Model comparison
Gemini 2.5 Flash-Lite vs Llama-3.3-70B-Instruct
Gemini 2.5 Flash-Lite is the stronger model overall, scoring 36.4 to 30.3 on the Noometry Index.
Last verified . 24 shared benchmarks.
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MetaSummary
- They share 24 benchmarks with published results for both. Gemini 2.5 Flash-Lite scores higher in 8 categories and Llama-3.3-70B-Instruct in 1 category; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where Gemini 2.5 Flash-Lite leads 35.5 to 14.3.
- The biggest single-benchmark swing is WeirdML: 35.2% for Gemini 2.5 Flash-Lite and 14.4% for Llama-3.3-70B-Instruct.
- Llama-3.3-70B-Instruct is cheaper at $0.10 / $0.32 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-3.3-70B-Instruct has downloadable open weights; the other is API-only.
Side by side
| Gemini 2.5 Flash-Lite | Llama-3.3-70B-Instruct | |
|---|---|---|
| Provider | Meta | |
| Noometry Index | 36.4 | 30.3 |
| Released | 2025-06-17 | 2024-12-06 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 128K |
| Max output | 66K | 4K |
| Input $ / M tokens | $0.10 | $0.10 |
| Output $ / M tokens | $0.40 | $0.32 |
| 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-3.3-70B-Instruct: 30.6 (#284)
| Benchmark | Gemini 2.5 Flash-Lite | Llama-3.3-70B-Instruct |
|---|---|---|
| WeirdML | 35.2% | 14.4% |
| LMArena Coding | 1373 | 1268 |
| SciCode | — | 26% |
| BigCodeBench Instruct | — | 46.9% |
| LiveBench Coding | — | 36.6% |
| BigCodeBench Complete | — | 57.5% |
| ALE-Bench | 325.9 | — |
Agentic & Tool Use Gemini 2.5 Flash-Lite leads
Gemini 2.5 Flash-Lite: 27.8 (#89), Llama-3.3-70B-Instruct: 25.6 (#100)
| Benchmark | Gemini 2.5 Flash-Lite | Llama-3.3-70B-Instruct |
|---|---|---|
| Berkeley Function Calling Leaderboard | 36.9% | 31.9% |
| BALROG | — | 23% |
Reasoning Gemini 2.5 Flash-Lite leads
Gemini 2.5 Flash-Lite: 21.5 (#196), Llama-3.3-70B-Instruct: 13.7 (#325)
| Benchmark | Gemini 2.5 Flash-Lite | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Hard Prompts | 1377 | 1257 |
| DTBench | 62.8% | 59.5% |
| LMCA | 18.1% | 17.5% |
| Epoch Capabilities Index | 133.93 | 127.32 |
| SimpleBench | — | 19.9% |
| Kagi LLM Benchmark | 40.5% | — |
| CritPt | — | 0% |
| LiveBench Reasoning | — | 50.8% |
| LiveBench Data Analysis | — | 49.5% |
| ForecastBench | — | 58.6 |
| LiveBench | — | 50.2% |
Math Gemini 2.5 Flash-Lite leads
Gemini 2.5 Flash-Lite: 35.5 (#144), Llama-3.3-70B-Instruct: 14.3 (#292)
| Benchmark | Gemini 2.5 Flash-Lite | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Math | 1373 | 1268 |
| OTIS Mock AIME 2024-2025 | — | 5.1% |
| Omni-MATH | 48% | — |
| LiveBench Math | — | 42.2% |
| MATH Level 5 | — | 41.6% |
Knowledge Gemini 2.5 Flash-Lite leads
Gemini 2.5 Flash-Lite: 32.1 (#206), Llama-3.3-70B-Instruct: 30.5 (#220)
| Benchmark | Gemini 2.5 Flash-Lite | Llama-3.3-70B-Instruct |
|---|---|---|
| Vectara Hallucination Rate | 3.3% | 4.1% |
| LMArena Expert | 1373 | 1225 |
| GPQA Diamond | — | 47.4% |
| MMLU-Pro | 53.7% | — |
| Confabulations | — | 22.8% |
| GPQA (HELM) | 30.9% | — |
| MMLU | — | 86.3% |
Multimodal Not comparable
Gemini 2.5 Flash-Lite: 28.6 (#109), Llama-3.3-70B-Instruct: —
| Benchmark | Gemini 2.5 Flash-Lite | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Vision | 1198 | — |
| VPCT | 30% | — |
Multilingual Gemini 2.5 Flash-Lite leads
Gemini 2.5 Flash-Lite: 49.5 (#129), Llama-3.3-70B-Instruct: 40.1 (#215)
| Benchmark | Gemini 2.5 Flash-Lite | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Non-English | 1369 | 1236 |
| LMArena Chinese | 1404 | 1217 |
| LMArena French | 1388 | 1280 |
| LMArena German | 1390 | 1251 |
| LMArena Japanese | 1359 | 1149 |
| LMArena Korean | 1358 | 1142 |
| LMArena Russian | 1373 | 1252 |
| LMArena Spanish | 1396 | 1269 |
Instruction Following Llama-3.3-70B-Instruct leads
Gemini 2.5 Flash-Lite: 70.2 (#163), Llama-3.3-70B-Instruct: 71.3 (#152)
| Benchmark | Gemini 2.5 Flash-Lite | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Instruction Following | 1366 | 1242 |
| LiveBench Instruction Following | — | 82.7% |
| IFEval | 81% | — |
Long Context Gemini 2.5 Flash-Lite leads
Gemini 2.5 Flash-Lite: 33.3 (#257), Llama-3.3-70B-Instruct: 26.4 (#290)
| Benchmark | Gemini 2.5 Flash-Lite | Llama-3.3-70B-Instruct |
|---|---|---|
| Fiction.LiveBench | 47.2% | 33.3% |
| LMArena Longer Query | 1373 | 1256 |
Writing & Preference Gemini 2.5 Flash-Lite leads
Gemini 2.5 Flash-Lite: 57.0 (#130), Llama-3.3-70B-Instruct: 47.8 (#203)
| Benchmark | Gemini 2.5 Flash-Lite | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Text | 1379 | 1274 |
| LMArena Creative Writing | 1366 | 1250 |
| LMArena Multi-Turn | 1367 | 1279 |
| WildBench | 81.8% | — |
| LiveBench Language | — | 39.2% |
Frequently asked questions
Is Gemini 2.5 Flash-Lite better than Llama-3.3-70B-Instruct?
Gemini 2.5 Flash-Lite is the stronger model overall, scoring 36.4 to 30.3 on the Noometry Index.
Which is cheaper, Gemini 2.5 Flash-Lite or Llama-3.3-70B-Instruct?
Llama-3.3-70B-Instruct is cheaper. It lists at $0.10 per million input tokens and $0.32 per million output tokens; Gemini 2.5 Flash-Lite lists at $0.10 and $0.40.
Is Gemini 2.5 Flash-Lite or Llama-3.3-70B-Instruct better for coding?
Gemini 2.5 Flash-Lite scores higher on coding benchmarks: 38.0 versus 30.6 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-3.3-70B-Instruct share?
24 benchmarks have published results for both models. Gemini 2.5 Flash-Lite has 33 scored results on Noometry and Llama-3.3-70B-Instruct has 43.