Model comparison
Gemini 2.5 Flash-Lite vs Llama 3.2 1B
Gemini 2.5 Flash-Lite is the stronger model overall, scoring 37.0 to 20.1 on the Noometry Index. Llama 3.2 1B costs 2.5× less per token, which makes it the better buy when Gemini 2.5 Flash-Lite's lead doesn't matter for your workload.
Last verified . 15 shared benchmarks.
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MetaSummary
- They share 15 benchmarks with published results for both. Gemini 2.5 Flash-Lite 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 Gemini 2.5 Flash-Lite leads 56.8 to 21.3.
- The biggest single-benchmark swing is Berkeley Function Calling Leaderboard: 36.9% for Gemini 2.5 Flash-Lite and 10.8% for Llama 3.2 1B.
- Llama 3.2 1B is cheaper at $0.027 / $0.20 per million input/output tokens, against $0.10 / $0.40 for Gemini 2.5 Flash-Lite.
- Gemini 2.5 Flash-Lite accepts more context: 1.05M tokens versus 60K.
- Llama 3.2 1B has downloadable open weights; the other is API-only.
Side by side
| Gemini 2.5 Flash-Lite | Llama 3.2 1B | |
|---|---|---|
| Provider | Meta | |
| Noometry Index | 37.0 | 20.1 |
| Released | 2025-06-17 | 2024-09-24 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 60K |
| Max output | 66K | 54K |
| Input $ / M tokens | $0.10 | $0.027 |
| Output $ / M tokens | $0.40 | $0.20 |
| Results tracked | 33 | 22 |
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Category by category
Coding Gemini 2.5 Flash-Lite leads
Gemini 2.5 Flash-Lite: 38.5 (#173), Llama 3.2 1B: 21.1 (#338)
| Benchmark | Gemini 2.5 Flash-Lite | Llama 3.2 1B |
|---|---|---|
| LMArena Coding | 1373 | 1070 |
| WeirdML | 35.2% | — |
| BigCodeBench Instruct | — | 8.2% |
| BigCodeBench Complete | — | 11.3% |
| ALE-Bench | 325.9 | — |
Agentic & Tool Use Gemini 2.5 Flash-Lite leads
Gemini 2.5 Flash-Lite: 28.0 (#96), Llama 3.2 1B: 14.6 (#150)
| Benchmark | Gemini 2.5 Flash-Lite | Llama 3.2 1B |
|---|---|---|
| Berkeley Function Calling Leaderboard | 36.9% | 10.8% |
| BALROG | — | 6.6% |
Reasoning Gemini 2.5 Flash-Lite leads
Gemini 2.5 Flash-Lite: 22.2 (#205), Llama 3.2 1B: 16.2 (#308)
| Benchmark | Gemini 2.5 Flash-Lite | Llama 3.2 1B |
|---|---|---|
| LMArena Hard Prompts | 1377 | 1044 |
| Epoch Capabilities Index | 133.94 | 101.99 |
| Kagi LLM Benchmark | 40.5% | — |
| Chess Puzzles | — | 0% |
| DTBench | 62.8% | — |
| LMCA | 18.1% | — |
Math Gemini 2.5 Flash-Lite leads
Gemini 2.5 Flash-Lite: 38.0 (#144), Llama 3.2 1B: 10.4 (#313)
| Benchmark | Gemini 2.5 Flash-Lite | Llama 3.2 1B |
|---|---|---|
| LMArena Math | 1373 | 1086 |
| OTIS Mock AIME 2024-2025 | — | 0.6% |
| Omni-MATH | 48% | — |
Knowledge Gemini 2.5 Flash-Lite leads
Gemini 2.5 Flash-Lite: 32.5 (#210), Llama 3.2 1B: 7.2 (#312)
| Benchmark | Gemini 2.5 Flash-Lite | Llama 3.2 1B |
|---|---|---|
| LMArena Expert | 1373 | 1007 |
| GPQA Diamond | — | 23.9% |
| MMLU-Pro | 53.7% | — |
| Vectara Hallucination Rate | 3.3% | — |
| GPQA (HELM) | 30.9% | — |
Multimodal Not comparable
Gemini 2.5 Flash-Lite: 29.1 (#114), Llama 3.2 1B: —
| Benchmark | Gemini 2.5 Flash-Lite | Llama 3.2 1B |
|---|---|---|
| LMArena Vision | 1198 | — |
| VPCT | 30% | — |
Multilingual Gemini 2.5 Flash-Lite leads
Gemini 2.5 Flash-Lite: 49.3 (#134), Llama 3.2 1B: 23.8 (#292)
| Benchmark | Gemini 2.5 Flash-Lite | Llama 3.2 1B |
|---|---|---|
| LMArena Non-English | 1369 | 973 |
| LMArena Chinese | 1404 | 959 |
| LMArena German | 1389 | 1014 |
| LMArena Russian | 1373 | 941 |
| LMArena French | 1388 | — |
| LMArena Japanese | 1359 | — |
| LMArena Korean | 1360 | — |
| LMArena Spanish | 1396 | — |
Instruction Following Gemini 2.5 Flash-Lite leads
Gemini 2.5 Flash-Lite: 70.0 (#168), Llama 3.2 1B: 52.4 (#290)
| Benchmark | Gemini 2.5 Flash-Lite | Llama 3.2 1B |
|---|---|---|
| LMArena Instruction Following | 1367 | 1031 |
| IFEval | 81% | — |
Long Context Gemini 2.5 Flash-Lite leads
Gemini 2.5 Flash-Lite: 33.3 (#262), Llama 3.2 1B: 31.9 (#274)
| Benchmark | Gemini 2.5 Flash-Lite | Llama 3.2 1B |
|---|---|---|
| LMArena Longer Query | 1373 | 1050 |
| Fiction.LiveBench | 47.2% | — |
Writing & Preference Gemini 2.5 Flash-Lite leads
Gemini 2.5 Flash-Lite: 56.8 (#135), Llama 3.2 1B: 21.3 (#310)
| Benchmark | Gemini 2.5 Flash-Lite | Llama 3.2 1B |
|---|---|---|
| LMArena Text | 1379 | 1055 |
| LMArena Creative Writing | 1367 | 1033 |
| LMArena Multi-Turn | 1366 | 1030 |
| EQ-Bench Creative Writing | — | 200 |
| WildBench | 81.8% | — |
Frequently asked questions
Is Gemini 2.5 Flash-Lite better than Llama 3.2 1B?
Gemini 2.5 Flash-Lite is the stronger model overall, scoring 37.0 to 20.1 on the Noometry Index. Llama 3.2 1B costs 2.5× less per token, which makes it the better buy when Gemini 2.5 Flash-Lite's lead doesn't matter for your workload.
Which is cheaper, Gemini 2.5 Flash-Lite 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; Gemini 2.5 Flash-Lite lists at $0.10 and $0.40.
Is Gemini 2.5 Flash-Lite or Llama 3.2 1B better for coding?
Gemini 2.5 Flash-Lite scores higher on coding benchmarks: 38.5 versus 21.1 in the Noometry coding category.
Which has the bigger context window?
Gemini 2.5 Flash-Lite does, with 1.05M tokens against 60K.
How many benchmarks do Gemini 2.5 Flash-Lite and Llama 3.2 1B share?
15 benchmarks have published results for both models. Gemini 2.5 Flash-Lite has 33 scored results on Noometry and Llama 3.2 1B has 22.