Model comparison
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.
Last verified . 18 shared benchmarks.
MetaSummary
- 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.
Side by side
| Claude Opus 4.7 | Llama 3.2 1B | |
|---|---|---|
| Provider | Anthropic | Meta |
| Noometry Index | 58.3 | 20.1 |
| Released | 2026-04-14 | 2024-09-24 |
| Weights | Proprietary | Open |
| Context window | 1M | 60K |
| Max output | 128K | 54K |
| Input $ / M tokens | $5 | $0.027 |
| Output $ / M tokens | $25 | $0.20 |
| Results tracked | 66 | 22 |
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Category by category
Coding Claude Opus 4.7 leads
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 leads
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 leads
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 leads
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 leads
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 Not comparable
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 leads
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 leads
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 leads
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 leads
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 | — |
Frequently asked questions
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.