# Claude Opus 4 vs Llama 3.1-8B

> Claude Opus 4 is the stronger model overall, scoring 43.1 to 23.0 on the Noometry Index. Llama 3.1-8B costs 522× 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-3-1-8b
- Last updated: 2026-10-10
- Shared benchmarks: 31

## Summary

- They share 31 benchmarks with published results for both. Claude Opus 4 scores higher in 9 categories and Llama 3.1-8B in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Claude Opus 4 leads 44.0 to 8.0.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 64.4% for Claude Opus 4 and 1.7% for Llama 3.1-8B.
- Llama 3.1-8B is cheaper at $0.05 / $0.08 per million input/output tokens, against $15 / $75 for Claude Opus 4.
- Claude Opus 4 accepts more context: 200K tokens versus 128K.
- Llama 3.1-8B has downloadable open weights; the other is API-only.

## Snapshot

| | Claude Opus 4 | Llama 3.1-8B |
|---|---|---|
| Provider | Anthropic | Meta |
| Noometry Index | 43.1 | 23.0 |
| Rank | 100 | 352 |
| Context | 200K | 128K |
| Input $/M | $15 | $0.05 |
| Output $/M | $75 | $0.08 |
| Weights | Proprietary | Open |

## Coding

- Claude Opus 4: 47.2 (#62)
- Llama 3.1-8B: 20.2 (#340)

| Benchmark | Claude Opus 4 | Llama 3.1-8B |
|---|---|---|
| WeirdML | 43.7% | 1.7% |
| LMArena Coding | 1442 | 1195 |
| SWE-bench Verified | 70.7% | — |
| SWE-bench Verified (bash only) | 67.6% | — |
| Aider Polyglot | 72% | — |
| SciCode | — | 13.2% |
| GSO | 6.9% | — |
| BigCodeBench Instruct | — | 32.8% |
| BigCodeBench Complete | — | 40.5% |
| AlgoTune | 1.33 | — |
| HumanEval+ | — | 62.8% |
| MBPP+ | — | 55.6% |

## Agentic & Tool Use

- Claude Opus 4: 34.8 (#42)
- Llama 3.1-8B: 22.5 (#131)

| Benchmark | Claude Opus 4 | Llama 3.1-8B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 25.8% |
| Cybench | 38% | — |
| DeepResearch Bench | 46.8% | — |
| BALROG | — | 15.1% |
| LMArena Search | 1127 | — |
| METR Time Horizons | 63.9% | — |

## Reasoning

- Claude Opus 4: 27.3 (#121)
- Llama 3.1-8B: 14.9 (#321)

| Benchmark | Claude Opus 4 | Llama 3.1-8B |
|---|---|---|
| CritPt | 0.3% | 0% |
| LMArena Hard Prompts | 1399 | 1175 |
| DTBench | 81.6% | 50.9% |
| LMCA | 37.4% | 5.4% |
| Epoch Capabilities Index | 142.67 | 116.57 |
| ARC-AGI-2 | 8.6% | — |
| SimpleBench | 58.8% | — |
| Kagi LLM Benchmark | 74.3% | — |
| ARC-AGI-1 | 35.7% | — |
| Chess Puzzles | — | 0% |
| EnigmaEval | 5.6% | — |
| ForecastBench | 61.1 | — |
| PIQA | — | 81.2% |

## Math

- Claude Opus 4: 42.0 (#86)
- Llama 3.1-8B: 10.2 (#317)

| Benchmark | Claude Opus 4 | Llama 3.1-8B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 64.4% | 1.7% |
| Omni-MATH | 61.6% | 13.7% |
| LMArena Math | 1390 | 1179 |
| MATH Level 5 | 85% | 22.9% |
| FrontierMath (Feb 2025 set) | 4.5% | — |
| FrontierMath Tier 4 (v1) | 4.2% | — |
| GSM8K | — | 82.4% |

## Knowledge

- Claude Opus 4: 44.0 (#88)
- Llama 3.1-8B: 8.0 (#307)

| Benchmark | Claude Opus 4 | Llama 3.1-8B |
|---|---|---|
| GPQA Diamond | 76.3% | 27% |
| MMLU-Pro | 87.5% | 40.6% |
| GPQA (HELM) | 70.8% | 24.7% |
| LMArena Expert | 1386 | 1144 |
| Humanity's Last Exam | 10.7% | — |
| Confabulations | 15.9% | — |
| Vectara Hallucination Rate | 12% | — |
| BoolQ | — | 82.8% |
| MMLU | — | 56.1% |

## Multimodal

- Claude Opus 4: 31.5 (#106)
- Llama 3.1-8B: —

| Benchmark | Claude Opus 4 | Llama 3.1-8B |
|---|---|---|
| LMArena Vision | 1192 | — |
| GeoBench | 49% | — |
| VPCT | 38% | — |

## Multilingual

- Claude Opus 4: 48.8 (#138)
- Llama 3.1-8B: 34.0 (#249)

| Benchmark | Claude Opus 4 | Llama 3.1-8B |
|---|---|---|
| LMArena Non-English | 1362 | 1148 |
| LMArena Chinese | 1386 | 1151 |
| LMArena French | 1372 | 1177 |
| LMArena German | 1391 | 1144 |
| LMArena Japanese | 1331 | 1061 |
| LMArena Korean | 1321 | 1053 |
| LMArena Russian | 1392 | 1158 |
| LMArena Spanish | 1389 | 1169 |

## Instruction Following

- Claude Opus 4: 77.1 (#28)
- Llama 3.1-8B: 58.9 (#258)

| Benchmark | Claude Opus 4 | Llama 3.1-8B |
|---|---|---|
| IFEval | 91.8% | 74.3% |
| LMArena Instruction Following | 1406 | 1159 |

## Long Context

- Claude Opus 4: 39.6 (#172)
- Llama 3.1-8B: 35.8 (#238)

| Benchmark | Claude Opus 4 | Llama 3.1-8B |
|---|---|---|
| LMArena Longer Query | 1422 | 1182 |
| Fiction.LiveBench | 61.1% | — |

## Writing & Preference

- Claude Opus 4: 61.2 (#89)
- Llama 3.1-8B: 29.7 (#290)

| Benchmark | Claude Opus 4 | Llama 3.1-8B |
|---|---|---|
| LMArena Text | 1377 | 1187 |
| LMArena Creative Writing | 1387 | 1154 |
| EQ-Bench Creative Writing | 1580 | 713 |
| WildBench | 85.2% | 68.7% |
| LMArena Multi-Turn | 1396 | 1172 |
| Short-Story Creative Writing | 83.6% | — |

## FAQ

### Is Claude Opus 4 better than Llama 3.1-8B?

Claude Opus 4 is the stronger model overall, scoring 43.1 to 23.0 on the Noometry Index. Llama 3.1-8B costs 522× 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 3.1-8B?

Llama 3.1-8B is cheaper. It lists at $0.05 per million input tokens and $0.08 per million output tokens; Claude Opus 4 lists at $15 and $75.

### Is Claude Opus 4 or Llama 3.1-8B better for coding?

Claude Opus 4 scores higher on coding benchmarks: 47.2 versus 20.2 in the Noometry coding category.

### Which has the bigger context window?

Claude Opus 4 does, with 200K tokens against 128K.

### How many benchmarks do Claude Opus 4 and Llama 3.1-8B share?

31 benchmarks have published results for both models. Claude Opus 4 has 56 scored results on Noometry and Llama 3.1-8B has 43.