Model comparison
Claude Opus 5 vs Llama 3.2 1B
Claude Opus 5 is the stronger model overall, scoring 67.8 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 5's lead doesn't matter for your workload.
Last verified . 19 shared benchmarks.
MetaSummary
- They share 19 benchmarks with published results for both. Claude Opus 5 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 5 leads 86.2 to 10.4.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 98.9% for Claude Opus 5 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 5.
- Claude Opus 5 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 5 | Llama 3.2 1B | |
|---|---|---|
| Provider | Anthropic | Meta |
| Noometry Index | 67.8 | 20.1 |
| Released | 2026-07-24 | 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 | 57 | 22 |
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Category by category
Coding Claude Opus 5 leads
Claude Opus 5: 67.5 (#5), Llama 3.2 1B: 21.1 (#338)
| Benchmark | Claude Opus 5 | Llama 3.2 1B |
|---|---|---|
| LMArena Coding | 1534 | 1070 |
| DeepSWE | 73.6% | — |
| FrontierCode | 53.4% | — |
| CursorBench | 46.6% | — |
| LMArena WebDev | 1691 | — |
| FrontierSWE | 52% | — |
| SciCode | 56.4% | — |
| WeirdML | 91.8% | — |
| BigCodeBench Instruct | — | 8.2% |
| BigCodeBench Complete | — | 11.3% |
| ALE-Bench | 2,165 | — |
Agentic & Tool Use Claude Opus 5 leads
Claude Opus 5: 55.6 (#1), Llama 3.2 1B: 14.6 (#150)
| Benchmark | Claude Opus 5 | Llama 3.2 1B |
|---|---|---|
| BALROG | 63.4% | 6.6% |
| APEX-Agents | 65.8% | — |
| Berkeley Function Calling Leaderboard | — | 10.8% |
| OSWorld 2.0 | 31.4% | — |
| τ²-bench Banking | 48.7% | — |
| PostTrainBench | 35% | — |
| GBAEval | 79.6% | — |
| GDP.pdf | 24% | — |
| Vending-Bench 2 | 11,182 | — |
Reasoning Claude Opus 5 leads
Claude Opus 5: 77.2 (#4), Llama 3.2 1B: 16.2 (#308)
| Benchmark | Claude Opus 5 | Llama 3.2 1B |
|---|---|---|
| Chess Puzzles | 42% | 0% |
| LMArena Hard Prompts | 1526 | 1044 |
| Epoch Capabilities Index | 162.78 | 101.99 |
| ARC-AGI-2 | 90.4% | — |
| SimpleBench | 80.6% | — |
| NYT Connections (extended) | 94.3% | — |
| ARC-AGI-1 | 97.5% | — |
| CritPt | 29.1% | — |
| EBR-Bench | 45.7% | — |
| Mystery Game Puzzles | 59% | — |
| DTBench | 97.9% | — |
| LMCA | 64.5% | — |
| Bench to the Future 3 | 0.12 | — |
Math Claude Opus 5 leads
Claude Opus 5: 86.2 (#8), Llama 3.2 1B: 10.4 (#313)
| Benchmark | Claude Opus 5 | Llama 3.2 1B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 98.9% | 0.6% |
| LMArena Math | 1531 | 1086 |
| FrontierMath (Tiers 1-3) | 85.6% | — |
| FrontierMath Tier 4 | 73.2% | — |
| ProofBench | 99% | — |
Knowledge Claude Opus 5 leads
Claude Opus 5: 66.8 (#9), Llama 3.2 1B: 7.2 (#312)
| Benchmark | Claude Opus 5 | Llama 3.2 1B |
|---|---|---|
| GPQA Diamond | 93.9% | 23.9% |
| LMArena Expert | 1557 | 1007 |
| SimpleQA Verified | 59.9% | — |
Multimodal Not comparable
Claude Opus 5: 50.8 (#8), Llama 3.2 1B: —
| Benchmark | Claude Opus 5 | Llama 3.2 1B |
|---|---|---|
| LMArena Vision | 1319 | — |
| Blueprint-Bench 2 | 30.4% | — |
| Furniture Assembly | 60.8% | — |
| LMArena Document | 1516 | — |
Multilingual Claude Opus 5 leads
Claude Opus 5: 58.8 (#4), Llama 3.2 1B: 23.8 (#292)
| Benchmark | Claude Opus 5 | Llama 3.2 1B |
|---|---|---|
| LMArena Non-English | 1501 | 973 |
| LMArena Chinese | 1574 | 959 |
| LMArena German | 1524 | 1014 |
| LMArena Russian | 1507 | 941 |
| LMArena French | 1519 | — |
| LMArena Japanese | 1516 | — |
| LMArena Korean | 1521 | — |
| LMArena Spanish | 1519 | — |
Instruction Following Claude Opus 5 leads
Claude Opus 5: 79.2 (#7), Llama 3.2 1B: 52.4 (#290)
| Benchmark | Claude Opus 5 | Llama 3.2 1B |
|---|---|---|
| LMArena Instruction Following | 1517 | 1031 |
Long Context Claude Opus 5 leads
Claude Opus 5: 46.5 (#21), Llama 3.2 1B: 31.9 (#274)
| Benchmark | Claude Opus 5 | Llama 3.2 1B |
|---|---|---|
| LMArena Longer Query | 1515 | 1050 |
Writing & Preference Claude Opus 5 leads
Claude Opus 5: 79.2 (#1), Llama 3.2 1B: 21.3 (#310)
| Benchmark | Claude Opus 5 | Llama 3.2 1B |
|---|---|---|
| LMArena Text | 1507 | 1055 |
| LMArena Creative Writing | 1491 | 1033 |
| EQ-Bench Creative Writing | 2133 | 200 |
| LMArena Multi-Turn | 1499 | 1030 |
| EQ-Bench 4 | 1385 | — |
Frequently asked questions
Is Claude Opus 5 better than Llama 3.2 1B?
Claude Opus 5 is the stronger model overall, scoring 67.8 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 5's lead doesn't matter for your workload.
Which is cheaper, Claude Opus 5 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 5 lists at $5 and $25.
Is Claude Opus 5 or Llama 3.2 1B better for coding?
Claude Opus 5 scores higher on coding benchmarks: 67.5 versus 21.1 in the Noometry coding category.
Which has the bigger context window?
Claude Opus 5 does, with 1M tokens against 60K.
How many benchmarks do Claude Opus 5 and Llama 3.2 1B share?
19 benchmarks have published results for both models. Claude Opus 5 has 57 scored results on Noometry and Llama 3.2 1B has 22.