Model comparison
Claude Opus 4 vs Llama 3.2 1B
Claude Opus 4 is the stronger model overall, scoring 43.1 to 20.1 on the Noometry Index. Llama 3.2 1B costs 426× less per token, which makes it the better buy when Claude Opus 4's lead doesn't matter for your workload.
Last verified . 17 shared benchmarks.
MetaSummary
- They share 17 benchmarks with published results for both. Claude Opus 4 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 writing & preference, where Claude Opus 4 leads 61.2 to 21.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 64.4% for Claude Opus 4 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 $15 / $75 for Claude Opus 4.
- Claude Opus 4 accepts more context: 200K tokens versus 60K.
- Llama 3.2 1B has downloadable open weights; the other is API-only.
Side by side
| Claude Opus 4 | Llama 3.2 1B | |
|---|---|---|
| Provider | Anthropic | Meta |
| Noometry Index | 43.1 | 20.1 |
| Released | 2025-05-22 | 2024-09-24 |
| Weights | Proprietary | Open |
| Context window | 200K | 60K |
| Max output | 32K | 54K |
| Input $ / M tokens | $15 | $0.027 |
| Output $ / M tokens | $75 | $0.20 |
| Results tracked | 56 | 22 |
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Category by category
Coding Claude Opus 4 leads
Claude Opus 4: 47.2 (#62), Llama 3.2 1B: 21.1 (#338)
| Benchmark | Claude Opus 4 | Llama 3.2 1B |
|---|---|---|
| LMArena Coding | 1442 | 1070 |
| SWE-bench Verified | 70.7% | — |
| SWE-bench Verified (bash only) | 67.6% | — |
| Aider Polyglot | 72% | — |
| GSO | 6.9% | — |
| WeirdML | 43.7% | — |
| BigCodeBench Instruct | — | 8.2% |
| BigCodeBench Complete | — | 11.3% |
| AlgoTune | 1.33 | — |
Agentic & Tool Use Claude Opus 4 leads
Claude Opus 4: 34.8 (#42), Llama 3.2 1B: 14.6 (#150)
| Benchmark | Claude Opus 4 | Llama 3.2 1B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 10.8% |
| Cybench | 38% | — |
| DeepResearch Bench | 46.8% | — |
| BALROG | — | 6.6% |
| LMArena Search | 1127 | — |
| METR Time Horizons | 63.9% | — |
Reasoning Claude Opus 4 leads
Claude Opus 4: 27.3 (#121), Llama 3.2 1B: 16.2 (#308)
| Benchmark | Claude Opus 4 | Llama 3.2 1B |
|---|---|---|
| LMArena Hard Prompts | 1399 | 1044 |
| Epoch Capabilities Index | 142.67 | 101.99 |
| ARC-AGI-2 | 8.6% | — |
| SimpleBench | 58.8% | — |
| Kagi LLM Benchmark | 74.3% | — |
| ARC-AGI-1 | 35.7% | — |
| CritPt | 0.3% | — |
| Chess Puzzles | — | 0% |
| EnigmaEval | 5.6% | — |
| DTBench | 81.6% | — |
| LMCA | 37.4% | — |
| ForecastBench | 61.1 | — |
Math Claude Opus 4 leads
Claude Opus 4: 42.0 (#86), Llama 3.2 1B: 10.4 (#313)
| Benchmark | Claude Opus 4 | Llama 3.2 1B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 64.4% | 0.6% |
| LMArena Math | 1390 | 1086 |
| Omni-MATH | 61.6% | — |
| MATH Level 5 | 85% | — |
| FrontierMath (Feb 2025 set) | 4.5% | — |
| FrontierMath Tier 4 (v1) | 4.2% | — |
Knowledge Claude Opus 4 leads
Claude Opus 4: 44.0 (#88), Llama 3.2 1B: 7.2 (#312)
| Benchmark | Claude Opus 4 | Llama 3.2 1B |
|---|---|---|
| GPQA Diamond | 76.3% | 23.9% |
| LMArena Expert | 1386 | 1007 |
| Humanity's Last Exam | 10.7% | — |
| MMLU-Pro | 87.5% | — |
| Confabulations | 15.9% | — |
| Vectara Hallucination Rate | 12% | — |
| GPQA (HELM) | 70.8% | — |
Multimodal Not comparable
Claude Opus 4: 31.5 (#106), Llama 3.2 1B: —
| Benchmark | Claude Opus 4 | Llama 3.2 1B |
|---|---|---|
| LMArena Vision | 1192 | — |
| GeoBench | 49% | — |
| VPCT | 38% | — |
Multilingual Claude Opus 4 leads
Claude Opus 4: 48.8 (#138), Llama 3.2 1B: 23.8 (#292)
| Benchmark | Claude Opus 4 | Llama 3.2 1B |
|---|---|---|
| LMArena Non-English | 1362 | 973 |
| LMArena Chinese | 1386 | 959 |
| LMArena German | 1391 | 1014 |
| LMArena Russian | 1392 | 941 |
| LMArena French | 1372 | — |
| LMArena Japanese | 1331 | — |
| LMArena Korean | 1321 | — |
| LMArena Spanish | 1389 | — |
Instruction Following Claude Opus 4 leads
Claude Opus 4: 77.1 (#28), Llama 3.2 1B: 52.4 (#290)
| Benchmark | Claude Opus 4 | Llama 3.2 1B |
|---|---|---|
| LMArena Instruction Following | 1406 | 1031 |
| IFEval | 91.8% | — |
Long Context Claude Opus 4 leads
Claude Opus 4: 39.6 (#172), Llama 3.2 1B: 31.9 (#274)
| Benchmark | Claude Opus 4 | Llama 3.2 1B |
|---|---|---|
| LMArena Longer Query | 1422 | 1050 |
| Fiction.LiveBench | 61.1% | — |
Writing & Preference Claude Opus 4 leads
Claude Opus 4: 61.2 (#89), Llama 3.2 1B: 21.3 (#310)
| Benchmark | Claude Opus 4 | Llama 3.2 1B |
|---|---|---|
| LMArena Text | 1377 | 1055 |
| LMArena Creative Writing | 1387 | 1033 |
| EQ-Bench Creative Writing | 1580 | 200 |
| LMArena Multi-Turn | 1396 | 1030 |
| Short-Story Creative Writing | 83.6% | — |
| WildBench | 85.2% | — |
Frequently asked questions
Is Claude Opus 4 better than Llama 3.2 1B?
Claude Opus 4 is the stronger model overall, scoring 43.1 to 20.1 on the Noometry Index. Llama 3.2 1B costs 426× 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 3.2 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 lists at $15 and $75.
Is Claude Opus 4 or Llama 3.2 1B better for coding?
Claude Opus 4 scores higher on coding benchmarks: 47.2 versus 21.1 in the Noometry coding category.
Which has the bigger context window?
Claude Opus 4 does, with 200K tokens against 60K.
How many benchmarks do Claude Opus 4 and Llama 3.2 1B share?
17 benchmarks have published results for both models. Claude Opus 4 has 56 scored results on Noometry and Llama 3.2 1B has 22.