| Input $ / M tokens | $0.24 | $0.05 |
| Output $ / M tokens | $0.90 | $0.33 |
| Results tracked | 60 | 18 |
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Category by category
Coding DeepSeek-V3 leads
DeepSeek-V3: 42.3 (#106), Llama 3.2 3B: 27.6 (#319)
| Benchmark | DeepSeek-V3 | Llama 3.2 3B |
|---|---|---|
| BigCodeBench Instruct | 50% | 23.4% |
| LMArena Coding | 1368 | 1098 |
| BigCodeBench Complete | 62.2% | 28.3% |
| Aider Polyglot | 55.1% | — |
| SciCode | 35.8% | — |
| WeirdML | 36.1% | — |
| LiveBench Coding | 70.9% | — |
| HumanEval+ | 86.6% | — |
| MBPP+ | 73% | — |
Agentic & Tool Use Not comparable
DeepSeek-V3: —, Llama 3.2 3B: 20.1 (#143)
| Benchmark | DeepSeek-V3 | Llama 3.2 3B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 21.9% |
| BALROG | — | 10.1% |
| METR Time Horizons | 49.6% | — |
Reasoning Too close to call
DeepSeek-V3: 20.5 (#236), Llama 3.2 3B: 21.0 (#228)
| Benchmark | DeepSeek-V3 | Llama 3.2 3B |
|---|---|---|
| LMArena Hard Prompts | 1365 | 1095 |
| SimpleBench | 27.2% | — |
| Kagi LLM Benchmark | 52.3% | — |
| CritPt | 0% | — |
| LiveBench Reasoning | 65.8% | — |
| DTBench | 64.8% | — |
| LiveBench Data Analysis | 60.9% | — |
| LMCA | 15.5% | — |
| BIG-Bench Hard | 87.5% | — |
| Epoch Capabilities Index | 135.94 | — |
| ForecastBench | 59.1 | — |
| HellaSwag | 88.9% | — |
| LiveBench | 66.9% | — |
| PIQA | 84.7% | — |
| WinoGrande | 85.2% | — |
Math Too close to call
DeepSeek-V3: 32.1 (#219), Llama 3.2 3B: 32.4 (#214)
| Benchmark | DeepSeek-V3 | Llama 3.2 3B |
|---|---|---|
| LMArena Math | 1373 | 1126 |
| OTIS Mock AIME 2024-2025 | 37.8% | — |
| Omni-MATH | 40.3% | — |
| LiveBench Math | 73.5% | — |
| MATH Level 5 | 75.5% | — |
| FrontierMath (Feb 2025 set) | 1.7% | — |
Knowledge DeepSeek-V3 leads
DeepSeek-V3: 37.5 (#155), Llama 3.2 3B: 29.7 (#235)
| Benchmark | DeepSeek-V3 | Llama 3.2 3B |
|---|---|---|
| LMArena Expert | 1351 | 1090 |
| GPQA Diamond | 67.6% | — |
| MMLU-Pro | 72.3% | — |
| Confabulations | 26.1% | — |
| Vectara Hallucination Rate | 6.1% | — |
| GPQA (HELM) | 53.8% | — |
| ARC (AI2) Challenge | 95.3% | — |
| MMLU | 87.2% | — |
| TriviaQA | 82.9% | — |
Multilingual DeepSeek-V3 leads
DeepSeek-V3: 48.5 (#143), Llama 3.2 3B: 26.2 (#281)
| Benchmark | DeepSeek-V3 | Llama 3.2 3B |
|---|---|---|
| LMArena Non-English | 1358 | 1019 |
| LMArena Chinese | 1391 | 1017 |
| LMArena German | 1374 | 1056 |
| LMArena Russian | 1373 | 949 |
| LMArena French | 1385 | — |
| LMArena Japanese | 1333 | — |
| LMArena Korean | 1319 | — |
| LMArena Spanish | 1358 | — |
Instruction Following DeepSeek-V3 leads
DeepSeek-V3: 72.8 (#130), Llama 3.2 3B: 56.0 (#275)
| Benchmark | DeepSeek-V3 | Llama 3.2 3B |
|---|---|---|
| LMArena Instruction Following | 1345 | 1089 |
| LiveBench Instruction Following | 81.5% | — |
| IFEval | 83.2% | — |
Long Context Too close to call
DeepSeek-V3: 34.0 (#253), Llama 3.2 3B: 33.4 (#261)
| Benchmark | DeepSeek-V3 | Llama 3.2 3B |
|---|---|---|
| LMArena Longer Query | 1352 | 1100 |
| Fiction.LiveBench | 50% | — |
Writing & Preference DeepSeek-V3 leads
DeepSeek-V3: 57.4 (#130), Llama 3.2 3B: 24.7 (#307)
| Benchmark | DeepSeek-V3 | Llama 3.2 3B |
|---|---|---|
| LMArena Text | 1375 | 1110 |
| LMArena Creative Writing | 1364 | 1094 |
| EQ-Bench Creative Writing | 1472 | 595 |
| LMArena Multi-Turn | 1389 | 1105 |
| Short-Story Creative Writing | 77% | — |
| WildBench | 83% | — |
| LiveBench Language | 49.1% | — |
Frequently asked questions
Is DeepSeek-V3 better than Llama 3.2 3B?
DeepSeek-V3 is the stronger model overall, scoring 39.5 to 28.9 on the Noometry Index. Llama 3.2 3B costs 3.4× less per token, which makes it the better buy when DeepSeek-V3's lead doesn't matter for your workload.
Which is cheaper, DeepSeek-V3 or Llama 3.2 3B?
Llama 3.2 3B is cheaper. It lists at $0.05 per million input tokens and $0.33 per million output tokens; DeepSeek-V3 lists at $0.24 and $0.90.
Is DeepSeek-V3 or Llama 3.2 3B better for coding?
DeepSeek-V3 scores higher on coding benchmarks: 42.3 versus 27.6 in the Noometry coding category.
Which has the bigger context window?
DeepSeek-V3 does, with 164K tokens against 131K.
How many benchmarks do DeepSeek-V3 and Llama 3.2 3B share?
16 benchmarks have published results for both models. DeepSeek-V3 has 60 scored results on Noometry and Llama 3.2 3B has 18.