Model comparison
Deepseek Coder v2 vs Llama-3.3-70B-Instruct
Deepseek Coder v2 is the stronger model overall, scoring 35.3 to 30.3 on the Noometry Index.
Last verified . 19 shared benchmarks.
DeepSeek
MetaSummary
- They share 19 benchmarks with published results for both. Deepseek Coder v2 scores higher in 5 categories and Llama-3.3-70B-Instruct in 3 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where Deepseek Coder v2 leads 32.3 to 14.3.
Side by side
| Deepseek Coder v2 | Llama-3.3-70B-Instruct | |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 35.3 | 30.3 |
| Released | 2024-06-17 | 2024-12-06 |
| Weights | Open | Open |
| Context window | — | 128K |
| Max output | — | 4K |
| Input $ / M tokens | — | $0.10 |
| Output $ / M tokens | — | $0.32 |
| Results tracked | 24 | 43 |
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Category by category
Coding Deepseek Coder v2 leads
Deepseek Coder v2: 37.5 (#178), Llama-3.3-70B-Instruct: 30.6 (#284)
| Benchmark | Deepseek Coder v2 | Llama-3.3-70B-Instruct |
|---|---|---|
| BigCodeBench Instruct | 48.2% | 46.9% |
| LMArena Coding | 1251 | 1268 |
| BigCodeBench Complete | 59.7% | 57.5% |
| SciCode | — | 26% |
| WeirdML | — | 14.4% |
| LiveBench Coding | — | 36.6% |
| HumanEval+ | 82.3% | — |
| MBPP+ | 75.1% | — |
Agentic & Tool Use Not comparable
Deepseek Coder v2: —, Llama-3.3-70B-Instruct: 25.6 (#100)
| Benchmark | Deepseek Coder v2 | Llama-3.3-70B-Instruct |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 31.9% |
| BALROG | — | 23% |
Reasoning Deepseek Coder v2 leads
Deepseek Coder v2: 22.6 (#175), Llama-3.3-70B-Instruct: 13.7 (#325)
| Benchmark | Deepseek Coder v2 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Hard Prompts | 1207 | 1257 |
| SimpleBench | — | 19.9% |
| CritPt | — | 0% |
| LiveBench Reasoning | — | 50.8% |
| DTBench | — | 59.5% |
| LiveBench Data Analysis | — | 49.5% |
| LMCA | — | 17.5% |
| Epoch Capabilities Index | — | 127.32 |
| ForecastBench | — | 58.6 |
| LiveBench | — | 50.2% |
| WinoGrande | 83.7% | — |
Math Deepseek Coder v2 leads
Deepseek Coder v2: 32.3 (#188), Llama-3.3-70B-Instruct: 14.3 (#292)
| Benchmark | Deepseek Coder v2 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Math | 1241 | 1268 |
| OTIS Mock AIME 2024-2025 | — | 5.1% |
| LiveBench Math | — | 42.2% |
| MATH Level 5 | — | 41.6% |
| GSM8K | 94.5% | — |
Knowledge Deepseek Coder v2 leads
Deepseek Coder v2: 31.7 (#209), Llama-3.3-70B-Instruct: 30.5 (#220)
| Benchmark | Deepseek Coder v2 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Expert | 1181 | 1225 |
| GPQA Diamond | — | 47.4% |
| Confabulations | — | 22.8% |
| Vectara Hallucination Rate | — | 4.1% |
| ARC (AI2) Challenge | 64.3% | — |
| MMLU | — | 86.3% |
Multilingual Llama-3.3-70B-Instruct leads
Deepseek Coder v2: 36.4 (#235), Llama-3.3-70B-Instruct: 40.1 (#215)
| Benchmark | Deepseek Coder v2 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Non-English | 1182 | 1236 |
| LMArena Chinese | 1201 | 1217 |
| LMArena French | 1184 | 1280 |
| LMArena German | 1164 | 1251 |
| LMArena Japanese | 1126 | 1149 |
| LMArena Korean | 1104 | 1142 |
| LMArena Russian | 1188 | 1252 |
| LMArena Spanish | 1153 | 1269 |
Instruction Following Llama-3.3-70B-Instruct leads
Deepseek Coder v2: 61.9 (#237), Llama-3.3-70B-Instruct: 71.3 (#152)
| Benchmark | Deepseek Coder v2 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Instruction Following | 1180 | 1242 |
| LiveBench Instruction Following | — | 82.7% |
Long Context Deepseek Coder v2 leads
Deepseek Coder v2: 37.0 (#219), Llama-3.3-70B-Instruct: 26.4 (#290)
| Benchmark | Deepseek Coder v2 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Longer Query | 1219 | 1256 |
| Fiction.LiveBench | — | 33.3% |
Writing & Preference Llama-3.3-70B-Instruct leads
Deepseek Coder v2: 38.4 (#249), Llama-3.3-70B-Instruct: 47.8 (#203)
| Benchmark | Deepseek Coder v2 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Text | 1191 | 1274 |
| LMArena Creative Writing | 1120 | 1250 |
| LMArena Multi-Turn | 1176 | 1279 |
| LiveBench Language | — | 39.2% |
Frequently asked questions
Is Deepseek Coder v2 better than Llama-3.3-70B-Instruct?
Deepseek Coder v2 is the stronger model overall, scoring 35.3 to 30.3 on the Noometry Index.
Is Deepseek Coder v2 or Llama-3.3-70B-Instruct better for coding?
Deepseek Coder v2 scores higher on coding benchmarks: 37.5 versus 30.6 in the Noometry coding category.
How many benchmarks do Deepseek Coder v2 and Llama-3.3-70B-Instruct share?
19 benchmarks have published results for both models. Deepseek Coder v2 has 24 scored results on Noometry and Llama-3.3-70B-Instruct has 43.