Model comparison
Claude Opus 4.8 vs Llama 3.2 3B
Claude Opus 4.8 is the stronger model overall, scoring 60.7 to 28.9 on the Noometry Index. Llama 3.2 3B costs 83× less per token, which makes it the better buy when Claude Opus 4.8'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 4.8 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 4.8 leads 72.0 to 24.7.
- Llama 3.2 3B is cheaper at $0.05 / $0.33 per million input/output tokens, against $5 / $25 for Claude Opus 4.8.
- Claude Opus 4.8 accepts more context: 1M tokens versus 131K.
- Llama 3.2 3B has downloadable open weights; the other is API-only.
Side by side
| Claude Opus 4.8 | Llama 3.2 3B | |
|---|---|---|
| Provider | Anthropic | Meta |
| Noometry Index | 60.7 | 28.9 |
| Released | 2026-05-28 | 2024-09-24 |
| Weights | Proprietary | Open |
| Context window | 1M | 131K |
| Max output | 128K | 118K |
| Input $ / M tokens | $5 | $0.05 |
| Output $ / M tokens | $25 | $0.33 |
| Results tracked | 65 | 18 |
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Category by category
Coding Claude Opus 4.8 leads
Claude Opus 4.8: 59.9 (#12), Llama 3.2 3B: 27.6 (#319)
| Benchmark | Claude Opus 4.8 | Llama 3.2 3B |
|---|---|---|
| LMArena Coding | 1490 | 1098 |
| DeepSWE | 59% | — |
| FrontierCode | 46.5% | — |
| LMArena WebDev | 1556 | — |
| SciCode | 53.5% | — |
| GSO | 47.1% | — |
| WeirdML | 82.9% | — |
| BigCodeBench Instruct | — | 23.4% |
| BigCodeBench Complete | — | 28.3% |
| ALE-Bench | 1,564 | — |
Agentic & Tool Use Claude Opus 4.8 leads
Claude Opus 4.8: 47.6 (#11), Llama 3.2 3B: 20.1 (#143)
| Benchmark | Claude Opus 4.8 | Llama 3.2 3B |
|---|---|---|
| APEX-Agents | 48.9% | — |
| Berkeley Function Calling Leaderboard | — | 21.9% |
| OSWorld 2.0 | 20.6% | — |
| Remote Labor Index | 8.3% | — |
| τ²-bench Banking | 39.7% | — |
| DeepResearch Bench | 50.2% | — |
| PostTrainBench | 33.8% | — |
| BALROG | — | 10.1% |
| GBAEval | 70.9% | — |
| GDP.pdf | 24% | — |
| LMArena Search | 1204 | — |
| Vending-Bench 2 | 5,787 | — |
Reasoning Claude Opus 4.8 leads
Claude Opus 4.8: 64.7 (#16), Llama 3.2 3B: 21.0 (#228)
| Benchmark | Claude Opus 4.8 | Llama 3.2 3B |
|---|---|---|
| LMArena Hard Prompts | 1482 | 1095 |
| ARC-AGI-2 | 72.1% | — |
| SimpleBench | 64.8% | — |
| Kagi LLM Benchmark | 88.8% | — |
| NYT Connections (extended) | 91.1% | — |
| ARC-AGI-1 | 92.5% | — |
| CritPt | 20.9% | — |
| Chess Puzzles | 34% | — |
| EnigmaEval | 23.5% | — |
| EBR-Bench | 28.6% | — |
| Mystery Game Puzzles | 36% | — |
| DTBench | 94.9% | — |
| LMCA | 57.5% | — |
| Surface Evolver Bench | 87.5% | — |
| Bench to the Future 3 | 0.14 | — |
| Epoch Capabilities Index | 158.21 | — |
| ForecastBench | 59.9 | — |
Math Claude Opus 4.8 leads
Claude Opus 4.8: 78.4 (#13), Llama 3.2 3B: 32.4 (#214)
| Benchmark | Claude Opus 4.8 | Llama 3.2 3B |
