# Claude Opus 4.7 vs Llama 3.2 1B

> Claude Opus 4.7 is the stronger model overall, scoring 58.3 to 20.1 on the Noometry Index. Llama 3.2 1B costs 142× less per token, which makes it the better buy when Claude Opus 4.7's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/claude-opus-4-7-vs-llama-3-2-1b
- Last updated: 2026-10-10
- Shared benchmarks: 18

## Summary

- They share 18 benchmarks with published results for both. Claude Opus 4.7 scores higher in 9 categories and Llama 3.2 1B in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where Claude Opus 4.7 leads 66.7 to 10.4.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 97.8% for Claude Opus 4.7 and 0.6% for Llama 3.2 1B.
- Llama 3.2 1B is cheaper at $0.027 / $0.20 per million input/output tokens, against $5 / $25 for Claude Opus 4.7.
- Claude Opus 4.7 accepts more context: 1M tokens versus 60K.
- Llama 3.2 1B has downloadable open weights; the other is API-only.

## Snapshot

| | Claude Opus 4.7 | Llama 3.2 1B |
|---|---|---|
| Provider | Anthropic | Meta |
| Noometry Index | 58.3 | 20.1 |
| Rank | 19 | 354 |
| Context | 1M | 60K |
| Input $/M | $5 | $0.027 |
| Output $/M | $25 | $0.20 |
| Weights | Proprietary | Open |

## Coding

- Claude Opus 4.7: 59.6 (#13)
- Llama 3.2 1B: 21.1 (#338)

| Benchmark | Claude Opus 4.7 | Llama 3.2 1B |
|---|---|---|
| LMArena Coding | 1518 | 1070 |
| SWE-bench Verified | 83.5% | — |
| FrontierCode | 38.5% | — |
| LMArena WebDev | 1558 | — |
| SciCode | 54.5% | — |
| GSO | 44.1% | — |
| WeirdML | 76.4% | — |
| BigCodeBench Instruct | — | 8.2% |
| MirrorCode | 31.1% | — |
| BigCodeBench Complete | — | 11.3% |
| ALE-Bench | 1,323 | — |

## Agentic & Tool Use

- Claude Opus 4.7: 47.9 (#10)
- Llama 3.2 1B: 14.6 (#150)

| Benchmark | Claude Opus 4.7 | Llama 3.2 1B |
|---|---|---|
| Terminal-Bench | 80.2% | — |
| APEX-Agents | 49.2% | — |
| Berkeley Function Calling Leaderboard | — | 10.8% |
| OSWorld 2.0 | 18.2% | — |
| τ²-bench Banking | 40.2% | — |
| PostTrainBench | 28.6% | — |
| BALROG | — | 6.6% |
| ExploitBench | 26.5% | — |
| GBAEval | 43.8% | — |
| GDP.pdf | 21% | — |
| LMArena Search | 1233 | — |
| Vending-Bench 2 | 10,937 | — |

## Reasoning

- Claude Opus 4.7: 53.8 (#29)
- Llama 3.2 1B: 16.2 (#308)

| Benchmark | Claude Opus 4.7 | Llama 3.2 1B |
|---|---|---|
| Chess Puzzles | 30% | 0% |
| LMArena Hard Prompts | 1506 | 1044 |
| Epoch Capabilities Index | 156.25 | 101.99 |
| 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% | — |
| DTBench | 94.7% | — |
| LMCA | 52.2% | — |
| ForecastBench | 60.3 | — |

## Math

- Claude Opus 4.7: 66.7 (#26)
- Llama 3.2 1B: 10.4 (#313)

| Benchmark | Claude Opus 4.7 | Llama 3.2 1B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 97.8% | 0.6% |
| LMArena Math | 1499 | 1086 |
| FrontierMath (Tiers 1-3) | 70.2% | — |
| FrontierMath Tier 4 | 31.7% | — |
| MathArena Final-Answer Competitions | 73.6% | — |
| ProofBench | 54% | — |
| FrontierMath (Feb 2025 set) | 43.8% | — |
| FrontierMath Tier 4 (v1) | 22.9% | — |

## Knowledge

- Claude Opus 4.7: 62.6 (#23)
- Llama 3.2 1B: 7.2 (#312)

| Benchmark | Claude Opus 4.7 | Llama 3.2 1B |
|---|---|---|
| GPQA Diamond | 90.2% | 23.9% |
| LMArena Expert | 1521 | 1007 |
| Humanity's Last Exam | 36.2% | — |
| SimpleQA Verified | 51.7% | — |
| Vectara Hallucination Rate | 12% | — |

## Multimodal

- Claude Opus 4.7: 41.2 (#38)
- Llama 3.2 1B: —

| Benchmark | Claude Opus 4.7 | Llama 3.2 1B |
|---|---|---|
| LMArena Vision | 1316 | — |
| Blueprint-Bench 2 | 24.5% | — |
| Furniture Assembly | 33.3% | — |
| LMArena Document | 1495 | — |

## Multilingual

- Claude Opus 4.7: 57.3 (#10)
- Llama 3.2 1B: 23.8 (#292)

| Benchmark | Claude Opus 4.7 | Llama 3.2 1B |
|---|---|---|
| LMArena Non-English | 1480 | 973 |
| LMArena Chinese | 1531 | 959 |
| LMArena German | 1495 | 1014 |
| LMArena Russian | 1494 | 941 |
| LMArena French | 1503 | — |
| LMArena Japanese | 1472 | — |
| LMArena Korean | 1464 | — |
| LMArena Spanish | 1495 | — |

## Instruction Following

- Claude Opus 4.7: 78.4 (#10)
- Llama 3.2 1B: 52.4 (#290)

| Benchmark | Claude Opus 4.7 | Llama 3.2 1B |
|---|---|---|
| LMArena Instruction Following | 1498 | 1031 |

## Long Context

- Claude Opus 4.7: 46.2 (#25)
- Llama 3.2 1B: 31.9 (#274)

| Benchmark | Claude Opus 4.7 | Llama 3.2 1B |
|---|---|---|
| LMArena Longer Query | 1505 | 1050 |

## Writing & Preference

- Claude Opus 4.7: 75.1 (#8)
- Llama 3.2 1B: 21.3 (#310)

| Benchmark | Claude Opus 4.7 | Llama 3.2 1B |
|---|---|---|
| LMArena Text | 1490 | 1055 |
| LMArena Creative Writing | 1486 | 1033 |
| EQ-Bench Creative Writing | 1914 | 200 |
| LMArena Multi-Turn | 1505 | 1030 |
| EQ-Bench 4 | 1311 | — |

## FAQ

### Is Claude Opus 4.7 better than Llama 3.2 1B?

Claude Opus 4.7 is the stronger model overall, scoring 58.3 to 20.1 on the Noometry Index. Llama 3.2 1B costs 142× less per token, which makes it the better buy when Claude Opus 4.7's lead doesn't matter for your workload.

### Which is cheaper, Claude Opus 4.7 or Llama 3.2 1B?

Llama 3.2 1B is cheaper. It lists at $0.027 per million input tokens and $0.20 per million output tokens; Claude Opus 4.7 lists at $5 and $25.

### Is Claude Opus 4.7 or Llama 3.2 1B better for coding?

Claude Opus 4.7 scores higher on coding benchmarks: 59.6 versus 21.1 in the Noometry coding category.

### Which has the bigger context window?

Claude Opus 4.7 does, with 1M tokens against 60K.

### How many benchmarks do Claude Opus 4.7 and Llama 3.2 1B share?

18 benchmarks have published results for both models. Claude Opus 4.7 has 66 scored results on Noometry and Llama 3.2 1B has 22.