Model comparison
Gemma 3 4B vs Llama-3.3-70B-Instruct
Llama-3.3-70B-Instruct is the stronger model overall, scoring 30.6 to 28.1 on the Noometry Index. Gemma 3 4B costs 3.1× less per token, which makes it the better buy when Llama-3.3-70B-Instruct's lead doesn't matter for your workload.
Last verified . 19 shared benchmarks.
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MetaSummary
- They share 19 benchmarks with published results for both. Gemma 3 4B scores higher in 4 categories and Llama-3.3-70B-Instruct in 5 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Llama-3.3-70B-Instruct leads 30.6 to 11.8.
- The biggest single-benchmark swing is GPQA Diamond: 23.2% for Gemma 3 4B and 47.4% for Llama-3.3-70B-Instruct.
- Gemma 3 4B is cheaper at $0.04 / $0.08 per million input/output tokens, against $0.10 / $0.32 for Llama-3.3-70B-Instruct.
- Gemma 3 4B accepts more context: 131K tokens versus 128K.
Side by side
| Gemma 3 4B | Llama-3.3-70B-Instruct | |
|---|---|---|
| Provider | Meta | |
| Noometry Index | 28.1 | 30.6 |
| Released | 2025-03-12 | 2024-12-06 |
| Weights | Open | Open |
| Context window | 131K | 128K |
| Max output | 4K | 4K |
| Input $ / M tokens | $0.04 | $0.10 |
| Output $ / M tokens | $0.08 | $0.32 |
| Results tracked | 22 | 43 |
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Category by category
Coding Gemma 3 4B leads
Gemma 3 4B: 35.9 (#215), Llama-3.3-70B-Instruct: 31.0 (#290)
| Benchmark | Gemma 3 4B | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Coding | 1230 | 1268 |
| SciCode | — | 26% |
| WeirdML | — | 14.4% |
| BigCodeBench Instruct | — | 46.9% |
| LiveBench Coding | — | 36.6% |
| BigCodeBench Complete | — | 57.5% |
Agentic & Tool Use Llama-3.3-70B-Instruct leads
Gemma 3 4B: 20.9 (#142), Llama-3.3-70B-Instruct: 25.8 (#105)
| Benchmark | Gemma 3 4B | Llama-3.3-70B-Instruct |
|---|---|---|
| Berkeley Function Calling Leaderboard | 19.6% | 31.9% |
| BALROG | — | 23% |
Reasoning Too close to call
Gemma 3 4B: 13.2 (#335), Llama-3.3-70B-Instruct: 14.1 (#327)
| Benchmark | Gemma 3 4B | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Hard Prompts | 1253 | 1257 |
| DTBench | 50.9% | 59.5% |
| LMCA | 2.8% | 17.5% |
| Epoch Capabilities Index | 116.02 | 127.33 |
| SimpleBench | — | 19.9% |
| Kagi LLM Benchmark | 25.2% | — |
| CritPt | — | 0% |
| Chess Puzzles | 0% | — |
| LiveBench Reasoning | — | 50.8% |
| LiveBench Data Analysis | — | 49.5% |
| ForecastBench | — | 58.6 |
| LiveBench | — | 50.2% |
Math Gemma 3 4B leads
Gemma 3 4B: 16.8 (#292), Llama-3.3-70B-Instruct: 15.3 (#298)
| Benchmark | Gemma 3 4B | Llama-3.3-70B-Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 7.5% | 5.1% |
| LMArena Math | 1239 | 1267 |
| LiveBench Math | — | 42.2% |
| MATH Level 5 | — | 41.6% |
Knowledge Llama-3.3-70B-Instruct leads
Gemma 3 4B: 11.8 (#299), Llama-3.3-70B-Instruct: 30.6 (#226)
| Benchmark | Gemma 3 4B | Llama-3.3-70B-Instruct |
|---|---|---|
| GPQA Diamond | 23.2% | 47.4% |
| Vectara Hallucination Rate | 6.4% | 4.1% |
| LMArena Expert | 1223 | 1225 |
| Confabulations | — | 22.8% |
| MMLU | — | 86.3% |
Multilingual Gemma 3 4B leads
Gemma 3 4B: 42.5 (#194), Llama-3.3-70B-Instruct: 39.9 (#220)
| Benchmark | Gemma 3 4B | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Non-English | 1273 | 1236 |
| LMArena German | 1281 | 1251 |
| LMArena Russian | 1294 | 1252 |
| LMArena Chinese | — | 1217 |
| LMArena French | — | 1281 |
| LMArena Japanese | — | 1150 |
| LMArena Korean | — | 1143 |
| LMArena Spanish | — | 1270 |
Instruction Following Llama-3.3-70B-Instruct leads
Gemma 3 4B: 65.2 (#225), Llama-3.3-70B-Instruct: 71.1 (#157)
| Benchmark | Gemma 3 4B | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Instruction Following | 1239 | 1242 |
| LiveBench Instruction Following | — | 82.7% |
Long Context Gemma 3 4B leads
Gemma 3 4B: 38.7 (#194), Llama-3.3-70B-Instruct: 26.4 (#295)
| Benchmark | Gemma 3 4B | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Longer Query | 1273 | 1256 |
| Fiction.LiveBench | — | 33.3% |
Writing & Preference Llama-3.3-70B-Instruct leads
Gemma 3 4B: 42.0 (#239), Llama-3.3-70B-Instruct: 47.6 (#207)
| Benchmark | Gemma 3 4B | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Text | 1291 | 1274 |
| LMArena Creative Writing | 1271 | 1250 |
| LMArena Multi-Turn | 1255 | 1280 |
| EQ-Bench Creative Writing | 1068 | — |
| LiveBench Language | — | 39.2% |
Frequently asked questions
Is Gemma 3 4B better than Llama-3.3-70B-Instruct?
Llama-3.3-70B-Instruct is the stronger model overall, scoring 30.6 to 28.1 on the Noometry Index. Gemma 3 4B costs 3.1× less per token, which makes it the better buy when Llama-3.3-70B-Instruct's lead doesn't matter for your workload.
Which is cheaper, Gemma 3 4B or Llama-3.3-70B-Instruct?
Gemma 3 4B is cheaper. It lists at $0.04 per million input tokens and $0.08 per million output tokens; Llama-3.3-70B-Instruct lists at $0.10 and $0.32.
Is Gemma 3 4B or Llama-3.3-70B-Instruct better for coding?
Gemma 3 4B scores higher on coding benchmarks: 35.9 versus 31.0 in the Noometry coding category.
Which has the bigger context window?
Gemma 3 4B does, with 131K tokens against 128K.
How many benchmarks do Gemma 3 4B and Llama-3.3-70B-Instruct share?
19 benchmarks have published results for both models. Gemma 3 4B has 22 scored results on Noometry and Llama-3.3-70B-Instruct has 43.