Model comparison
Claude Sonnet 4 vs Llama 3.2 1B
Claude Sonnet 4 is the stronger model overall, scoring 40.8 to 20.1 on the Noometry Index. Llama 3.2 1B costs 85× less per token, which makes it the better buy when Claude Sonnet 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 Sonnet 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 Sonnet 4 leads 57.1 to 21.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 71.1% for Claude Sonnet 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 $3 / $15 for Claude Sonnet 4.
- Claude Sonnet 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 Sonnet 4 | Llama 3.2 1B | |
|---|---|---|
| Provider | Anthropic | Meta |
| Noometry Index | 40.8 | 20.1 |
| Released | 2025-05-22 | 2024-09-24 |
| Weights | Proprietary | Open |
| Context window | 200K | 60K |
| Max output | 64K | 54K |
| Input $ / M tokens | $3 | $0.027 |
| Output $ / M tokens | $15 | $0.20 |
| Results tracked | 58 | 22 |
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Category by category
Coding Claude Sonnet 4 leads
Claude Sonnet 4: 43.5 (#88), Llama 3.2 1B: 21.1 (#338)
| Benchmark | Claude Sonnet 4 | Llama 3.2 1B |
|---|---|---|
| LMArena Coding | 1414 | 1070 |
| SWE-bench Verified (bash only) | 64.9% | — |
| Aider Polyglot | 61.3% | — |
| SciCode | 40% | — |
| GSO | 4.9% | — |
| WeirdML | 46.1% | — |
| BigCodeBench Instruct | — | 8.2% |
| BigCodeBench Complete | — | 11.3% |
| ALE-Bench | 655.35 | — |
Agentic & Tool Use Claude Sonnet 4 leads
Claude Sonnet 4: 38.5 (#31), Llama 3.2 1B: 14.6 (#150)
| Benchmark | Claude Sonnet 4 | Llama 3.2 1B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 10.8% |
| TheAgentCompany | 33.1% | — |
| Cybench | 35% | — |
| DeepResearch Bench | 46.6% | — |
| OSWorld | 43.9% | — |
| BALROG | — | 6.6% |
| METR Time Horizons | 62% | — |
Reasoning Claude Sonnet 4 leads
Claude Sonnet 4: 22.9 (#187), Llama 3.2 1B: 16.2 (#308)
| Benchmark | Claude Sonnet 4 | Llama 3.2 1B |
|---|---|---|
| LMArena Hard Prompts | 1372 | 1044 |
| Epoch Capabilities Index | 141.69 | 101.99 |
| ARC-AGI-2 | 5.9% | — |
| SimpleBench | 45.5% | — |
| Kagi LLM Benchmark | 73% | — |
| ARC-AGI-1 | 40% | — |
| CritPt | 0.3% | — |
| Chess Puzzles | — | 0% |
| EnigmaEval | 3.1% | — |
| DTBench | 77.1% | — |
| LMCA | 29% | — |
| ForecastBench | 60.2 | — |
Math Claude Sonnet 4 leads
Claude Sonnet 4: 43.3 (#80), Llama 3.2 1B: 10.4 (#313)
| Benchmark | Claude Sonnet 4 | Llama 3.2 1B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 71.1% | 0.6% |
| LMArena Math | 1375 | 1086 |
| Omni-MATH | 60.2% | — |
| MATH Level 5 | 84.4% | — |
| FrontierMath (Feb 2025 set) | 4.1% | — |
| FrontierMath Tier 4 (v1) | 0% | — |
Knowledge Claude Sonnet 4 leads
Claude Sonnet 4: 41.8 (#108), Llama 3.2 1B: 7.2 (#312)
| Benchmark | Claude Sonnet 4 | Llama 3.2 1B |
|---|---|---|
| GPQA Diamond | 79.2% | 23.9% |
| LMArena Expert | 1372 | 1007 |
| Humanity's Last Exam | 7.8% | — |
| MMLU-Pro | 84.3% | — |
| Confabulations | 13.2% | — |
| Vectara Hallucination Rate | 10.3% | — |
| GPQA (HELM) | 70.6% | — |
Multimodal Not comparable
Claude Sonnet 4: 26.2 (#121), Llama 3.2 1B: —
| Benchmark | Claude Sonnet 4 | Llama 3.2 1B |
|---|---|---|
| LMArena Vision | 1191 | — |
| GeoBench | 37% | — |
| VPCT | 34% | — |
| MindCube | 44.8% | — |
Multilingual Claude Sonnet 4 leads
Claude Sonnet 4: 46.7 (#156), Llama 3.2 1B: 23.8 (#292)
| Benchmark | Claude Sonnet 4 | Llama 3.2 1B |
|---|---|---|
| LMArena Non-English | 1333 | 973 |
| LMArena Chinese | 1350 | 959 |
| LMArena German | 1331 | 1014 |
| LMArena Russian | 1355 | 941 |
| LMArena French | 1363 | — |
| LMArena Japanese | 1302 | — |
| LMArena Korean | 1291 | — |
| LMArena Spanish | 1357 | — |
Instruction Following Claude Sonnet 4 leads
Claude Sonnet 4: 71.7 (#145), Llama 3.2 1B: 52.4 (#290)
| Benchmark | Claude Sonnet 4 | Llama 3.2 1B |
|---|---|---|
| LMArena Instruction Following | 1376 | 1031 |
| IFEval | 84% | — |
Long Context Claude Sonnet 4 leads
Claude Sonnet 4: 33.7 (#259), Llama 3.2 1B: 31.9 (#274)
| Benchmark | Claude Sonnet 4 | Llama 3.2 1B |
|---|---|---|
| LMArena Longer Query | 1398 | 1050 |
| Fiction.LiveBench | 46.9% | — |
Writing & Preference Claude Sonnet 4 leads
Claude Sonnet 4: 57.1 (#132), Llama 3.2 1B: 21.3 (#310)
| Benchmark | Claude Sonnet 4 | Llama 3.2 1B |
|---|---|---|
| LMArena Text | 1351 | 1055 |
| LMArena Creative Writing | 1345 | 1033 |
| EQ-Bench Creative Writing | 1483 | 200 |
| LMArena Multi-Turn | 1376 | 1030 |
| Short-Story Creative Writing | 81.4% | — |
| WildBench | 83.8% | — |
Frequently asked questions
Is Claude Sonnet 4 better than Llama 3.2 1B?
Claude Sonnet 4 is the stronger model overall, scoring 40.8 to 20.1 on the Noometry Index. Llama 3.2 1B costs 85× less per token, which makes it the better buy when Claude Sonnet 4's lead doesn't matter for your workload.
Which is cheaper, Claude Sonnet 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 Sonnet 4 lists at $3 and $15.
Is Claude Sonnet 4 or Llama 3.2 1B better for coding?
Claude Sonnet 4 scores higher on coding benchmarks: 43.5 versus 21.1 in the Noometry coding category.
Which has the bigger context window?
Claude Sonnet 4 does, with 200K tokens against 60K.
How many benchmarks do Claude Sonnet 4 and Llama 3.2 1B share?
17 benchmarks have published results for both models. Claude Sonnet 4 has 58 scored results on Noometry and Llama 3.2 1B has 22.