Model comparison
Gemini 3.8 Flash vs Llama 3.2 1B
Gemini 3.8 Flash is the stronger model overall, scoring 61.8 to 20.1 on the Noometry Index. Llama 3.2 1B costs 21× less per token, which makes it the better buy when Gemini 3.8 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 3.8 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 knowledge, where Gemini 3.8 Flash leads 74.8 to 7.2.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 98.9% for Gemini 3.8 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 $0.75 / $3.75 for Gemini 3.8 Flash.
- Gemini 3.8 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 3.8 Flash | Llama 3.2 1B | |
|---|---|---|
| Provider | Meta | |
| Noometry Index | 61.8 | 20.1 |
| Released | 2026-09-02 | 2024-09-24 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 60K |
| Max output | 66K | 54K |
| Input $ / M tokens | $0.75 | $0.027 |
| Output $ / M tokens | $3.75 | $0.20 |
| Results tracked | 50 | 22 |
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Category by category
Coding Gemini 3.8 Flash leads
Gemini 3.8 Flash: 59.2 (#15), Llama 3.2 1B: 21.1 (#338)
| Benchmark | Gemini 3.8 Flash | Llama 3.2 1B |
|---|---|---|
| LMArena Coding | 1510 | 1070 |
| DeepSWE | 73.8% | — |
| FrontierCode | 41.2% | — |
| CursorBench | 39.6% | — |
| LMArena WebDev | 1584 | — |
| FrontierSWE | 19.6% | — |
| SciCode | 56.6% | — |
| WeirdML | 84.8% | — |
| BigCodeBench Instruct | — | 8.2% |
| BigCodeBench Complete | — | 11.3% |
| ALE-Bench | 1,270 | — |
Agentic & Tool Use Gemini 3.8 Flash leads
Gemini 3.8 Flash: 41.8 (#21), Llama 3.2 1B: 14.6 (#150)
| Benchmark | Gemini 3.8 Flash | Llama 3.2 1B |
|---|---|---|
| APEX-Agents | 64.3% | — |
| Berkeley Function Calling Leaderboard | — | 10.8% |
| Remote Labor Index | 5.8% | — |
| BALROG | — | 6.6% |
| GDP.pdf | 23.4% | — |
| Vending-Bench 2 | 5,094 | — |
Reasoning Gemini 3.8 Flash leads
Gemini 3.8 Flash: 76.9 (#5), Llama 3.2 1B: 16.2 (#308)
| Benchmark | Gemini 3.8 Flash | Llama 3.2 1B |
|---|---|---|
| Chess Puzzles | 61% | 0% |
| LMArena Hard Prompts | 1508 | 1044 |
| Epoch Capabilities Index | 156.71 | 101.99 |
| ARC-AGI-2 | 89.2% | — |
| NYT Connections (extended) | 97.4% | — |
| ARC-AGI-1 | 98.5% | — |
| CritPt | 18.3% | — |
| Mystery Game Puzzles | 47% | — |
| DTBench | 95.7% | — |
| LMCA | 52.9% | — |
| Surface Evolver Bench | 76.9% | — |
Math Gemini 3.8 Flash leads
Gemini 3.8 Flash: 65.3 (#28), Llama 3.2 1B: 10.4 (#313)
| Benchmark | Gemini 3.8 Flash | Llama 3.2 1B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 98.9% | 0.6% |
| LMArena Math | 1528 | 1086 |
| FrontierMath (Tiers 1-3) | 68.4% | — |
| FrontierMath Tier 4 | 22% | — |
| ProofBench | 48% | — |
Knowledge Gemini 3.8 Flash leads
Gemini 3.8 Flash: 74.8 (#2), Llama 3.2 1B: 7.2 (#312)
| Benchmark | Gemini 3.8 Flash | Llama 3.2 1B |
|---|---|---|
| GPQA Diamond | 95.4% | 23.9% |
| LMArena Expert | 1524 | 1007 |
| Humanity's Last Exam | 44.5% | — |
| SimpleQA Verified | 69.7% | — |
Multimodal Not comparable
Gemini 3.8 Flash: 40.7 (#45), Llama 3.2 1B: —
| Benchmark | Gemini 3.8 Flash | Llama 3.2 1B |
|---|---|---|
| LMArena Vision | 1314 | — |
| Blueprint-Bench 2 | 38.6% | — |
| Furniture Assembly | 31.7% | — |
Multilingual Gemini 3.8 Flash leads
Gemini 3.8 Flash: 58.0 (#5), Llama 3.2 1B: 23.8 (#292)
| Benchmark | Gemini 3.8 Flash | Llama 3.2 1B |
|---|---|---|
| LMArena Non-English | 1491 | 973 |
| LMArena Chinese | 1554 | 959 |
| LMArena German | 1493 | 1014 |
| LMArena Russian | 1515 | 941 |
| LMArena French | 1498 | — |
| LMArena Japanese | 1502 | — |
| LMArena Korean | 1459 | — |
| LMArena Spanish | 1485 | — |
Instruction Following Gemini 3.8 Flash leads
Gemini 3.8 Flash: 78.0 (#13), Llama 3.2 1B: 52.4 (#290)
| Benchmark | Gemini 3.8 Flash | Llama 3.2 1B |
|---|---|---|
| LMArena Instruction Following | 1490 | 1031 |
Long Context Gemini 3.8 Flash leads
Gemini 3.8 Flash: 46.3 (#24), Llama 3.2 1B: 31.9 (#274)
| Benchmark | Gemini 3.8 Flash | Llama 3.2 1B |
|---|---|---|
| LMArena Longer Query | 1508 | 1050 |
Writing & Preference Gemini 3.8 Flash leads
Gemini 3.8 Flash: 72.2 (#15), Llama 3.2 1B: 21.3 (#310)
| Benchmark | Gemini 3.8 Flash | Llama 3.2 1B |
|---|---|---|
| LMArena Text | 1499 | 1055 |
| LMArena Creative Writing | 1492 | 1033 |
| EQ-Bench Creative Writing | 1748 | 200 |
| LMArena Multi-Turn | 1501 | 1030 |
Frequently asked questions
Is Gemini 3.8 Flash better than Llama 3.2 1B?
Gemini 3.8 Flash is the stronger model overall, scoring 61.8 to 20.1 on the Noometry Index. Llama 3.2 1B costs 21× less per token, which makes it the better buy when Gemini 3.8 Flash's lead doesn't matter for your workload.
Which is cheaper, Gemini 3.8 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 3.8 Flash lists at $0.75 and $3.75.
Is Gemini 3.8 Flash or Llama 3.2 1B better for coding?
Gemini 3.8 Flash scores higher on coding benchmarks: 59.2 versus 21.1 in the Noometry coding category.
Which has the bigger context window?
Gemini 3.8 Flash does, with 1.05M tokens against 60K.
How many benchmarks do Gemini 3.8 Flash and Llama 3.2 1B share?
18 benchmarks have published results for both models. Gemini 3.8 Flash has 50 scored results on Noometry and Llama 3.2 1B has 22.