Model comparison
Gemini 2.5 Flash-Lite vs Llama 3.1-8B
Gemini 2.5 Flash-Lite is the stronger model overall, scoring 36.4 to 22.8 on the Noometry Index. Llama 3.1-8B costs 3.0× 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 . 27 shared benchmarks.
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MetaSummary
- They share 27 benchmarks with published results for both. Gemini 2.5 Flash-Lite scores higher in 8 categories and Llama 3.1-8B in 1 category; 9 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where Gemini 2.5 Flash-Lite leads 57.0 to 29.9.
- The biggest single-benchmark swing is Omni-MATH: 48% for Gemini 2.5 Flash-Lite and 13.7% for Llama 3.1-8B.
- Llama 3.1-8B is cheaper at $0.05 / $0.08 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 128K.
- Llama 3.1-8B has downloadable open weights; the other is API-only.
Side by side
| Gemini 2.5 Flash-Lite | Llama 3.1-8B | |
|---|---|---|
| Provider | Meta | |
| Noometry Index | 36.4 | 22.8 |
| Released | 2025-06-17 | 2024-07-23 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 128K |
| Max output | 66K | 4K |
| Input $ / M tokens | $0.10 | $0.05 |
| Output $ / M tokens | $0.40 | $0.08 |
| Results tracked | 33 | 43 |
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Category by category
Coding Gemini 2.5 Flash-Lite leads
Gemini 2.5 Flash-Lite: 38.0 (#167), Llama 3.1-8B: 19.9 (#335)
| Benchmark | Gemini 2.5 Flash-Lite | Llama 3.1-8B |
|---|---|---|
| WeirdML | 35.2% | 1.7% |
| LMArena Coding | 1373 | 1195 |
| SciCode | — | 13.2% |
| BigCodeBench Instruct | — | 32.8% |
| BigCodeBench Complete | — | 40.5% |
| ALE-Bench | 325.9 | — |
| HumanEval+ | — | 62.8% |
| MBPP+ | — | 55.6% |
Agentic & Tool Use Gemini 2.5 Flash-Lite leads
Gemini 2.5 Flash-Lite: 27.8 (#89), Llama 3.1-8B: 22.3 (#123)
| Benchmark | Gemini 2.5 Flash-Lite | Llama 3.1-8B |
|---|---|---|
| Berkeley Function Calling Leaderboard | 36.9% | 25.8% |
| BALROG | — | 15.1% |
Reasoning Gemini 2.5 Flash-Lite leads
Gemini 2.5 Flash-Lite: 21.5 (#196), Llama 3.1-8B: 14.5 (#316)
| Benchmark | Gemini 2.5 Flash-Lite | Llama 3.1-8B |
|---|---|---|
| LMArena Hard Prompts | 1377 | 1175 |
| DTBench | 62.8% | 50.9% |
| LMCA | 18.1% | 5.4% |
| Epoch Capabilities Index | 133.93 | 116.48 |
| Kagi LLM Benchmark | 40.5% | — |
| CritPt | — | 0% |
| Chess Puzzles | — | 0% |
| PIQA | — | 81.2% |
Math Gemini 2.5 Flash-Lite leads
Gemini 2.5 Flash-Lite: 35.5 (#144), Llama 3.1-8B: 9.5 (#311)
| Benchmark | Gemini 2.5 Flash-Lite | Llama 3.1-8B |
|---|---|---|
| Omni-MATH | 48% | 13.7% |
| LMArena Math | 1373 | 1179 |
| OTIS Mock AIME 2024-2025 | — | 1.7% |
| MATH Level 5 | — | 22.9% |
| GSM8K | — | 82.4% |
Knowledge Gemini 2.5 Flash-Lite leads
Gemini 2.5 Flash-Lite: 32.1 (#206), Llama 3.1-8B: 8.0 (#302)
| Benchmark | Gemini 2.5 Flash-Lite | Llama 3.1-8B |
|---|---|---|
| MMLU-Pro | 53.7% | 40.6% |
| GPQA (HELM) | 30.9% | 24.7% |
| LMArena Expert | 1373 | 1144 |
| GPQA Diamond | — | 27% |
| Vectara Hallucination Rate | 3.3% | — |
| BoolQ | — | 82.8% |
| MMLU | — | 56.1% |
Multimodal Not comparable
Gemini 2.5 Flash-Lite: 28.6 (#109), Llama 3.1-8B: —
| Benchmark | Gemini 2.5 Flash-Lite | Llama 3.1-8B |
|---|---|---|
| LMArena Vision | 1198 | — |
| VPCT | 30% | — |
Multilingual Gemini 2.5 Flash-Lite leads
Gemini 2.5 Flash-Lite: 49.5 (#129), Llama 3.1-8B: 34.2 (#244)
| Benchmark | Gemini 2.5 Flash-Lite | Llama 3.1-8B |
|---|---|---|
| LMArena Non-English | 1369 | 1148 |
| LMArena Chinese | 1404 | 1151 |
| LMArena French | 1388 | 1176 |
| LMArena German | 1390 | 1144 |
| LMArena Japanese | 1359 | 1061 |
| LMArena Korean | 1358 | 1053 |
| LMArena Russian | 1373 | 1158 |
| LMArena Spanish | 1396 | 1169 |
Instruction Following Gemini 2.5 Flash-Lite leads
Gemini 2.5 Flash-Lite: 70.2 (#163), Llama 3.1-8B: 59.1 (#253)
| Benchmark | Gemini 2.5 Flash-Lite | Llama 3.1-8B |
|---|---|---|
| IFEval | 81% | 74.3% |
| LMArena Instruction Following | 1366 | 1159 |
Long Context Llama 3.1-8B leads
Gemini 2.5 Flash-Lite: 33.3 (#257), Llama 3.1-8B: 35.8 (#233)
| Benchmark | Gemini 2.5 Flash-Lite | Llama 3.1-8B |
|---|---|---|
| LMArena Longer Query | 1373 | 1182 |
| Fiction.LiveBench | 47.2% | — |
Writing & Preference Gemini 2.5 Flash-Lite leads
Gemini 2.5 Flash-Lite: 57.0 (#130), Llama 3.1-8B: 29.9 (#285)
| Benchmark | Gemini 2.5 Flash-Lite | Llama 3.1-8B |
|---|---|---|
| LMArena Text | 1379 | 1187 |
| LMArena Creative Writing | 1366 | 1154 |
| WildBench | 81.8% | 68.7% |
| LMArena Multi-Turn | 1367 | 1172 |
| EQ-Bench Creative Writing | — | 713 |
Frequently asked questions
Is Gemini 2.5 Flash-Lite better than Llama 3.1-8B?
Gemini 2.5 Flash-Lite is the stronger model overall, scoring 36.4 to 22.8 on the Noometry Index. Llama 3.1-8B costs 3.0× 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 3.1-8B?
Llama 3.1-8B is cheaper. It lists at $0.05 per million input tokens and $0.08 per million output tokens; Gemini 2.5 Flash-Lite lists at $0.10 and $0.40.
Is Gemini 2.5 Flash-Lite or Llama 3.1-8B better for coding?
Gemini 2.5 Flash-Lite scores higher on coding benchmarks: 38.0 versus 19.9 in the Noometry coding category.
Which has the bigger context window?
Gemini 2.5 Flash-Lite does, with 1.05M tokens against 128K.
How many benchmarks do Gemini 2.5 Flash-Lite and Llama 3.1-8B share?
27 benchmarks have published results for both models. Gemini 2.5 Flash-Lite has 33 scored results on Noometry and Llama 3.1-8B has 43.