Model comparison
Gemini 1.5 Flash (May 2024) vs Llama-3.3-70B-Instruct
Gemini 1.5 Flash (May 2024) is the stronger model overall, scoring 33.2 to 30.6 on the Noometry Index.
Last verified . 28 shared benchmarks.
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MetaSummary
- They share 28 benchmarks with published results for both. Gemini 1.5 Flash (May 2024) scores higher in 7 categories and Llama-3.3-70B-Instruct in 2 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in long context, where Gemini 1.5 Flash (May 2024) leads 39.0 to 26.4.
- The biggest single-benchmark swing is MATH Level 5: 61.9% for Gemini 1.5 Flash (May 2024) and 41.6% for Llama-3.3-70B-Instruct.
- Llama-3.3-70B-Instruct has downloadable open weights; the other is API-only.
Side by side
| Gemini 1.5 Flash (May 2024) | Llama-3.3-70B-Instruct | |
|---|---|---|
| Provider | Meta | |
| Noometry Index | 33.2 | 30.6 |
| Released | 2024-05-14 | 2024-12-06 |
| Weights | Proprietary | Open |
| Context window | — | 128K |
| Max output | — | 4K |
| Input $ / M tokens | — | $0.10 |
| Output $ / M tokens | — | $0.32 |
| Results tracked | 42 | 43 |
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Category by category
Coding Gemini 1.5 Flash (May 2024) leads
Gemini 1.5 Flash (May 2024): 34.4 (#236), Llama-3.3-70B-Instruct: 31.0 (#290)
| Benchmark | Gemini 1.5 Flash (May 2024) | Llama-3.3-70B-Instruct |
|---|---|---|
| WeirdML | 24.9% | 14.4% |
| BigCodeBench Instruct | 43.5% | 46.9% |
| LMArena Coding | 1261 | 1268 |
| BigCodeBench Complete | 55.1% | 57.5% |
| SciCode | — | 26% |
| LiveBench Coding | — | 36.6% |
| HumanEval+ | 75.6% | — |
| MBPP+ | 67.5% | — |
Agentic & Tool Use Too close to call
Gemini 1.5 Flash (May 2024): 26.6 (#102), Llama-3.3-70B-Instruct: 25.8 (#105)
| Benchmark | Gemini 1.5 Flash (May 2024) | Llama-3.3-70B-Instruct |
|---|---|---|
| BALROG | 14.6% | 23% |
| Berkeley Function Calling Leaderboard | — | 31.9% |
Reasoning Gemini 1.5 Flash (May 2024) leads
Gemini 1.5 Flash (May 2024): 21.7 (#215), Llama-3.3-70B-Instruct: 14.1 (#327)
| Benchmark | Gemini 1.5 Flash (May 2024) | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Hard Prompts | 1257 | 1257 |
| DTBench | 53.8% | 59.5% |
| Epoch Capabilities Index | 129.36 | 127.33 |
| ForecastBench | 53.9 | 58.6 |
| SimpleBench | — | 19.9% |
| CritPt | — | 0% |
| LiveBench Reasoning | — | 50.8% |
| LiveBench Data Analysis | — | 49.5% |
| LMCA | — | 17.5% |
| LiveBench | — | 50.2% |
| PIQA | 87.5% | — |
Math Gemini 1.5 Flash (May 2024) leads
Gemini 1.5 Flash (May 2024): 22.1 (#281), Llama-3.3-70B-Instruct: 15.3 (#298)
| Benchmark | Gemini 1.5 Flash (May 2024) | Llama-3.3-70B-Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 16.3% | 5.1% |
| LMArena Math | 1269 | 1267 |
| MATH Level 5 | 61.9% | 41.6% |
| Omni-MATH | 30.4% | — |
| LiveBench Math | — | 42.2% |
| FrontierMath (Feb 2025 set) | 0% | — |
| GSM8K | 82.4% | — |
Knowledge Llama-3.3-70B-Instruct leads
Gemini 1.5 Flash (May 2024): 26.2 (#260), Llama-3.3-70B-Instruct: 30.6 (#226)
| Benchmark | Gemini 1.5 Flash (May 2024) | Llama-3.3-70B-Instruct |
|---|---|---|
| GPQA Diamond | 47.3% | 47.4% |
| LMArena Expert | 1233 | 1225 |
| MMLU | 77.9% | 86.3% |
| MMLU-Pro | 67.8% | — |
| Confabulations | — | 22.8% |
| Vectara Hallucination Rate | — | 4.1% |
| GPQA (HELM) | 43.7% | — |
| BoolQ | 85.8% | — |
Multimodal Not comparable
Gemini 1.5 Flash (May 2024): 36.0 (#81), Llama-3.3-70B-Instruct: —
| Benchmark | Gemini 1.5 Flash (May 2024) | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Vision | 1141 | — |
| Video-MME | 70.3% | — |
| GeoBench | 76% | — |
Multilingual Gemini 1.5 Flash (May 2024) leads
Gemini 1.5 Flash (May 2024): 42.9 (#189), Llama-3.3-70B-Instruct: 39.9 (#220)
| Benchmark | Gemini 1.5 Flash (May 2024) | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Non-English | 1278 | 1236 |
| LMArena Chinese | 1295 | 1217 |
| LMArena French | 1258 | 1281 |
| LMArena German | 1262 | 1251 |
| LMArena Japanese | 1252 | 1150 |
| LMArena Korean | 1221 | 1143 |
| LMArena Russian | 1288 | 1252 |
| LMArena Spanish | 1243 | 1270 |
Instruction Following Llama-3.3-70B-Instruct leads
Gemini 1.5 Flash (May 2024): 66.8 (#205), Llama-3.3-70B-Instruct: 71.1 (#157)
| Benchmark | Gemini 1.5 Flash (May 2024) | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Instruction Following | 1258 | 1242 |
| LiveBench Instruction Following | — | 82.7% |
| IFEval | 83.1% | — |
Long Context Gemini 1.5 Flash (May 2024) leads
Gemini 1.5 Flash (May 2024): 39.0 (#187), Llama-3.3-70B-Instruct: 26.4 (#295)
| Benchmark | Gemini 1.5 Flash (May 2024) | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Longer Query | 1284 | 1256 |
| Fiction.LiveBench | — | 33.3% |
Writing & Preference Gemini 1.5 Flash (May 2024) leads
Gemini 1.5 Flash (May 2024): 48.7 (#196), Llama-3.3-70B-Instruct: 47.6 (#207)
| Benchmark | Gemini 1.5 Flash (May 2024) | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Text | 1287 | 1274 |
| LMArena Creative Writing | 1285 | 1250 |
| LMArena Multi-Turn | 1253 | 1280 |
| WildBench | 79.2% | — |
| LiveBench Language | — | 39.2% |
Frequently asked questions
Is Gemini 1.5 Flash (May 2024) better than Llama-3.3-70B-Instruct?
Gemini 1.5 Flash (May 2024) is the stronger model overall, scoring 33.2 to 30.6 on the Noometry Index.
Is Gemini 1.5 Flash (May 2024) or Llama-3.3-70B-Instruct better for coding?
Gemini 1.5 Flash (May 2024) scores higher on coding benchmarks: 34.4 versus 31.0 in the Noometry coding category.
How many benchmarks do Gemini 1.5 Flash (May 2024) and Llama-3.3-70B-Instruct share?
28 benchmarks have published results for both models. Gemini 1.5 Flash (May 2024) has 42 scored results on Noometry and Llama-3.3-70B-Instruct has 43.