Model comparison
DeepSeek-V3 vs Llama 3.2 1B
DeepSeek-V3 is the stronger model overall, scoring 39.5 to 20.1 on the Noometry Index. Llama 3.2 1B costs 5.7× less per token, which makes it the better buy when DeepSeek-V3's lead doesn't matter for your workload.
Last verified . 19 shared benchmarks.
DeepSeek
MetaSummary
- They share 19 benchmarks with published results for both. DeepSeek-V3 scores higher in 8 categories and Llama 3.2 1B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where DeepSeek-V3 leads 57.4 to 21.3.
- The biggest single-benchmark swing is BigCodeBench Complete: 62.2% for DeepSeek-V3 and 11.3% for Llama 3.2 1B.
- Llama 3.2 1B is cheaper at $0.027 / $0.20 per million input/output tokens, against $0.24 / $0.90 for DeepSeek-V3.
- DeepSeek-V3 accepts more context: 164K tokens versus 60K.
Side by side
| DeepSeek-V3 | Llama 3.2 1B | |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 39.5 | 20.1 |
| Released | 2024-12-26 | 2024-09-24 |
| Weights | Open | Open |
| Context window | 164K | 60K |
| Max output | 164K | 54K |
| Input $ / M tokens | $0.24 | $0.027 |
| Output $ / M tokens | $0.90 | $0.20 |
| Results tracked | 60 | 22 |
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Category by category
Coding DeepSeek-V3 leads
DeepSeek-V3: 42.3 (#106), Llama 3.2 1B: 21.1 (#338)
| Benchmark | DeepSeek-V3 | Llama 3.2 1B |
|---|---|---|
| BigCodeBench Instruct | 50% | 8.2% |
| LMArena Coding | 1368 | 1070 |
| BigCodeBench Complete | 62.2% | 11.3% |
| 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 1B: 14.6 (#150)
| Benchmark | DeepSeek-V3 | Llama 3.2 1B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 10.8% |
| BALROG | — | 6.6% |
| METR Time Horizons | 49.6% | — |
Reasoning DeepSeek-V3 leads
DeepSeek-V3: 20.5 (#236), Llama 3.2 1B: 16.2 (#308)
| Benchmark | DeepSeek-V3 | Llama 3.2 1B |
|---|---|---|
| LMArena Hard Prompts | 1365 | 1044 |
| Epoch Capabilities Index | 135.94 | 101.99 |
| SimpleBench | 27.2% | — |
| Kagi LLM Benchmark | 52.3% | — |
| CritPt | 0% | — |
| Chess Puzzles | — | 0% |
| LiveBench Reasoning | 65.8% | — |
| DTBench | 64.8% | — |
| LiveBench Data Analysis | 60.9% | — |
| LMCA | 15.5% | — |
| BIG-Bench Hard | 87.5% | — |
| ForecastBench | 59.1 | — |
| HellaSwag | 88.9% | — |
| LiveBench | 66.9% | — |
| PIQA | 84.7% | — |
| WinoGrande | 85.2% | — |
Math DeepSeek-V3 leads
DeepSeek-V3: 32.1 (#219), Llama 3.2 1B: 10.4 (#313)
| Benchmark | DeepSeek-V3 | Llama 3.2 1B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 37.8% | 0.6% |
| LMArena Math | 1373 | 1086 |
| 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 1B: 7.2 (#312)
| Benchmark | DeepSeek-V3 | Llama 3.2 1B |
|---|---|---|
| GPQA Diamond | 67.6% | 23.9% |
| LMArena Expert | 1351 | 1007 |
| 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 1B: 23.8 (#292)
| Benchmark | DeepSeek-V3 | Llama 3.2 1B |
|---|---|---|
| LMArena Non-English | 1358 | 973 |
| LMArena Chinese | 1391 | 959 |
| LMArena German | 1374 | 1014 |
| LMArena Russian | 1373 | 941 |
| 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 1B: 52.4 (#290)
| Benchmark | DeepSeek-V3 | Llama 3.2 1B |
|---|---|---|
| LMArena Instruction Following | 1345 | 1031 |
| LiveBench Instruction Following | 81.5% | — |
| IFEval | 83.2% | — |
Long Context DeepSeek-V3 leads
DeepSeek-V3: 34.0 (#253), Llama 3.2 1B: 31.9 (#274)
| Benchmark | DeepSeek-V3 | Llama 3.2 1B |
|---|---|---|
| LMArena Longer Query | 1352 | 1050 |
| Fiction.LiveBench | 50% | — |
Writing & Preference DeepSeek-V3 leads
DeepSeek-V3: 57.4 (#130), Llama 3.2 1B: 21.3 (#310)
| Benchmark | DeepSeek-V3 | Llama 3.2 1B |
|---|---|---|
| LMArena Text | 1375 | 1055 |
| LMArena Creative Writing | 1364 | 1033 |
| EQ-Bench Creative Writing | 1472 | 200 |
| LMArena Multi-Turn | 1389 | 1030 |
| Short-Story Creative Writing | 77% | — |
| WildBench | 83% | — |
| LiveBench Language | 49.1% | — |
Frequently asked questions
Is DeepSeek-V3 better than Llama 3.2 1B?
DeepSeek-V3 is the stronger model overall, scoring 39.5 to 20.1 on the Noometry Index. Llama 3.2 1B costs 5.7× 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 1B?
Llama 3.2 1B is cheaper. It lists at $0.027 per million input tokens and $0.20 per million output tokens; DeepSeek-V3 lists at $0.24 and $0.90.
Is DeepSeek-V3 or Llama 3.2 1B better for coding?
DeepSeek-V3 scores higher on coding benchmarks: 42.3 versus 21.1 in the Noometry coding category.
Which has the bigger context window?
DeepSeek-V3 does, with 164K tokens against 60K.
How many benchmarks do DeepSeek-V3 and Llama 3.2 1B share?
19 benchmarks have published results for both models. DeepSeek-V3 has 60 scored results on Noometry and Llama 3.2 1B has 22.