Model comparison
Claude Opus 4.6 vs Llama 3-8B
Claude Opus 4.6 is the stronger model overall, scoring 58.2 to 25.5 on the Noometry Index.
Last verified . 23 shared benchmarks.
MetaSummary
- They share 23 benchmarks with published results for both. Claude Opus 4.6 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 4.6 leads 63.0 to 8.8.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 94.4% for Claude Opus 4.6 and 1.9% for Llama 3-8B.
- Llama 3-8B has downloadable open weights; the other is API-only.
Side by side
| Claude Opus 4.6 | Llama 3-8B | |
|---|---|---|
| Provider | Anthropic | Meta |
| Noometry Index | 58.2 | 25.5 |
| Released | 2026-02-04 | 2024-04-18 |
| Weights | Proprietary | Open |
| Context window | 1M | — |
| Max output | 128K | — |
| Input $ / M tokens | $5 | — |
| Output $ / M tokens | $25 | — |
| Results tracked | 68 | 34 |
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Category by category
Coding Claude Opus 4.6 leads
Claude Opus 4.6: 57.2 (#20), Llama 3-8B: 31.0 (#289)
| Benchmark | Claude Opus 4.6 | Llama 3-8B |
|---|---|---|
| LMArena Coding | 1536 | 1152 |
| SWE-bench Verified | 78.7% | — |
| FrontierCode | 26.6% | — |
| SWE-bench Verified (bash only) | 75.6% | — |
| LMArena WebDev | 1547 | — |
| SWE-bench Multilingual | 72% | — |
| GSO | 41.2% | — |
| WeirdML | 78% | — |
| BigCodeBench Instruct | — | 31.9% |
| BigCodeBench Complete | — | 36.9% |
| ALE-Bench | 996.5 | — |
| AlgoTune | 1.47 | — |
| HumanEval+ | — | 56.7% |
| MBPP+ | — | 54.8% |
Agentic & Tool Use Not comparable
Claude Opus 4.6: 51.1 (#4), Llama 3-8B: —
| Benchmark | Claude Opus 4.6 | Llama 3-8B |
|---|---|---|
| Terminal-Bench | 79.8% | — |
| APEX-Agents | 46.3% | — |
| Remote Labor Index | 4.2% | — |
| τ²-bench Banking | 27.3% | — |
| Cybench | 93% | — |
| DeepResearch Bench | 55.3% | — |
| GBAEval | 44.1% | — |
| LMArena Search | 1253 | — |
| METR Time Horizons | 78.9% | — |
| Vending-Bench 2 | 8,018 | — |
Reasoning Claude Opus 4.6 leads
Claude Opus 4.6: 57.8 (#23), Llama 3-8B: 14.3 (#326)
| Benchmark | Claude Opus 4.6 | Llama 3-8B |
|---|---|---|
| Chess Puzzles | 17% | 0% |
| LMArena Hard Prompts | 1527 | 1133 |
| DTBench | 91.2% | 43.9% |
| Epoch Capabilities Index | 155.24 | 116.45 |
| ForecastBench | 60 | 58.6 |
| ARC-AGI-2 | 69.2% | — |
| SimpleBench | 67.6% | — |
| Kagi LLM Benchmark | 83.6% | — |
| NYT Connections (extended) | 92.1% | — |
| ARC-AGI-1 | 94% | — |
| EnigmaEval | 7.6% | — |
| Thematic Generalization | 80.6% | — |
| EBR-Bench | 12.7% | — |
| Mystery Game Puzzles | 25% | — |
| LMCA | 55.8% | — |
| Adversarial NLI | — | 57.3% |
| WinoGrande | — | 75.7% |
Math Claude Opus 4.6 leads
Claude Opus 4.6: 63.0 (#31), Llama 3-8B: 8.8 (#323)
