Model comparison
Claude Opus 4 vs Llama 3.2 3B
Claude Opus 4 is the stronger model overall, scoring 42.9 to 28.4 on the Noometry Index. Llama 3.2 3B costs 250× less per token, which makes it the better buy when Claude Opus 4's lead doesn't matter for your workload.
Last verified . 14 shared benchmarks.
MetaSummary
- They share 14 benchmarks with published results for both. Claude Opus 4 scores higher in 9 categories and Llama 3.2 3B in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where Claude Opus 4 leads 61.4 to 24.9.
- Llama 3.2 3B is cheaper at $0.05 / $0.33 per million input/output tokens, against $15 / $75 for Claude Opus 4.
- Claude Opus 4 accepts more context: 200K tokens versus 131K.
- Llama 3.2 3B has downloadable open weights; the other is API-only.
Side by side
| Claude Opus 4 | Llama 3.2 3B | |
|---|---|---|
| Provider | Anthropic | Meta |
| Noometry Index | 42.9 | 28.4 |
| Released | 2025-05-22 | 2024-09-24 |
| Weights | Proprietary | Open |
| Context window | 200K | 131K |
| Max output | 32K | 118K |
| Input $ / M tokens | $15 | $0.05 |
| Output $ / M tokens | $75 | $0.33 |
| Results tracked | 56 | 18 |
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Category by category
Coding Claude Opus 4 leads
Claude Opus 4: 47.8 (#55), Llama 3.2 3B: 27.1 (#314)
| Benchmark | Claude Opus 4 | Llama 3.2 3B |
|---|---|---|
| LMArena Coding | 1442 | 1098 |
| SWE-bench Verified | 70.7% | — |
| SWE-bench Verified (bash only) | 67.6% | — |
| Aider Polyglot | 72% | — |
| GSO | 6.9% | — |
| WeirdML | 43.7% | — |
| BigCodeBench Instruct | — | 23.4% |
| BigCodeBench Complete | — | 28.3% |
| AlgoTune | 1.33 | — |
Agentic & Tool Use Claude Opus 4 leads
Claude Opus 4: 34.5 (#39), Llama 3.2 3B: 19.9 (#135)
| Benchmark | Claude Opus 4 | Llama 3.2 3B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 21.9% |
| Cybench | 38% | — |
| DeepResearch Bench | 46.8% | — |
| BALROG | — | 10.1% |
| LMArena Search | 1127 | — |
| METR Time Horizons | 63.9% | — |
Reasoning Claude Opus 4 leads
Claude Opus 4: 27.4 (#104), Llama 3.2 3B: 20.1 (#225)
| Benchmark | Claude Opus 4 | Llama 3.2 3B |
|---|---|---|
| LMArena Hard Prompts | 1399 | 1095 |
| ARC-AGI-2 | 8.6% | — |
| SimpleBench | 58.8% | — |
| Kagi LLM Benchmark | 74.3% | — |
| ARC-AGI-1 | 35.7% | — |
| CritPt | 0.3% | — |
| EnigmaEval | 5.6% | — |
| DTBench | 81.6% | — |
| LMCA | 37.4% | — |
| Epoch Capabilities Index | 142.67 | — |
| ForecastBench | 61.1 | — |
Math Claude Opus 4 leads
Claude Opus 4: 40.2 (#80), Llama 3.2 3B: 29.9 (#213)
| Benchmark | Claude Opus 4 | Llama 3.2 3B |
|---|---|---|
| LMArena Math | 1391 | 1126 |
| OTIS Mock AIME 2024-2025 | 64.4% | — |
| 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: 43.9 (#82), Llama 3.2 3B: 29.1 (#232)
| Benchmark | Claude Opus 4 | Llama 3.2 3B |
|---|---|---|
| LMArena Expert | 1384 | 1090 |
| GPQA Diamond | 76.3% | — |
| 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: 30.9 (#101), Llama 3.2 3B: —
| Benchmark | Claude Opus 4 | Llama 3.2 3B |
|---|---|---|
| LMArena Vision | 1192 | — |
| GeoBench | 49% | — |
| VPCT | 38% | — |
Multilingual Claude Opus 4 leads
Claude Opus 4: 49.0 (#133), Llama 3.2 3B: 26.4 (#276)
| Benchmark | Claude Opus 4 | Llama 3.2 3B |
|---|---|---|
| LMArena Non-English | 1362 | 1019 |
| LMArena Chinese | 1386 | 1017 |
| LMArena German | 1391 | 1056 |
| LMArena Russian | 1392 | 949 |
| LMArena French | 1370 | — |
| LMArena Japanese | 1332 | — |
| LMArena Korean | 1318 | — |
| LMArena Spanish | 1388 | — |
Instruction Following Claude Opus 4 leads
Claude Opus 4: 77.3 (#26), Llama 3.2 3B: 56.3 (#270)
| Benchmark | Claude Opus 4 | Llama 3.2 3B |
|---|---|---|
| LMArena Instruction Following | 1406 | 1089 |
| IFEval | 91.8% | — |
Long Context Claude Opus 4 leads
Claude Opus 4: 39.5 (#167), Llama 3.2 3B: 33.4 (#256)
| Benchmark | Claude Opus 4 | Llama 3.2 3B |
|---|---|---|
| LMArena Longer Query | 1422 | 1101 |
| Fiction.LiveBench | 61.1% | — |
Writing & Preference Claude Opus 4 leads
Claude Opus 4: 61.4 (#85), Llama 3.2 3B: 24.9 (#302)
| Benchmark | Claude Opus 4 | Llama 3.2 3B |
|---|---|---|
| LMArena Text | 1377 | 1110 |
| LMArena Creative Writing | 1386 | 1094 |
| EQ-Bench Creative Writing | 1580 | 595 |
| LMArena Multi-Turn | 1396 | 1105 |
| Short-Story Creative Writing | 83.6% | — |
| WildBench | 85.2% | — |
Frequently asked questions
Is Claude Opus 4 better than Llama 3.2 3B?
Claude Opus 4 is the stronger model overall, scoring 42.9 to 28.4 on the Noometry Index. Llama 3.2 3B costs 250× 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 3B?
Llama 3.2 3B is cheaper. It lists at $0.05 per million input tokens and $0.33 per million output tokens; Claude Opus 4 lists at $15 and $75.
Is Claude Opus 4 or Llama 3.2 3B better for coding?
Claude Opus 4 scores higher on coding benchmarks: 47.8 versus 27.1 in the Noometry coding category.
Which has the bigger context window?
Claude Opus 4 does, with 200K tokens against 131K.
How many benchmarks do Claude Opus 4 and Llama 3.2 3B share?
14 benchmarks have published results for both models. Claude Opus 4 has 56 scored results on Noometry and Llama 3.2 3B has 18.