Model comparison
Gemini 3 Pro vs Llama 3-8B
Gemini 3 Pro is the stronger model overall, scoring 54.7 to 25.3 on the Noometry Index.
Last verified . 22 shared benchmarks.
Google
MetaSummary
- They share 22 benchmarks with published results for both. Gemini 3 Pro 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 Gemini 3 Pro leads 64.3 to 7.8.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 91.4% for Gemini 3 Pro and 1.9% for Llama 3-8B.
- Llama 3-8B has downloadable open weights; the other is API-only.
Side by side
| Gemini 3 Pro | Llama 3-8B | |
|---|---|---|
| Provider | Meta | |
| Noometry Index | 54.7 | 25.3 |
| Released | 2025-11-18 | 2024-04-18 |
| Weights | Proprietary | Open |
| Context window | — | 8K |
| Max output | — | 8K |
| Input $ / M tokens | — | $0.14 |
| Output $ / M tokens | — | $0.14 |
| Results tracked | 67 | 34 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding Gemini 3 Pro leads
Gemini 3 Pro: 52.1 (#33), Llama 3-8B: 30.5 (#285)
| Benchmark | Gemini 3 Pro | Llama 3-8B |
|---|---|---|
| LMArena Coding | 1482 | 1152 |
| SWE-bench Verified | 72.9% | — |
| SWE-bench Verified (bash only) | 74.2% | — |
| LMArena WebDev | 1439 | — |
| SWE-bench Multilingual | 68.7% | — |
| GSO | 18.6% | — |
| WeirdML | 69.9% | — |
| BigCodeBench Instruct | — | 31.9% |
| BigCodeBench Complete | — | 36.9% |
| ALE-Bench | 1,177 | — |
| AlgoTune | 1.83 | — |
| HumanEval+ | — | 56.7% |
| MBPP+ | — | 54.8% |
Agentic & Tool Use Not comparable
Gemini 3 Pro: 40.5 (#21), Llama 3-8B: —
| Benchmark | Gemini 3 Pro | Llama 3-8B |
|---|---|---|
| Terminal-Bench | 69.4% | — |
| Berkeley Function Calling Leaderboard | 72.5% | — |
| GDPval | 40.3% | — |
| Remote Labor Index | 1.3% | — |
| τ²-bench Airline | 80.5% | — |
| τ²-bench Banking | 18% | — |
| τ²-bench Retail | 75.9% | — |
| τ²-bench Telecom | 91% | — |
| DeepResearch Bench | 46.3% | — |
| BALROG | 58.1% | — |
| LMArena Search | 1207 | — |
| METR Time Horizons | 71% | — |
| Vending-Bench 2 | 5,478 | — |
Reasoning Gemini 3 Pro leads
Gemini 3 Pro: 52.5 (#28), Llama 3-8B: 13.8 (#321)
| Benchmark | Gemini 3 Pro | Llama 3-8B |
|---|---|---|
| Chess Puzzles | 31% | 0% |
| LMArena Hard Prompts | 1481 | 1133 |
| Epoch Capabilities Index | 152.96 | 116.33 |
| ForecastBench | 61.2 | 58.6 |
| ARC-AGI-2 | 31.1% | — |
| SimpleBench | 76.4% | — |
| Kagi LLM Benchmark | 80.1% | — |
| NYT Connections (extended) | 94.4% | — |
| ARC-AGI-1 | 75% | — |
| CritPt | 6.9% | — |
| EnigmaEval | 18.2% | — |
| DTBench | — | 43.9% |
| Adversarial NLI | — | 57.3% |
| WinoGrande | — | 75.7% |
Math Gemini 3 Pro leads
Gemini 3 Pro: 48.8 (#50), Llama 3-8B: 8.3 (#317)
| Benchmark | Gemini 3 Pro | Llama 3-8B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 91.4% | 1.9% |
| LMArena Math | 1475 | 1151 |
| MathArena Final-Answer Competitions | 67% | — |
| ProofBench | 20% | — |
| Omni-MATH | 55.5% | — |
| MATH Level 5 | — | 6.1% |
| FrontierMath (Feb 2025 set) | 37.6% | — |
| FrontierMath Tier 4 (v1) | 18.8% | — |
Knowledge Gemini 3 Pro leads
Gemini 3 Pro: 64.3 (#15), Llama 3-8B: 7.8 (#303)
| Benchmark | Gemini 3 Pro | Llama 3-8B |
|---|---|---|
| GPQA Diamond | 92.6% | 26.1% |
| LMArena Expert | 1475 | 1113 |
| Humanity's Last Exam | 37.5% | — |
| MMLU-Pro | 90.3% | — |
| Vectara Hallucination Rate | 13.6% | — |
| GPQA (HELM) | 80.3% | — |
| ARC (AI2) Challenge | — | 82.8% |
| MMLU | — | 68.8% |
| OpenBookQA | — | 82.6% |
| TriviaQA | — | 67.7% |
Multimodal Not comparable
Gemini 3 Pro: 56.9 (#1), Llama 3-8B: —
| Benchmark | Gemini 3 Pro | Llama 3-8B |
|---|---|---|
| LMArena Vision | 1305 | — |
| GeoBench | 84% | — |
| VPCT | 91% | — |
| LMArena Document | 1434 | — |
Multilingual Gemini 3 Pro leads
Gemini 3 Pro: 57.1 (#14), Llama 3-8B: 31.0 (#256)
| Benchmark | Gemini 3 Pro | Llama 3-8B |
|---|---|---|
| LMArena Non-English | 1474 | 1098 |
| LMArena Chinese | 1522 | 1076 |
| LMArena French | 1492 | 1158 |
| LMArena German | 1517 | 1104 |
| LMArena Japanese | 1513 | 967 |
| LMArena Korean | 1449 | 1003 |
| LMArena Russian | 1493 | 1109 |
| LMArena Spanish | 1468 | 1172 |
Instruction Following Gemini 3 Pro leads
Gemini 3 Pro: 76.4 (#42), Llama 3-8B: 58.7 (#255)
| Benchmark | Gemini 3 Pro | Llama 3-8B |
|---|---|---|
| LMArena Instruction Following | 1458 | 1127 |
| IFEval | 87.7% | — |
Long Context Gemini 3 Pro leads
Gemini 3 Pro: 44.0 (#75), Llama 3-8B: 34.2 (#246)
| Benchmark | Gemini 3 Pro | Llama 3-8B |
|---|---|---|
| LMArena Longer Query | 1472 | 1128 |
| CL-bench | 15.8% | — |
Writing & Preference Gemini 3 Pro leads
Gemini 3 Pro: 66.6 (#34), Llama 3-8B: 37.7 (#251)
| Benchmark | Gemini 3 Pro | Llama 3-8B |
|---|---|---|
| LMArena Text | 1479 | 1166 |
| LMArena Creative Writing | 1482 | 1150 |
| LMArena Multi-Turn | 1484 | 1152 |
| EQ-Bench Creative Writing | 1525 | — |
| WildBench | 85.9% | — |
Frequently asked questions
Is Gemini 3 Pro better than Llama 3-8B?
Gemini 3 Pro is the stronger model overall, scoring 54.7 to 25.3 on the Noometry Index.
Is Gemini 3 Pro or Llama 3-8B better for coding?
Gemini 3 Pro scores higher on coding benchmarks: 52.1 versus 30.5 in the Noometry coding category.
How many benchmarks do Gemini 3 Pro and Llama 3-8B share?
22 benchmarks have published results for both models. Gemini 3 Pro has 67 scored results on Noometry and Llama 3-8B has 34.