|---|---|---|
| LMArena Math | 1487 | 1126 |
| FrontierMath (Tiers 1-3) | 80% | — |
| FrontierMath Tier 4 | 56.1% | — |
| MathArena Final-Answer Competitions | 91.8% | — |
| OTIS Mock AIME 2024-2025 | 98.3% | — |
| ProofBench | 69% | — |
| FrontierMath (Feb 2025 set) | 47.2% | — |
| FrontierMath Tier 4 (v1) | 31.3% | — |
Knowledge Claude Opus 4.8 leads
Claude Opus 4.8: 61.3 (#29), Llama 3.2 3B: 29.7 (#235)
| Benchmark | Claude Opus 4.8 | Llama 3.2 3B |
|---|---|---|
| LMArena Expert | 1502 | 1090 |
| GPQA Diamond | 91% | — |
| SimpleQA Verified | 53% | — |
Multimodal Not comparable
Claude Opus 4.8: 42.9 (#26), Llama 3.2 3B: —
| Benchmark | Claude Opus 4.8 | Llama 3.2 3B |
|---|---|---|
| LMArena Vision | 1294 | — |
| Blueprint-Bench 2 | 14.5% | — |
| Furniture Assembly | 42.5% | — |
| LMArena Document | 1475 | — |
Multilingual Claude Opus 4.8 leads
Claude Opus 4.8: 55.2 (#33), Llama 3.2 3B: 26.2 (#281)
| Benchmark | Claude Opus 4.8 | Llama 3.2 3B |
|---|---|---|
| LMArena Non-English | 1450 | 1019 |
| LMArena Chinese | 1507 | 1017 |
| LMArena German | 1472 | 1056 |
| LMArena Russian | 1474 | 949 |
| LMArena French | 1481 | — |
| LMArena Japanese | 1440 | — |
| LMArena Korean | 1432 | — |
| LMArena Spanish | 1466 | — |
Instruction Following Claude Opus 4.8 leads
Claude Opus 4.8: 77.4 (#24), Llama 3.2 3B: 56.0 (#275)
| Benchmark | Claude Opus 4.8 | Llama 3.2 3B |
|---|---|---|
| LMArena Instruction Following | 1476 | 1089 |
Long Context Claude Opus 4.8 leads
Claude Opus 4.8: 45.4 (#35), Llama 3.2 3B: 33.4 (#261)
| Benchmark | Claude Opus 4.8 | Llama 3.2 3B |
|---|---|---|
| LMArena Longer Query | 1483 | 1100 |
Writing & Preference Claude Opus 4.8 leads
Claude Opus 4.8: 72.0 (#16), Llama 3.2 3B: 24.7 (#307)
| Benchmark | Claude Opus 4.8 | Llama 3.2 3B |
|---|---|---|
| LMArena Text | 1461 | 1110 |
| LMArena Creative Writing | 1454 | 1094 |
| EQ-Bench Creative Writing | 1840 | 595 |
| LMArena Multi-Turn | 1476 | 1105 |
| EQ-Bench 4 | 1281 | — |
Frequently asked questions
Is Claude Opus 4.8 better than Llama 3.2 3B?
Claude Opus 4.8 is the stronger model overall, scoring 60.7 to 28.9 on the Noometry Index. Llama 3.2 3B costs 83× less per token, which makes it the better buy when Claude Opus 4.8's lead doesn't matter for your workload.
Which is cheaper, Claude Opus 4.8 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 4.8 lists at $5 and $25.
Is Claude Opus 4.8 or Llama 3.2 3B better for coding?
Claude Opus 4.8 scores higher on coding benchmarks: 59.9 versus 27.6 in the Noometry coding category.
Which has the bigger context window?
Claude Opus 4.8 does, with 1M tokens against 131K.
How many benchmarks do Claude Opus 4.8 and Llama 3.2 3B share?
14 benchmarks have published results for both models. Claude Opus 4.8 has 65 scored results on Noometry and Llama 3.2 3B has 18.