Model comparison
Gemini 2.5 Pro vs Llama 3-8B
Gemini 2.5 Pro is the stronger model overall, scoring 45.0 to 25.5 on the Noometry Index.
Last verified . 24 shared benchmarks.
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MetaSummary
- They share 24 benchmarks with published results for both. Gemini 2.5 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 2.5 Pro leads 56.0 to 7.8.
- The biggest single-benchmark swing is MATH Level 5: 95.9% for Gemini 2.5 Pro and 6.1% for Llama 3-8B.
- Llama 3-8B has downloadable open weights; the other is API-only.
Side by side
| Gemini 2.5 Pro | Llama 3-8B | |
|---|---|---|
| Provider | Meta | |
| Noometry Index | 45.0 | 25.5 |
| Released | 2025-03-25 | 2024-04-18 |
| Weights | Proprietary | Open |
| Context window | 1.05M | — |
| Max output | 66K | — |
| Input $ / M tokens | $1.25 | — |
| Output $ / M tokens | $10 | — |
| Results tracked | 78 | 34 |
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Category by category
Coding Gemini 2.5 Pro leads
Gemini 2.5 Pro: 42.4 (#101), Llama 3-8B: 31.0 (#289)
| Benchmark | Gemini 2.5 Pro | Llama 3-8B |
|---|---|---|
| LMArena Coding | 1452 | 1152 |
| SWE-bench Verified | 57.6% | — |
| SWE-bench Verified (bash only) | 53.6% | — |
| Aider Polyglot | 83.1% | — |
| LMArena WebDev | 1227 | — |
| SciCode | 42.8% | — |
| GSO | 3.9% | — |
| WeirdML | 54% | — |
| BigCodeBench Instruct | — | 31.9% |
| LiveBench Coding | 85.9% | — |
| BigCodeBench Complete | — | 36.9% |
| CadEval | 64% | — |
| ALE-Bench | 785.52 | — |
| AlgoTune | 1.51 | — |
| HumanEval+ | — | 56.7% |
| MBPP+ | — | 54.8% |
Agentic & Tool Use Not comparable
Gemini 2.5 Pro: 29.2 (#88), Llama 3-8B: —
| Benchmark | Gemini 2.5 Pro | Llama 3-8B |
|---|---|---|
| Terminal-Bench | 32.6% | — |
| GDPval | 23.3% | — |
| Remote Labor Index | 0.8% | — |
| TheAgentCompany | 30.3% | — |
| τ²-bench Banking | 13.7% | — |
| DeepResearch Bench | 42.8% | — |
| BALROG | 43.3% | — |
| LMArena Search | 1142 | — |
| METR Time Horizons | 55.4% | — |
| Vending-Bench 2 | 573.64 | — |
Reasoning Gemini 2.5 Pro leads
Gemini 2.5 Pro: 28.8 (#99), Llama 3-8B: 14.3 (#326)
| Benchmark | Gemini 2.5 Pro | Llama 3-8B |
|---|---|---|
| Chess Puzzles | 20% | 0% |
| LMArena Hard Prompts | 1455 | 1133 |
| DTBench | 82.4% | 43.9% |
| Epoch Capabilities Index | 145.32 | 116.45 |
| ForecastBench | 61.3 | 58.6 |
| ARC-AGI-2 | 4.9% | — |
| SimpleBench | 62.4% | — |
| Kagi LLM Benchmark | 70.3% | — |
| ARC-AGI-1 | 41% | — |
| CritPt | 2% | — |
| EnigmaEval | 5.6% | — |
| LiveBench Reasoning | 89.8% | — |
| LiveBench Data Analysis | 79.9% | — |
| LMCA | 34.8% | — |
| Adversarial NLI | — | 57.3% |
| LiveBench | 82.3% | — |
| WinoGrande | — | 75.7% |
Math Gemini 2.5 Pro leads
Gemini 2.5 Pro: 32.5 (#213), Llama 3-8B: 8.8 (#323)
| Benchmark | Gemini 2.5 Pro | Llama 3-8B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 84.7% | 1.9% |
