Model comparison
Llama 3.2 1B vs Llama 4 Maverick
Llama 4 Maverick is the stronger model overall, scoring 30.9 to 20.1 on the Noometry Index. Llama 3.2 1B costs 4.3× less per token, which makes it the better buy when Llama 4 Maverick's lead doesn't matter for your workload.
Last verified . 20 shared benchmarks.
MetaSummary
- They share 20 benchmarks with published results for both. Llama 3.2 1B scores higher in 2 categories and Llama 4 Maverick in 7 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Llama 4 Maverick leads 33.4 to 7.2.
- The biggest single-benchmark swing is BigCodeBench Complete: 11.3% for Llama 3.2 1B and 61.4% for Llama 4 Maverick.
- Llama 3.2 1B is cheaper at $0.027 / $0.20 per million input/output tokens, against $0.19 / $0.65 for Llama 4 Maverick.
- Llama 4 Maverick accepts more context: 128K tokens versus 60K.
Side by side
| Llama 3.2 1B | Llama 4 Maverick | |
|---|---|---|
| Provider | Meta | Meta |
| Noometry Index | 20.1 | 30.9 |
| Released | 2024-09-24 | 2025-04-05 |
| Weights | Open | Open |
| Context window | 60K | 128K |
| Max output | 54K | 4K |
| Input $ / M tokens | $0.027 | $0.19 |
| Output $ / M tokens | $0.20 | $0.65 |
| Results tracked | 22 | 54 |
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Category by category
Coding Llama 4 Maverick leads
Llama 3.2 1B: 21.1 (#338), Llama 4 Maverick: 26.6 (#324)
| Benchmark | Llama 3.2 1B | Llama 4 Maverick |
|---|---|---|
| BigCodeBench Instruct | 8.2% | 49.7% |
| LMArena Coding | 1070 | 1302 |
| BigCodeBench Complete | 11.3% | 61.4% |
| SWE-bench Verified (bash only) | — | 21% |
| Aider Polyglot | — | 15.6% |
| SciCode | — | 33.1% |
| WeirdML | — | 24.5% |
| ALE-Bench | — | 172.97 |
Agentic & Tool Use Llama 4 Maverick leads
Llama 3.2 1B: 14.6 (#150), Llama 4 Maverick: 28.2 (#91)
| Benchmark | Llama 3.2 1B | Llama 4 Maverick |
|---|---|---|
| Berkeley Function Calling Leaderboard | 10.8% | 37.3% |
| BALROG | 6.6% | — |
Reasoning Llama 3.2 1B leads
Llama 3.2 1B: 16.2 (#308), Llama 4 Maverick: 10.1 (#342)
| Benchmark | Llama 3.2 1B | Llama 4 Maverick |
|---|---|---|
| LMArena Hard Prompts | 1044 | 1281 |
| Epoch Capabilities Index | 101.99 | 132.2 |
| ARC-AGI-2 | — | 0% |
| SimpleBench | — | 27.7% |
| Kagi LLM Benchmark | — | 55.9% |
| NYT Connections (extended) | — | 8% |
| ARC-AGI-1 | — | 4.4% |
| CritPt | — | 0% |
| Chess Puzzles | 0% | — |
| EnigmaEval | — | 0.6% |
| DTBench | — | 61.9% |
| LMCA | — | 15.9% |
| ForecastBench | — | 57.5 |
Math Llama 4 Maverick leads
Llama 3.2 1B: 10.4 (#313), Llama 4 Maverick: 26.0 (#262)
| Benchmark | Llama 3.2 1B | Llama 4 Maverick |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 0.6% | 20.6% |
| LMArena Math | 1086 | 1299 |
| Omni-MATH | — | 42.2% |
| MATH Level 5 | — | 73% |
| FrontierMath (Feb 2025 set) | — | 0.7% |
Knowledge Llama 4 Maverick leads
Llama 3.2 1B: 7.2 (#312), Llama 4 Maverick: 33.4 (#204)
| Benchmark | Llama 3.2 1B | Llama 4 Maverick |
|---|---|---|
| GPQA Diamond | 23.9% | 67% |
| LMArena Expert | 1007 | 1259 |
| Humanity's Last Exam | — | 5.7% |
| MMLU-Pro | — | 81% |
| Confabulations | — | 22.6% |
| Vectara Hallucination Rate | — | 8.2% |
| GPQA (HELM) | — | 65% |
Multimodal Not comparable
Llama 3.2 1B: —, Llama 4 Maverick: 31.6 (#105)
| Benchmark | Llama 3.2 1B | Llama 4 Maverick |
|---|---|---|
| LMArena Vision | — | 1142 |
| GeoBench | — | 52% |
| SpatialViz-Bench | — | 31.8% |
Multilingual Llama 4 Maverick leads
Llama 3.2 1B: 23.8 (#292), Llama 4 Maverick: 42.2 (#195)
| Benchmark | Llama 3.2 1B | Llama 4 Maverick |
|---|---|---|
| LMArena Non-English | 973 | 1269 |
| LMArena Chinese | 959 | 1277 |
| LMArena German | 1014 | 1291 |
| LMArena Russian | 941 | 1286 |
| LMArena French | — | 1259 |
| LMArena Japanese | — | 1207 |
| LMArena Korean | — | 1203 |
| LMArena Spanish | — | 1293 |
Instruction Following Llama 4 Maverick leads
Llama 3.2 1B: 52.4 (#290), Llama 4 Maverick: 71.7 (#146)
| Benchmark | Llama 3.2 1B | Llama 4 Maverick |
|---|---|---|
| LMArena Instruction Following | 1031 | 1267 |
| IFEval | — | 90.8% |
Long Context Too close to call
Llama 3.2 1B: 31.9 (#274), Llama 4 Maverick: 31.4 (#279)
| Benchmark | Llama 3.2 1B | Llama 4 Maverick |
|---|---|---|
| LMArena Longer Query | 1050 | 1280 |
| Fiction.LiveBench | — | 46.2% |
Writing & Preference Llama 4 Maverick leads
Llama 3.2 1B: 21.3 (#310), Llama 4 Maverick: 38.8 (#252)
| Benchmark | Llama 3.2 1B | Llama 4 Maverick |
|---|---|---|
| LMArena Text | 1055 | 1287 |
| LMArena Creative Writing | 1033 | 1267 |
| EQ-Bench Creative Writing | 200 | 860 |
| LMArena Multi-Turn | 1030 | 1289 |
| Short-Story Creative Writing | — | 62% |
| WildBench | — | 80% |
Frequently asked questions
Is Llama 3.2 1B better than Llama 4 Maverick?
Llama 4 Maverick is the stronger model overall, scoring 30.9 to 20.1 on the Noometry Index. Llama 3.2 1B costs 4.3× less per token, which makes it the better buy when Llama 4 Maverick's lead doesn't matter for your workload.
Which is cheaper, Llama 3.2 1B or Llama 4 Maverick?
Llama 3.2 1B is cheaper. It lists at $0.027 per million input tokens and $0.20 per million output tokens; Llama 4 Maverick lists at $0.19 and $0.65.
Is Llama 3.2 1B or Llama 4 Maverick better for coding?
Llama 4 Maverick scores higher on coding benchmarks: 26.6 versus 21.1 in the Noometry coding category.
Which has the bigger context window?
Llama 4 Maverick does, with 128K tokens against 60K.
How many benchmarks do Llama 3.2 1B and Llama 4 Maverick share?
20 benchmarks have published results for both models. Llama 3.2 1B has 22 scored results on Noometry and Llama 4 Maverick has 54.