# Claude Opus 4.1 vs Llama-3.3-70B-Instruct

> Claude Opus 4.1 is the stronger model overall, scoring 41.0 to 30.6 on the Noometry Index. Llama-3.3-70B-Instruct costs 194× less per token, which makes it the better buy when Claude Opus 4.1's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/claude-opus-4-1-vs-llama-3-3-70b-instruct
- Last updated: 2026-10-10
- Shared benchmarks: 27

## Summary

- They share 27 benchmarks with published results for both. Claude Opus 4.1 scores higher in 9 categories and Llama-3.3-70B-Instruct in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Claude Opus 4.1 leads 32.2 to 14.1.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 68.9% for Claude Opus 4.1 and 5.1% for Llama-3.3-70B-Instruct.
- Llama-3.3-70B-Instruct is cheaper at $0.10 / $0.32 per million input/output tokens, against $15 / $75 for Claude Opus 4.1.
- Claude Opus 4.1 accepts more context: 200K tokens versus 128K.
- Llama-3.3-70B-Instruct has downloadable open weights; the other is API-only.

## Snapshot

| | Claude Opus 4.1 | Llama-3.3-70B-Instruct |
|---|---|---|
| Provider | Anthropic | Meta |
| Noometry Index | 41.0 | 30.6 |
| Rank | 142 | 291 |
| Context | 200K | 128K |
| Input $/M | $15 | $0.10 |
| Output $/M | $75 | $0.32 |
| Weights | Proprietary | Open |

## Coding

- Claude Opus 4.1: 44.4 (#73)
- Llama-3.3-70B-Instruct: 31.0 (#290)

| Benchmark | Claude Opus 4.1 | Llama-3.3-70B-Instruct |
|---|---|---|
| WeirdML | 45.9% | 14.4% |
| LMArena Coding | 1479 | 1268 |
| SWE-bench Verified | 73.3% | — |
| LMArena WebDev | 1390 | — |
| SciCode | — | 26% |
| BigCodeBench Instruct | — | 46.9% |
| LiveBench Coding | — | 36.6% |
| BigCodeBench Complete | — | 57.5% |
| ALE-Bench | 674.77 | — |
| AlgoTune | 1.34 | — |

## Agentic & Tool Use

- Claude Opus 4.1: 35.0 (#41)
- Llama-3.3-70B-Instruct: 25.8 (#105)

| Benchmark | Claude Opus 4.1 | Llama-3.3-70B-Instruct |
|---|---|---|
| Terminal-Bench | 38% | — |
| Berkeley Function Calling Leaderboard | — | 31.9% |
| GDPval | 43.6% | — |
| Cybench | 42% | — |
| DeepResearch Bench | 48.3% | — |
| BALROG | — | 23% |
| LMArena Search | 1148 | — |
| METR Time Horizons | 66.8% | — |

## Reasoning

- Claude Opus 4.1: 32.2 (#76)
- Llama-3.3-70B-Instruct: 14.1 (#327)

| Benchmark | Claude Opus 4.1 | Llama-3.3-70B-Instruct |
|---|---|---|
| SimpleBench | 60% | 19.9% |
| LMArena Hard Prompts | 1443 | 1257 |
| DTBench | 80% | 59.5% |
| LMCA | 37.1% | 17.5% |
| Epoch Capabilities Index | 144.12 | 127.33 |
| ForecastBench | 62 | 58.6 |
| CritPt | — | 0% |
| Chess Puzzles | 7% | — |
| EnigmaEval | 7.2% | — |
| EBR-Bench | 7.9% | — |
| LiveBench Reasoning | — | 50.8% |
| Mystery Game Puzzles | 21% | — |
| LiveBench Data Analysis | — | 49.5% |
| LiveBench | — | 50.2% |

