# Claude Opus 4 vs Llama 3-8B

> Claude Opus 4 is the stronger model overall, scoring 42.9 to 25.3 on the Noometry Index. Llama 3-8B costs 214× less per token, which makes it the better buy when Claude Opus 4's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/claude-opus-4-vs-llama-3-8b
- Last updated: 2026-10-10
- Shared benchmarks: 23

## Summary

- They share 23 benchmarks with published results for both. Claude Opus 4 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 Claude Opus 4 leads 43.9 to 7.8.
- The biggest single-benchmark swing is MATH Level 5: 85% for Claude Opus 4 and 6.1% for Llama 3-8B.
- Llama 3-8B is cheaper at $0.14 / $0.14 per million input/output tokens, against $15 / $75 for Claude Opus 4.
- Claude Opus 4 accepts more context: 200K tokens versus 8K.
- Llama 3-8B has downloadable open weights; the other is API-only.

## Snapshot

| | Claude Opus 4 | Llama 3-8B |
|---|---|---|
| Provider | Anthropic | Meta |
| Noometry Index | 42.9 | 25.3 |
| Rank | 93 | 338 |
| Context | 200K | 8K |
| Input $/M | $15 | $0.14 |
| Output $/M | $75 | $0.14 |
| Weights | Proprietary | Open |

## Coding

- Claude Opus 4: 47.8 (#55)
- Llama 3-8B: 30.5 (#285)

| Benchmark | Claude Opus 4 | Llama 3-8B |
|---|---|---|
| LMArena Coding | 1442 | 1152 |
| SWE-bench Verified | 70.7% | — |
| SWE-bench Verified (bash only) | 67.6% | — |
| Aider Polyglot | 72% | — |
| GSO | 6.9% | — |
| WeirdML | 43.7% | — |
| BigCodeBench Instruct | — | 31.9% |
| BigCodeBench Complete | — | 36.9% |
| AlgoTune | 1.33 | — |
| HumanEval+ | — | 56.7% |
| MBPP+ | — | 54.8% |

## Agentic & Tool Use

- Claude Opus 4: 34.5 (#39)
- Llama 3-8B: —

| Benchmark | Claude Opus 4 | Llama 3-8B |
|---|---|---|
| Cybench | 38% | — |
| DeepResearch Bench | 46.8% | — |
| LMArena Search | 1127 | — |
| METR Time Horizons | 63.9% | — |

## Reasoning

- Claude Opus 4: 27.4 (#104)
- Llama 3-8B: 13.8 (#321)

| Benchmark | Claude Opus 4 | Llama 3-8B |
|---|---|---|
| LMArena Hard Prompts | 1399 | 1133 |
| DTBench | 81.6% | 43.9% |
| Epoch Capabilities Index | 142.67 | 116.33 |
| ForecastBench | 61.1 | 58.6 |
| ARC-AGI-2 | 8.6% | — |
| SimpleBench | 58.8% | — |
| Kagi LLM Benchmark | 74.3% | — |
| ARC-AGI-1 | 35.7% | — |
| CritPt | 0.3% | — |
| Chess Puzzles | — | 0% |
| EnigmaEval | 5.6% | — |
| LMCA | 37.4% | — |
| Adversarial NLI | — | 57.3% |
| WinoGrande | — | 75.7% |

## Math

- Claude Opus 4: 40.2 (#80)
- Llama 3-8B: 8.3 (#317)

| Benchmark | Claude Opus 4 | Llama 3-8B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 64.4% | 1.9% |
| LMArena Math | 1391 | 1151 |
| MATH Level 5 | 85% | 6.1% |
| Omni-MATH | 61.6% | — |
| FrontierMath (Feb 2025 set) | 4.5% | — |
| FrontierMath Tier 4 (v1) | 4.2% | — |

## Knowledge

- Claude Opus 4: 43.9 (#82)
- Llama 3-8B: 7.8 (#303)

| Benchmark | Claude Opus 4 | Llama 3-8B |
|---|---|---|
| GPQA Diamond | 76.3% | 26.1% |
| LMArena Expert | 1384 | 1113 |
| Humanity's Last Exam | 10.7% | — |
| MMLU-Pro | 87.5% | — |
| Confabulations | 15.9% | — |
| Vectara Hallucination Rate | 12% | — |
| GPQA (HELM) | 70.8% | — |
| ARC (AI2) Challenge | — | 82.8% |
| MMLU | — | 68.8% |
| OpenBookQA | — | 82.6% |
| TriviaQA | — | 67.7% |

## Multimodal

- Claude Opus 4: 30.9 (#101)
- Llama 3-8B: —

| Benchmark | Claude Opus 4 | Llama 3-8B |
|---|---|---|
| LMArena Vision | 1192 | — |
| GeoBench | 49% | — |
| VPCT | 38% | — |

## Multilingual

- Claude Opus 4: 49.0 (#133)
- Llama 3-8B: 31.0 (#256)

| Benchmark | Claude Opus 4 | Llama 3-8B |
|---|---|---|
| LMArena Non-English | 1362 | 1098 |
| LMArena Chinese | 1386 | 1076 |
| LMArena French | 1370 | 1158 |
| LMArena German | 1391 | 1104 |
| LMArena Japanese | 1332 | 967 |
| LMArena Korean | 1318 | 1003 |
| LMArena Russian | 1392 | 1109 |
| LMArena Spanish | 1388 | 1172 |

## Instruction Following

- Claude Opus 4: 77.3 (#26)
- Llama 3-8B: 58.7 (#255)

| Benchmark | Claude Opus 4 | Llama 3-8B |
|---|---|---|
| LMArena Instruction Following | 1406 | 1127 |
| IFEval | 91.8% | — |

## Long Context

- Claude Opus 4: 39.5 (#167)
- Llama 3-8B: 34.2 (#246)

| Benchmark | Claude Opus 4 | Llama 3-8B |
|---|---|---|
| LMArena Longer Query | 1422 | 1128 |
| Fiction.LiveBench | 61.1% | — |

## Writing & Preference

- Claude Opus 4: 61.4 (#85)
- Llama 3-8B: 37.7 (#251)

| Benchmark | Claude Opus 4 | Llama 3-8B |
|---|---|---|
| LMArena Text | 1377 | 1166 |
| LMArena Creative Writing | 1386 | 1150 |
| LMArena Multi-Turn | 1396 | 1152 |
| Short-Story Creative Writing | 83.6% | — |
| EQ-Bench Creative Writing | 1580 | — |
| WildBench | 85.2% | — |

## FAQ

### Is Claude Opus 4 better than Llama 3-8B?

Claude Opus 4 is the stronger model overall, scoring 42.9 to 25.3 on the Noometry Index. Llama 3-8B costs 214× less per token, which makes it the better buy when Claude Opus 4's lead doesn't matter for your workload.

### Which is cheaper, Claude Opus 4 or Llama 3-8B?

Llama 3-8B is cheaper. It lists at $0.14 per million input tokens and $0.14 per million output tokens; Claude Opus 4 lists at $15 and $75.

### Is Claude Opus 4 or Llama 3-8B better for coding?

Claude Opus 4 scores higher on coding benchmarks: 47.8 versus 30.5 in the Noometry coding category.

### Which has the bigger context window?

Claude Opus 4 does, with 200K tokens against 8K.

### How many benchmarks do Claude Opus 4 and Llama 3-8B share?

23 benchmarks have published results for both models. Claude Opus 4 has 56 scored results on Noometry and Llama 3-8B has 34.