Model comparison
Gemini 2.5 Flash vs Llama 3.2 1B
Gemini 2.5 Flash is the stronger model overall, scoring 39.3 to 20.1 on the Noometry Index. Llama 3.2 1B costs 12× less per token, which makes it the better buy when Gemini 2.5 Flash's lead doesn't matter for your workload.
Last verified . 18 shared benchmarks.
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MetaSummary
- They share 18 benchmarks with published results for both. Gemini 2.5 Flash 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 leads 53.8 to 21.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 73.1% for Gemini 2.5 Flash 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 $0.30 / $2.50 for Gemini 2.5 Flash.
- Gemini 2.5 Flash 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 | Llama 3.2 1B | |
|---|---|---|
| Provider | Meta | |
| Noometry Index | 39.3 | 20.1 |
| Released | 2025-04-17 | 2024-09-24 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 60K |
| Max output | 66K | 54K |
| Input $ / M tokens | $0.30 | $0.027 |
| Output $ / M tokens | $2.50 | $0.20 |
| Results tracked | 54 | 22 |
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Category by category
Coding Gemini 2.5 Flash leads
Gemini 2.5 Flash: 35.8 (#220), Llama 3.2 1B: 21.1 (#338)
| Benchmark | Gemini 2.5 Flash | Llama 3.2 1B |
|---|---|---|
| LMArena Coding | 1424 | 1070 |
| SWE-bench Verified (bash only) | 28.7% | — |
| Aider Polyglot | 55.1% | — |
| WeirdML | 41.9% | — |
| BigCodeBench Instruct | — | 8.2% |
| BigCodeBench Complete | — | 11.3% |
| ALE-Bench | 661.88 | — |
Agentic & Tool Use Gemini 2.5 Flash leads
Gemini 2.5 Flash: 30.8 (#74), Llama 3.2 1B: 14.6 (#150)
| Benchmark | Gemini 2.5 Flash | Llama 3.2 1B |
|---|---|---|
| Berkeley Function Calling Leaderboard | 56.2% | 10.8% |
| BALROG | 33.5% | 6.6% |
| Terminal-Bench | 17.1% | — |
| TheAgentCompany | 41.1% | — |
| Vending-Bench 2 | 548.84 | — |
Reasoning Gemini 2.5 Flash leads
Gemini 2.5 Flash: 18.1 (#286), Llama 3.2 1B: 16.2 (#308)
| Benchmark | Gemini 2.5 Flash | Llama 3.2 1B |
|---|---|---|
| LMArena Hard Prompts | 1422 | 1044 |
| Epoch Capabilities Index | 143.03 | 101.99 |
| ARC-AGI-2 | 2.5% | — |
| SimpleBench | 41.2% | — |
| Kagi LLM Benchmark | 56.8% | — |
| ARC-AGI-1 | 33.3% | — |
| CritPt | 1.1% | — |
| Chess Puzzles | — | 0% |
| EnigmaEval | 2.7% | — |
| DTBench | 76.5% | — |
| LMCA | 27.5% | — |
| ForecastBench | 60.6 | — |
Math Gemini 2.5 Flash leads
Gemini 2.5 Flash: 39.9 (#98), Llama 3.2 1B: 10.4 (#313)
| Benchmark | Gemini 2.5 Flash | Llama 3.2 1B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 73.1% | 0.6% |
| LMArena Math | 1415 | 1086 |
| Omni-MATH | 38.5% | — |
| FrontierMath (Feb 2025 set) | 4.8% | — |
| FrontierMath Tier 4 (v1) | 4.2% | — |
Knowledge Gemini 2.5 Flash leads
Gemini 2.5 Flash: 36.4 (#168), Llama 3.2 1B: 7.2 (#312)
| Benchmark | Gemini 2.5 Flash | Llama 3.2 1B |
|---|---|---|
| LMArena Expert | 1426 | 1007 |
| GPQA Diamond | — | 23.9% |
| Humanity's Last Exam | 12.1% | — |
| MMLU-Pro | 63.9% | — |
| Confabulations | 16.8% | — |
| Vectara Hallucination Rate | 7.8% | — |
| GPQA (HELM) | 39% | — |
Multimodal Not comparable
Gemini 2.5 Flash: 41.8 (#32), Llama 3.2 1B: —
| Benchmark | Gemini 2.5 Flash | Llama 3.2 1B |
|---|---|---|
| LMArena Vision | 1253 | — |
| GeoBench | 76% | — |
| VPCT | 46.2% | — |
| SpatialViz-Bench | 36.9% | — |
Multilingual Gemini 2.5 Flash leads
Gemini 2.5 Flash: 52.3 (#88), Llama 3.2 1B: 23.8 (#292)
| Benchmark | Gemini 2.5 Flash | Llama 3.2 1B |
|---|---|---|
| LMArena Non-English | 1409 | 973 |
| LMArena Chinese | 1450 | 959 |
| LMArena German | 1418 | 1014 |
| LMArena Russian | 1415 | 941 |
| LMArena French | 1433 | — |
| LMArena Japanese | 1405 | — |
| LMArena Korean | 1385 | — |
| LMArena Spanish | 1421 | — |
Instruction Following Gemini 2.5 Flash leads
Gemini 2.5 Flash: 75.7 (#54), Llama 3.2 1B: 52.4 (#290)
| Benchmark | Gemini 2.5 Flash | Llama 3.2 1B |
|---|---|---|
| LMArena Instruction Following | 1405 | 1031 |
| IFEval | 89.8% | — |
Long Context Gemini 2.5 Flash leads
Gemini 2.5 Flash: 47.5 (#17), Llama 3.2 1B: 31.9 (#274)
| Benchmark | Gemini 2.5 Flash | Llama 3.2 1B |
|---|---|---|
| LMArena Longer Query | 1419 | 1050 |
| Fiction.LiveBench | 77.8% | — |
Writing & Preference Gemini 2.5 Flash leads
Gemini 2.5 Flash: 53.8 (#157), Llama 3.2 1B: 21.3 (#310)
| Benchmark | Gemini 2.5 Flash | Llama 3.2 1B |
|---|---|---|
| LMArena Text | 1417 | 1055 |
| LMArena Creative Writing | 1400 | 1033 |
| EQ-Bench Creative Writing | 1137 | 200 |
| LMArena Multi-Turn | 1408 | 1030 |
| Short-Story Creative Writing | 76.5% | — |
| WildBench | 81.7% | — |
Frequently asked questions
Is Gemini 2.5 Flash better than Llama 3.2 1B?
Gemini 2.5 Flash is the stronger model overall, scoring 39.3 to 20.1 on the Noometry Index. Llama 3.2 1B costs 12× less per token, which makes it the better buy when Gemini 2.5 Flash's lead doesn't matter for your workload.
Which is cheaper, Gemini 2.5 Flash 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 lists at $0.30 and $2.50.
Is Gemini 2.5 Flash or Llama 3.2 1B better for coding?
Gemini 2.5 Flash scores higher on coding benchmarks: 35.8 versus 21.1 in the Noometry coding category.
Which has the bigger context window?
Gemini 2.5 Flash does, with 1.05M tokens against 60K.
How many benchmarks do Gemini 2.5 Flash and Llama 3.2 1B share?
18 benchmarks have published results for both models. Gemini 2.5 Flash has 54 scored results on Noometry and Llama 3.2 1B has 22.