## Math

- Claude Opus 4.1: 22.3 (#277)
- Llama-3.3-70B-Instruct: 15.3 (#298)

| Benchmark | Claude Opus 4.1 | Llama-3.3-70B-Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 68.9% | 5.1% |
| LMArena Math | 1431 | 1267 |
| FrontierMath (Tiers 1-3) | 12.6% | — |
| FrontierMath Tier 4 | 2.4% | — |
| LiveBench Math | — | 42.2% |
| MATH Level 5 | — | 41.6% |
| FrontierMath (Feb 2025 set) | 7.2% | — |
| FrontierMath Tier 4 (v1) | 4.2% | — |

## Knowledge

- Claude Opus 4.1: 42.0 (#101)
- Llama-3.3-70B-Instruct: 30.6 (#226)

| Benchmark | Claude Opus 4.1 | Llama-3.3-70B-Instruct |
|---|---|---|
| GPQA Diamond | 77.3% | 47.4% |
| Confabulations | 17.1% | 22.8% |
| Vectara Hallucination Rate | 11.8% | 4.1% |
| LMArena Expert | 1439 | 1225 |
| Humanity's Last Exam | 11.5% | — |
| MMLU | — | 86.3% |

## Multimodal

- Claude Opus 4.1: 26.8 (#119)
- Llama-3.3-70B-Instruct: —

| Benchmark | Claude Opus 4.1 | Llama-3.3-70B-Instruct |
|---|---|---|
| VPCT | 35% | — |

## Multilingual

- Claude Opus 4.1: 52.0 (#95)
- Llama-3.3-70B-Instruct: 39.9 (#220)

| Benchmark | Claude Opus 4.1 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Non-English | 1405 | 1236 |
| LMArena Chinese | 1427 | 1217 |
| LMArena French | 1431 | 1281 |
| LMArena German | 1413 | 1251 |
| LMArena Japanese | 1378 | 1150 |
| LMArena Korean | 1380 | 1143 |
| LMArena Russian | 1422 | 1252 |
| LMArena Spanish | 1448 | 1270 |

## Instruction Following

- Claude Opus 4.1: 75.6 (#58)
- Llama-3.3-70B-Instruct: 71.1 (#157)

| Benchmark | Claude Opus 4.1 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Instruction Following | 1435 | 1242 |
| LiveBench Instruction Following | — | 82.7% |

## Long Context

- Claude Opus 4.1: 44.5 (#63)
- Llama-3.3-70B-Instruct: 26.4 (#295)

| Benchmark | Claude Opus 4.1 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Longer Query | 1455 | 1256 |
| Fiction.LiveBench | — | 33.3% |

## Writing & Preference

- Claude Opus 4.1: 62.4 (#74)
- Llama-3.3-70B-Instruct: 47.6 (#207)

| Benchmark | Claude Opus 4.1 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Text | 1419 | 1274 |
| LMArena Creative Writing | 1412 | 1250 |
| LMArena Multi-Turn | 1444 | 1280 |
| Short-Story Creative Writing | 84.7% | — |
| LiveBench Language | — | 39.2% |

## FAQ

### Is Claude Opus 4.1 better than Llama-3.3-70B-Instruct?

Claude Opus 4.1 is the stronger model overall, scoring 41.0 to 30.6 on the Noometry Index. Llama-3.3-70B-Instruct costs 194× less per token, which makes it the better buy when Claude Opus 4.1's lead doesn't matter for your workload.

### Which is cheaper, Claude Opus 4.1 or Llama-3.3-70B-Instruct?

Llama-3.3-70B-Instruct is cheaper. It lists at $0.10 per million input tokens and $0.32 per million output tokens; Claude Opus 4.1 lists at $15 and $75.

### Is Claude Opus 4.1 or Llama-3.3-70B-Instruct better for coding?

Claude Opus 4.1 scores higher on coding benchmarks: 44.4 versus 31.0 in the Noometry coding category.

### Which has the bigger context window?

Claude Opus 4.1 does, with 200K tokens against 128K.

### How many benchmarks do Claude Opus 4.1 and Llama-3.3-70B-Instruct share?

27 benchmarks have published results for both models. Claude Opus 4.1 has 48 scored results on Noometry and Llama-3.3-70B-Instruct has 43.