Model comparison
DeepSeek-V3.2-Exp vs Llama 3-8B
DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.3 to 25.5 on the Noometry Index.
Last verified . 22 shared benchmarks.
DeepSeek
MetaSummary
- They share 22 benchmarks with published results for both. DeepSeek-V3.2-Exp scores higher in 8 categories and Llama 3-8B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where DeepSeek-V3.2-Exp leads 51.7 to 7.8.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 87.8% for DeepSeek-V3.2-Exp and 1.9% for Llama 3-8B.
Side by side
| DeepSeek-V3.2-Exp | Llama 3-8B | |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 44.3 | 25.5 |
| Released | 2025-09-29 | 2024-04-18 |
| Weights | Open | Open |
| Context window | 164K | — |
| Max output | 66K | — |
| Input $ / M tokens | $0.26 | — |
| Output $ / M tokens | $0.38 | — |
| Results tracked | 49 | 34 |
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Category by category
Coding DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 46.5 (#65), Llama 3-8B: 31.0 (#289)
| Benchmark | DeepSeek-V3.2-Exp | Llama 3-8B |
|---|---|---|
| LMArena Coding | 1454 | 1152 |
| SWE-bench Verified (bash only) | 70% | — |
| Aider Polyglot | 74.2% | — |
| LMArena WebDev | 1362 | — |
| SWE-bench Multilingual | 59% | — |
| SciCode | 38.9% | — |
| WeirdML | 39.5% | — |
| BigCodeBench Instruct | — | 31.9% |
| BigCodeBench Complete | — | 36.9% |
| HumanEval+ | — | 56.7% |
| MBPP+ | — | 54.8% |
Agentic & Tool Use Not comparable
DeepSeek-V3.2-Exp: 32.7 (#59), Llama 3-8B: —
| Benchmark | DeepSeek-V3.2-Exp | Llama 3-8B |
|---|---|---|
| Terminal-Bench | 39.6% | — |
| APEX-Agents | 21.3% | — |
| Berkeley Function Calling Leaderboard | 56.7% | — |
| TheAgentCompany | 42.9% | — |
| Vending-Bench 2 | 1,034 | — |
Reasoning DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 22.1 (#208), Llama 3-8B: 14.3 (#326)
| Benchmark | DeepSeek-V3.2-Exp | Llama 3-8B |
|---|---|---|
| Chess Puzzles | 14% | 0% |
| LMArena Hard Prompts | 1434 | 1133 |
| DTBench | 87.7% | 43.9% |
| Epoch Capabilities Index | 146.27 | 116.45 |
| ARC-AGI-2 | 4% | — |
| Kagi LLM Benchmark | 52.2% | — |
| NYT Connections (extended) | 36.7% | — |
| ARC-AGI-1 | 57% | — |
| CritPt | 2.9% | — |
| Thematic Generalization | 65% | — |
| LMCA | 29.1% | — |
| Adversarial NLI | — | 57.3% |
| ForecastBench | — | 58.6 |
| WinoGrande | — | 75.7% |
Math DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 41.7 (#87), Llama 3-8B: 8.8 (#323)
| Benchmark | DeepSeek-V3.2-Exp | Llama 3-8B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 87.8% | 1.9% |
| LMArena Math | 1435 | 1151 |
| MathArena Final-Answer Competitions | 57.7% | — |
| ProofBench | 8% | — |
| MATH Level 5 | — | 6.1% |
| FrontierMath (Feb 2025 set) | 22.1% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 51.7 (#66), Llama 3-8B: 7.8 (#308)
| Benchmark | DeepSeek-V3.2-Exp | Llama 3-8B |
|---|---|---|
| GPQA Diamond | 83.4% | 26.1% |
| LMArena Expert | 1436 | 1113 |
| Vectara Hallucination Rate | 5.3% | — |
| ARC (AI2) Challenge | — | 82.8% |
| MMLU | — | 68.8% |
| OpenBookQA | — | 82.6% |
| TriviaQA | — | 67.7% |
Multilingual DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 52.2 (#90), Llama 3-8B: 30.8 (#261)
| Benchmark | DeepSeek-V3.2-Exp | Llama 3-8B |
|---|---|---|
| LMArena Non-English | 1409 | 1098 |
| LMArena Chinese | 1461 | 1076 |
| LMArena French | 1433 | 1159 |
| LMArena German | 1440 | 1104 |
| LMArena Japanese | 1374 | 967 |
| LMArena Korean | 1371 | 1004 |
| LMArena Russian | 1424 | 1109 |
| LMArena Spanish | 1440 | 1173 |
Instruction Following DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 74.5 (#93), Llama 3-8B: 58.4 (#260)
| Benchmark | DeepSeek-V3.2-Exp | Llama 3-8B |
|---|---|---|
| LMArena Instruction Following | 1413 | 1127 |
Long Context DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 47.6 (#16), Llama 3-8B: 34.2 (#251)
| Benchmark | DeepSeek-V3.2-Exp | Llama 3-8B |
|---|---|---|
| LMArena Longer Query | 1428 | 1128 |
| Fiction.LiveBench | 83.3% | — |
| CL-bench | 13.2% | — |
| CL-bench Life | 9.5% | — |
Writing & Preference DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 62.4 (#77), Llama 3-8B: 37.5 (#256)
| Benchmark | DeepSeek-V3.2-Exp | Llama 3-8B |
|---|---|---|
| LMArena Text | 1425 | 1166 |
| LMArena Creative Writing | 1403 | 1150 |
| LMArena Multi-Turn | 1427 | 1152 |
| EQ-Bench Creative Writing | 1515 | — |
Frequently asked questions
Is DeepSeek-V3.2-Exp better than Llama 3-8B?
DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.3 to 25.5 on the Noometry Index.
Is DeepSeek-V3.2-Exp or Llama 3-8B better for coding?
DeepSeek-V3.2-Exp scores higher on coding benchmarks: 46.5 versus 31.0 in the Noometry coding category.
How many benchmarks do DeepSeek-V3.2-Exp and Llama 3-8B share?
22 benchmarks have published results for both models. DeepSeek-V3.2-Exp has 49 scored results on Noometry and Llama 3-8B has 34.