| LMArena Math | 1450 | 1151 |
| MATH Level 5 | 95.9% | 6.1% |
| FrontierMath (Tiers 1-3) | 24.6% | — |
| FrontierMath Tier 4 | 0% | — |
| Omni-MATH | 41.6% | — |
| LiveBench Math | 90.2% | — |
| FrontierMath (Feb 2025 set) | 14.1% | — |
| FrontierMath Tier 4 (v1) | 4.2% | — |
Knowledge Gemini 2.5 Pro leads
Gemini 2.5 Pro: 56.0 (#46), Llama 3-8B: 7.8 (#308)
| Benchmark | Gemini 2.5 Pro | Llama 3-8B |
|---|---|---|
| GPQA Diamond | 85.3% | 26.1% |
| LMArena Expert | 1452 | 1113 |
| Humanity's Last Exam | 21.6% | — |
| MMLU-Pro | 86.3% | — |
| Confabulations | 10.6% | — |
| Vectara Hallucination Rate | 7% | — |
| GPQA (HELM) | 74.9% | — |
| ARC (AI2) Challenge | — | 82.8% |
| MMLU | — | 68.8% |
| OpenBookQA | — | 82.6% |
| TriviaQA | — | 67.7% |
Multimodal Not comparable
Gemini 2.5 Pro: 45.2 (#18), Llama 3-8B: —
| Benchmark | Gemini 2.5 Pro | Llama 3-8B |
|---|---|---|
| LMArena Vision | 1263 | — |
| GeoBench | 86% | — |
| VPCT | 48% | — |
| LMArena Document | 1421 | — |
| SpatialViz-Bench | 44.7% | — |
Multilingual Gemini 2.5 Pro leads
Gemini 2.5 Pro: 55.3 (#31), Llama 3-8B: 30.8 (#261)
| Benchmark | Gemini 2.5 Pro | Llama 3-8B |
|---|---|---|
| LMArena Non-English | 1451 | 1098 |
| LMArena Chinese | 1507 | 1076 |
| LMArena French | 1472 | 1159 |
| LMArena German | 1487 | 1104 |
| LMArena Japanese | 1461 | 967 |
| LMArena Korean | 1434 | 1004 |
| LMArena Russian | 1461 | 1109 |
| LMArena Spanish | 1473 | 1173 |
Instruction Following Gemini 2.5 Pro leads
Gemini 2.5 Pro: 75.0 (#75), Llama 3-8B: 58.4 (#260)
| Benchmark | Gemini 2.5 Pro | Llama 3-8B |
|---|---|---|
| LMArena Instruction Following | 1437 | 1127 |
| LiveBench Instruction Following | 80.6% | — |
| IFEval | 84% | — |
Long Context Gemini 2.5 Pro leads
Gemini 2.5 Pro: 59.8 (#5), Llama 3-8B: 34.2 (#251)
| Benchmark | Gemini 2.5 Pro | Llama 3-8B |
|---|---|---|
| LMArena Longer Query | 1449 | 1128 |
| Fiction.LiveBench | 91.7% | — |
Writing & Preference Gemini 2.5 Pro leads
Gemini 2.5 Pro: 63.7 (#62), Llama 3-8B: 37.5 (#256)
| Benchmark | Gemini 2.5 Pro | Llama 3-8B |
|---|---|---|
| LMArena Text | 1458 | 1166 |
| LMArena Creative Writing | 1454 | 1150 |
| LMArena Multi-Turn | 1453 | 1152 |
| Short-Story Creative Writing | 83.8% | — |
| EQ-Bench Creative Writing | 1421 | — |
| WildBench | 85.7% | — |
| LiveBench Language | 67.8% | — |
Frequently asked questions
Is Gemini 2.5 Pro better than Llama 3-8B?
Gemini 2.5 Pro is the stronger model overall, scoring 45.0 to 25.5 on the Noometry Index.
Is Gemini 2.5 Pro or Llama 3-8B better for coding?
Gemini 2.5 Pro scores higher on coding benchmarks: 42.4 versus 31.0 in the Noometry coding category.
How many benchmarks do Gemini 2.5 Pro and Llama 3-8B share?
24 benchmarks have published results for both models. Gemini 2.5 Pro has 78 scored results on Noometry and Llama 3-8B has 34.