Model comparison
Llama 3.2 1B vs Phi-4 Mini
Phi-4 Mini is the stronger model overall, scoring 30.4 to 19.9 on the Noometry Index. Llama 3.2 1B costs 1.9× less per token, which makes it the better buy when Phi-4 Mini's lead doesn't matter for your workload.
Last verified . 0 shared benchmarks.
Meta
MicrosoftSummary
- The widest gap is in knowledge, where Phi-4 Mini leads 24.8 to 7.2.
- Llama 3.2 1B is cheaper at $0.027 / $0.20 per million input/output tokens, against $0.075 / $0.30 for Phi-4 Mini.
- Phi-4 Mini accepts more context: 128K tokens versus 60K.
Side by side
| Llama 3.2 1B | Phi-4 Mini | |
|---|---|---|
| Provider | Meta | Microsoft |
| Noometry Index | 19.9 | 30.4 |
| Released | 2024-09-24 | 2024-12-11 |
| Weights | Open | Open |
| Context window | 60K | 128K |
| Max output | 54K | 4K |
| Input $ / M tokens | $0.027 | $0.075 |
| Output $ / M tokens | $0.20 | $0.30 |
| Results tracked | 22 | 3 |
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Category by category
Coding Phi-4 Mini leads
Llama 3.2 1B: 20.6 (#333), Phi-4 Mini: 27.6 (#312)
| Benchmark | Llama 3.2 1B | Phi-4 Mini |
|---|---|---|
| SciCode | — | 10.8% |
| BigCodeBench Instruct | 8.2% | — |
| LMArena Coding | 1071 | — |
| BigCodeBench Complete | 11.3% | — |
Agentic & Tool Use Not comparable
Llama 3.2 1B: 14.5 (#142), Phi-4 Mini: —
| Benchmark | Llama 3.2 1B | Phi-4 Mini |
|---|---|---|
| Berkeley Function Calling Leaderboard | 10.8% | — |
| BALROG | 6.6% | — |
Reasoning Phi-4 Mini leads
Llama 3.2 1B: 15.6 (#304), Phi-4 Mini: 21.7 (#190)
| Benchmark | Llama 3.2 1B | Phi-4 Mini |
|---|---|---|
| CritPt | — | 0% |
| Chess Puzzles | 0% | — |
| LMArena Hard Prompts | 1044 | — |
| Epoch Capabilities Index | 101.95 | — |
Math Not comparable
Llama 3.2 1B: 9.7 (#308), Phi-4 Mini: —
| Benchmark | Llama 3.2 1B | Phi-4 Mini |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 0.6% | — |
| LMArena Math | 1086 | — |
Knowledge Phi-4 Mini leads
Llama 3.2 1B: 7.2 (#307), Phi-4 Mini: 24.8 (#261)
| Benchmark | Llama 3.2 1B | Phi-4 Mini |
|---|---|---|
| GPQA Diamond | 23.9% | — |
| Vectara Hallucination Rate | — | 23.5% |
| LMArena Expert | 1007 | — |
Multilingual Not comparable
Llama 3.2 1B: 23.9 (#287), Phi-4 Mini: —
| Benchmark | Llama 3.2 1B | Phi-4 Mini |
|---|---|---|
| LMArena Non-English | 973 | — |
| LMArena Chinese | 958 | — |
| LMArena German | 1014 | — |
| LMArena Russian | 941 | — |
Instruction Following Not comparable
Llama 3.2 1B: 52.6 (#285), Phi-4 Mini: —
| Benchmark | Llama 3.2 1B | Phi-4 Mini |
|---|---|---|
| LMArena Instruction Following | 1031 | — |
Long Context Not comparable
Llama 3.2 1B: 31.9 (#269), Phi-4 Mini: —
| Benchmark | Llama 3.2 1B | Phi-4 Mini |
|---|---|---|
| LMArena Longer Query | 1050 | — |
Writing & Preference Not comparable
Llama 3.2 1B: 21.4 (#305), Phi-4 Mini: —
| Benchmark | Llama 3.2 1B | Phi-4 Mini |
|---|---|---|
| LMArena Text | 1055 | — |
| LMArena Creative Writing | 1033 | — |
| EQ-Bench Creative Writing | 200 | — |
| LMArena Multi-Turn | 1030 | — |
Frequently asked questions
Is Llama 3.2 1B better than Phi-4 Mini?
Phi-4 Mini is the stronger model overall, scoring 30.4 to 19.9 on the Noometry Index. Llama 3.2 1B costs 1.9× less per token, which makes it the better buy when Phi-4 Mini's lead doesn't matter for your workload.
Which is cheaper, Llama 3.2 1B or Phi-4 Mini?
Llama 3.2 1B is cheaper. It lists at $0.027 per million input tokens and $0.20 per million output tokens; Phi-4 Mini lists at $0.075 and $0.30.
Is Llama 3.2 1B or Phi-4 Mini better for coding?
Phi-4 Mini scores higher on coding benchmarks: 27.6 versus 20.6 in the Noometry coding category.
Which has the bigger context window?
Phi-4 Mini does, with 128K tokens against 60K.
How many benchmarks do Llama 3.2 1B and Phi-4 Mini share?
0 benchmarks have published results for both models. Llama 3.2 1B has 22 scored results on Noometry and Phi-4 Mini has 3.