| Benchmark | Claude Opus 4.6 | Llama 3-8B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 94.4% | 1.9% |
| LMArena Math | 1519 | 1151 |
| FrontierMath (Tiers 1-3) | 66% | — |
| FrontierMath Tier 4 | 26.8% | — |
| MathArena Final-Answer Competitions | 78.5% | — |
| ProofBench | 50% | — |
| MATH Level 5 | — | 6.1% |
| FrontierMath (Feb 2025 set) | 40.7% | — |
| FrontierMath Tier 4 (v1) | 22.9% | — |
Knowledge Claude Opus 4.6 leads
Claude Opus 4.6: 61.9 (#26), Llama 3-8B: 7.8 (#308)
| Benchmark | Claude Opus 4.6 | Llama 3-8B |
|---|---|---|
| GPQA Diamond | 90.5% | 26.1% |
| LMArena Expert | 1546 | 1113 |
| Humanity's Last Exam | 34.4% | — |
| SimpleQA Verified | 47% | — |
| Vectara Hallucination Rate | 12.2% | — |
| ARC (AI2) Challenge | — | 82.8% |
| MMLU | — | 68.8% |
| OpenBookQA | — | 82.6% |
| TriviaQA | — | 67.7% |
Multimodal Not comparable
Claude Opus 4.6: 37.3 (#74), Llama 3-8B: —
| Benchmark | Claude Opus 4.6 | Llama 3-8B |
|---|---|---|
| LMArena Vision | 1316 | — |
| Furniture Assembly | 28.3% | — |
| LMArena Document | 1507 | — |
Multilingual Claude Opus 4.6 leads
Claude Opus 4.6: 57.9 (#6), Llama 3-8B: 30.8 (#261)
| Benchmark | Claude Opus 4.6 | Llama 3-8B |
|---|---|---|
| LMArena Non-English | 1489 | 1098 |
| LMArena Chinese | 1551 | 1076 |
| LMArena French | 1513 | 1159 |
| LMArena German | 1502 | 1104 |
| LMArena Japanese | 1484 | 967 |
| LMArena Korean | 1464 | 1004 |
| LMArena Russian | 1497 | 1109 |
| LMArena Spanish | 1510 | 1173 |
Instruction Following Claude Opus 4.6 leads
Claude Opus 4.6: 79.5 (#4), Llama 3-8B: 58.4 (#260)
| Benchmark | Claude Opus 4.6 | Llama 3-8B |
|---|---|---|
| LMArena Instruction Following | 1523 | 1127 |
Long Context Claude Opus 4.6 leads
Claude Opus 4.6: 48.1 (#13), Llama 3-8B: 34.2 (#251)
| Benchmark | Claude Opus 4.6 | Llama 3-8B |
|---|---|---|
| LMArena Longer Query | 1520 | 1128 |
| CL-bench | 20.7% | — |
| CL-bench Life | 17% | — |
Writing & Preference Claude Opus 4.6 leads
Claude Opus 4.6: 73.5 (#10), Llama 3-8B: 37.5 (#256)
| Benchmark | Claude Opus 4.6 | Llama 3-8B |
|---|---|---|
| LMArena Text | 1503 | 1166 |
| LMArena Creative Writing | 1505 | 1150 |
| LMArena Multi-Turn | 1513 | 1152 |
| EQ-Bench Creative Writing | 1809 | — |
| EQ-Bench 4 | 1223 | — |
Frequently asked questions
Is Claude Opus 4.6 better than Llama 3-8B?
Claude Opus 4.6 is the stronger model overall, scoring 58.2 to 25.5 on the Noometry Index.
Is Claude Opus 4.6 or Llama 3-8B better for coding?
Claude Opus 4.6 scores higher on coding benchmarks: 57.2 versus 31.0 in the Noometry coding category.
How many benchmarks do Claude Opus 4.6 and Llama 3-8B share?
23 benchmarks have published results for both models. Claude Opus 4.6 has 68 scored results on Noometry and Llama 3-8B has 34.