Model comparison
Claude Opus 4.8 vs Llama 3-8B
Claude Opus 4.8 is the stronger model overall, scoring 60.7 to 25.5 on the Noometry Index.
Last verified . 23 shared benchmarks.
MetaSummary
- They share 23 benchmarks with published results for both. Claude Opus 4.8 scores higher in 8 categories and Llama 3-8B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where Claude Opus 4.8 leads 78.4 to 8.8.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 98.3% for Claude Opus 4.8 and 1.9% for Llama 3-8B.
- Llama 3-8B has downloadable open weights; the other is API-only.
Side by side
| Claude Opus 4.8 | Llama 3-8B | |
|---|---|---|
| Provider | Anthropic | Meta |
| Noometry Index | 60.7 | 25.5 |
| Released | 2026-05-28 | 2024-04-18 |
| Weights | Proprietary | Open |
| Context window | 1M | — |
| Max output | 128K | — |
| Input $ / M tokens | $5 | — |
| Output $ / M tokens | $25 | — |
| Results tracked | 65 | 34 |
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Category by category
Coding Claude Opus 4.8 leads
Claude Opus 4.8: 59.9 (#12), Llama 3-8B: 31.0 (#289)
| Benchmark | Claude Opus 4.8 | Llama 3-8B |
|---|---|---|
| LMArena Coding | 1490 | 1152 |
| DeepSWE | 59% | — |
| FrontierCode | 46.5% | — |
| LMArena WebDev | 1556 | — |
| SciCode | 53.5% | — |
| GSO | 47.1% | — |
| WeirdML | 82.9% | — |
| BigCodeBench Instruct | — | 31.9% |
| BigCodeBench Complete | — | 36.9% |
| ALE-Bench | 1,564 | — |
| HumanEval+ | — | 56.7% |
| MBPP+ | — | 54.8% |
Agentic & Tool Use Not comparable
Claude Opus 4.8: 47.6 (#11), Llama 3-8B: —
| Benchmark | Claude Opus 4.8 | Llama 3-8B |
|---|---|---|
| APEX-Agents | 48.9% | — |
| OSWorld 2.0 | 20.6% | — |
| Remote Labor Index | 8.3% | — |
| τ²-bench Banking | 39.7% | — |
| DeepResearch Bench | 50.2% | — |
| PostTrainBench | 33.8% | — |
| GBAEval | 70.9% | — |
| GDP.pdf | 24% | — |
| LMArena Search | 1204 | — |
| Vending-Bench 2 | 5,787 | — |
Reasoning Claude Opus 4.8 leads
Claude Opus 4.8: 64.7 (#16), Llama 3-8B: 14.3 (#326)
| Benchmark | Claude Opus 4.8 | Llama 3-8B |
|---|---|---|
| Chess Puzzles | 34% | 0% |
| LMArena Hard Prompts | 1482 | 1133 |
| DTBench | 94.9% | 43.9% |
| Epoch Capabilities Index | 158.21 | 116.45 |
| ForecastBench | 59.9 | 58.6 |
| ARC-AGI-2 | 72.1% | — |
| SimpleBench | 64.8% | — |
| Kagi LLM Benchmark | 88.8% | — |
| NYT Connections (extended) | 91.1% | — |
| ARC-AGI-1 | 92.5% | — |
| CritPt | 20.9% | — |
| EnigmaEval | 23.5% | — |
| EBR-Bench | 28.6% | — |
| Mystery Game Puzzles | 36% | — |
| LMCA | 57.5% | — |
| Surface Evolver Bench | 87.5% | — |
| Adversarial NLI | — | 57.3% |
| Bench to the Future 3 | 0.14 | — |
| WinoGrande | — | 75.7% |
Math Claude Opus 4.8 leads
Claude Opus 4.8: 78.4 (#13), Llama 3-8B: 8.8 (#323)
| Benchmark | Claude Opus 4.8 | Llama 3-8B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 98.3% | 1.9% |
| LMArena Math | 1487 | 1151 |
| FrontierMath (Tiers 1-3) | 80% | — |
| FrontierMath Tier 4 | 56.1% | — |
| MathArena Final-Answer Competitions | 91.8% | — |
| ProofBench | 69% | — |
| MATH Level 5 | — | 6.1% |
| FrontierMath (Feb 2025 set) | 47.2% | — |
| FrontierMath Tier 4 (v1) | 31.3% | — |
Knowledge Claude Opus 4.8 leads
Claude Opus 4.8: 61.3 (#29), Llama 3-8B: 7.8 (#308)
| Benchmark | Claude Opus 4.8 | Llama 3-8B |
|---|---|---|
| GPQA Diamond | 91% | 26.1% |
| LMArena Expert | 1502 | 1113 |
| SimpleQA Verified | 53% | — |
| ARC (AI2) Challenge | — | 82.8% |
| MMLU | — | 68.8% |
| OpenBookQA | — | 82.6% |
| TriviaQA | — | 67.7% |
Multimodal Not comparable
Claude Opus 4.8: 42.9 (#26), Llama 3-8B: —
| Benchmark | Claude Opus 4.8 | Llama 3-8B |
|---|---|---|
| LMArena Vision | 1294 | — |
| Blueprint-Bench 2 | 14.5% | — |
| Furniture Assembly | 42.5% | — |
| LMArena Document | 1475 | — |
Multilingual Claude Opus 4.8 leads
Claude Opus 4.8: 55.2 (#33), Llama 3-8B: 30.8 (#261)
| Benchmark | Claude Opus 4.8 | Llama 3-8B |
|---|---|---|
| LMArena Non-English | 1450 | 1098 |
| LMArena Chinese | 1507 | 1076 |
| LMArena French | 1481 | 1159 |
| LMArena German | 1472 | 1104 |
| LMArena Japanese | 1440 | 967 |
| LMArena Korean | 1432 | 1004 |
| LMArena Russian | 1474 | 1109 |
| LMArena Spanish | 1466 | 1173 |
Instruction Following Claude Opus 4.8 leads
Claude Opus 4.8: 77.4 (#24), Llama 3-8B: 58.4 (#260)
| Benchmark | Claude Opus 4.8 | Llama 3-8B |
|---|---|---|
| LMArena Instruction Following | 1476 | 1127 |
Long Context Claude Opus 4.8 leads
Claude Opus 4.8: 45.4 (#35), Llama 3-8B: 34.2 (#251)
| Benchmark | Claude Opus 4.8 | Llama 3-8B |
|---|---|---|
| LMArena Longer Query | 1483 | 1128 |
Writing & Preference Claude Opus 4.8 leads
Claude Opus 4.8: 72.0 (#16), Llama 3-8B: 37.5 (#256)
| Benchmark | Claude Opus 4.8 | Llama 3-8B |
|---|---|---|
| LMArena Text | 1461 | 1166 |
| LMArena Creative Writing | 1454 | 1150 |
| LMArena Multi-Turn | 1476 | 1152 |
| EQ-Bench Creative Writing | 1840 | — |
| EQ-Bench 4 | 1281 | — |
Frequently asked questions
Is Claude Opus 4.8 better than Llama 3-8B?
Claude Opus 4.8 is the stronger model overall, scoring 60.7 to 25.5 on the Noometry Index.
Is Claude Opus 4.8 or Llama 3-8B better for coding?
Claude Opus 4.8 scores higher on coding benchmarks: 59.9 versus 31.0 in the Noometry coding category.
How many benchmarks do Claude Opus 4.8 and Llama 3-8B share?
23 benchmarks have published results for both models. Claude Opus 4.8 has 65 scored results on Noometry and Llama 3-8B has 34.