Model comparison
Claude Opus 4.7 vs Llama 3-8B
Claude Opus 4.7 is the stronger model overall, scoring 58.3 to 25.5 on the Noometry Index.
Last verified . 23 shared benchmarks.
MetaSummary
- They share 23 benchmarks with published results for both. Claude Opus 4.7 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 4.7 leads 66.7 to 8.8.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 97.8% for Claude Opus 4.7 and 1.9% for Llama 3-8B.
- Llama 3-8B has downloadable open weights; the other is API-only.
Side by side
| Claude Opus 4.7 | Llama 3-8B | |
|---|---|---|
| Provider | Anthropic | Meta |
| Noometry Index | 58.3 | 25.5 |
| Released | 2026-04-14 | 2024-04-18 |
| Weights | Proprietary | Open |
| Context window | 1M | — |
| Max output | 128K | — |
| Input $ / M tokens | $5 | — |
| Output $ / M tokens | $25 | — |
| Results tracked | 66 | 34 |
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Category by category
Coding Claude Opus 4.7 leads
Claude Opus 4.7: 59.6 (#13), Llama 3-8B: 31.0 (#289)
| Benchmark | Claude Opus 4.7 | Llama 3-8B |
|---|---|---|
| LMArena Coding | 1518 | 1152 |
| SWE-bench Verified | 83.5% | — |
| FrontierCode | 38.5% | — |
| LMArena WebDev | 1558 | — |
| SciCode | 54.5% | — |
| GSO | 44.1% | — |
| WeirdML | 76.4% | — |
| BigCodeBench Instruct | — | 31.9% |
| MirrorCode | 31.1% | — |
| BigCodeBench Complete | — | 36.9% |
| ALE-Bench | 1,323 | — |
| HumanEval+ | — | 56.7% |
| MBPP+ | — | 54.8% |
Agentic & Tool Use Not comparable
Claude Opus 4.7: 47.9 (#10), Llama 3-8B: —
| Benchmark | Claude Opus 4.7 | Llama 3-8B |
|---|---|---|
| Terminal-Bench | 80.2% | — |
| APEX-Agents | 49.2% | — |
| OSWorld 2.0 | 18.2% | — |
| τ²-bench Banking | 40.2% | — |
| PostTrainBench | 28.6% | — |
| ExploitBench | 26.5% | — |
| GBAEval | 43.8% | — |
| GDP.pdf | 21% | — |
| LMArena Search | 1233 | — |
| Vending-Bench 2 | 10,937 | — |
Reasoning Claude Opus 4.7 leads
Claude Opus 4.7: 53.8 (#29), Llama 3-8B: 14.3 (#326)
| Benchmark | Claude Opus 4.7 | Llama 3-8B |
|---|---|---|
| Chess Puzzles | 30% | 0% |
| LMArena Hard Prompts | 1506 | 1133 |
| DTBench | 94.7% | 43.9% |
| Epoch Capabilities Index | 156.25 | 116.45 |
| ForecastBench | 60.3 | 58.6 |
| ARC-AGI-2 | 75.8% | — |
| SimpleBench | 61.7% | — |
| Kagi LLM Benchmark | 80.7% | — |
| NYT Connections (extended) | 39% | — |
| ARC-AGI-1 | 93.5% | — |
| CritPt | 12% | — |
| Thematic Generalization | 72.8% | — |
| EBR-Bench | 19% | — |
| Mystery Game Puzzles | 28% | — |
| LMCA | 52.2% | — |
| Adversarial NLI | — | 57.3% |
| WinoGrande | — | 75.7% |
Math Claude Opus 4.7 leads
Claude Opus 4.7: 66.7 (#26), Llama 3-8B: 8.8 (#323)
| Benchmark | Claude Opus 4.7 | Llama 3-8B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 97.8% | 1.9% |
| LMArena Math | 1499 | 1151 |
| FrontierMath (Tiers 1-3) | 70.2% | — |
| FrontierMath Tier 4 | 31.7% | — |
| MathArena Final-Answer Competitions | 73.6% | — |
| ProofBench | 54% | — |
| MATH Level 5 | — | 6.1% |
| FrontierMath (Feb 2025 set) | 43.8% | — |
| FrontierMath Tier 4 (v1) | 22.9% | — |
Knowledge Claude Opus 4.7 leads
Claude Opus 4.7: 62.6 (#23), Llama 3-8B: 7.8 (#308)
| Benchmark | Claude Opus 4.7 | Llama 3-8B |
|---|---|---|
| GPQA Diamond | 90.2% | 26.1% |
| LMArena Expert | 1521 | 1113 |
| Humanity's Last Exam | 36.2% | — |
| SimpleQA Verified | 51.7% | — |
| Vectara Hallucination Rate | 12% | — |
| ARC (AI2) Challenge | — | 82.8% |
| MMLU | — | 68.8% |
| OpenBookQA | — | 82.6% |
| TriviaQA | — | 67.7% |
Multimodal Not comparable
Claude Opus 4.7: 41.2 (#38), Llama 3-8B: —
| Benchmark | Claude Opus 4.7 | Llama 3-8B |
|---|---|---|
| LMArena Vision | 1316 | — |
| Blueprint-Bench 2 | 24.5% | — |
| Furniture Assembly | 33.3% | — |
| LMArena Document | 1495 | — |
Multilingual Claude Opus 4.7 leads
Claude Opus 4.7: 57.3 (#10), Llama 3-8B: 30.8 (#261)
| Benchmark | Claude Opus 4.7 | Llama 3-8B |
|---|---|---|
| LMArena Non-English | 1480 | 1098 |
| LMArena Chinese | 1531 | 1076 |
| LMArena French | 1503 | 1159 |
| LMArena German | 1495 | 1104 |
| LMArena Japanese | 1472 | 967 |
| LMArena Korean | 1464 | 1004 |
| LMArena Russian | 1494 | 1109 |
| LMArena Spanish | 1495 | 1173 |
Instruction Following Claude Opus 4.7 leads
Claude Opus 4.7: 78.4 (#10), Llama 3-8B: 58.4 (#260)
| Benchmark | Claude Opus 4.7 | Llama 3-8B |
|---|---|---|
| LMArena Instruction Following | 1498 | 1127 |
Long Context Claude Opus 4.7 leads
Claude Opus 4.7: 46.2 (#25), Llama 3-8B: 34.2 (#251)
| Benchmark | Claude Opus 4.7 | Llama 3-8B |
|---|---|---|
| LMArena Longer Query | 1505 | 1128 |
Writing & Preference Claude Opus 4.7 leads
Claude Opus 4.7: 75.1 (#8), Llama 3-8B: 37.5 (#256)
| Benchmark | Claude Opus 4.7 | Llama 3-8B |
|---|---|---|
| LMArena Text | 1490 | 1166 |
| LMArena Creative Writing | 1486 | 1150 |
| LMArena Multi-Turn | 1505 | 1152 |
| EQ-Bench Creative Writing | 1914 | — |
| EQ-Bench 4 | 1311 | — |
Frequently asked questions
Is Claude Opus 4.7 better than Llama 3-8B?
Claude Opus 4.7 is the stronger model overall, scoring 58.3 to 25.5 on the Noometry Index.
Is Claude Opus 4.7 or Llama 3-8B better for coding?
Claude Opus 4.7 scores higher on coding benchmarks: 59.6 versus 31.0 in the Noometry coding category.
How many benchmarks do Claude Opus 4.7 and Llama 3-8B share?
23 benchmarks have published results for both models. Claude Opus 4.7 has 66 scored results on Noometry and Llama 3-8B has 34.