# Gemma 3 4B vs Llama-3.3-70B-Instruct

> Llama-3.3-70B-Instruct is the stronger model overall, scoring 30.3 to 27.9 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.

- Canonical page: https://noometry.com/compare/gemma-3-4b-vs-llama-3-3-70b-instruct
- Last updated: 2026-10-10
- Shared benchmarks: 19

## Summary

- 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 30.5 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.

## Snapshot

| | Gemma 3 4B | Llama-3.3-70B-Instruct |
|---|---|---|
| Provider | Google | Meta |
| Noometry Index | 27.9 | 30.3 |
| Rank | 320 | 283 |
| Context | 131K | 128K |
| Input $/M | $0.04 | $0.10 |
| Output $/M | $0.08 | $0.32 |
| Weights | Open | Open |

## Coding

- Gemma 3 4B: 35.3 (#214)
- Llama-3.3-70B-Instruct: 30.6 (#284)

| 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

- Gemma 3 4B: 20.7 (#134)
- Llama-3.3-70B-Instruct: 25.6 (#100)

| Benchmark | Gemma 3 4B | Llama-3.3-70B-Instruct |
|---|---|---|
| Berkeley Function Calling Leaderboard | 19.6% | 31.9% |
| BALROG | — | 23% |

## Reasoning

- Gemma 3 4B: 12.7 (#330)
- Llama-3.3-70B-Instruct: 13.7 (#325)

| 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 | 127.32 |
| 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: 15.8 (#286)
- Llama-3.3-70B-Instruct: 14.3 (#292)

| Benchmark | Gemma 3 4B | Llama-3.3-70B-Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 7.5% | 5.1% |
| LMArena Math | 1239 | 1268 |
| LiveBench Math | — | 42.2% |
| MATH Level 5 | — | 41.6% |

## Knowledge

- Gemma 3 4B: 11.8 (#294)
- Llama-3.3-70B-Instruct: 30.5 (#220)

| 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: 42.7 (#189)
- Llama-3.3-70B-Instruct: 40.1 (#215)

| 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 | — | 1280 |
| LMArena Japanese | — | 1149 |
| LMArena Korean | — | 1142 |
| LMArena Spanish | — | 1269 |

## Instruction Following

- Gemma 3 4B: 65.4 (#219)
- Llama-3.3-70B-Instruct: 71.3 (#152)

| Benchmark | Gemma 3 4B | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Instruction Following | 1239 | 1242 |
| LiveBench Instruction Following | — | 82.7% |

## Long Context

- Gemma 3 4B: 38.6 (#189)
- Llama-3.3-70B-Instruct: 26.4 (#290)

| Benchmark | Gemma 3 4B | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Longer Query | 1274 | 1256 |
| Fiction.LiveBench | — | 33.3% |

## Writing & Preference

- Gemma 3 4B: 42.2 (#234)
- Llama-3.3-70B-Instruct: 47.8 (#203)

| Benchmark | Gemma 3 4B | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Text | 1291 | 1274 |
| LMArena Creative Writing | 1270 | 1250 |
| LMArena Multi-Turn | 1255 | 1279 |
| EQ-Bench Creative Writing | 1068 | — |
| LiveBench Language | — | 39.2% |

## FAQ

### Is Gemma 3 4B better than Llama-3.3-70B-Instruct?

Llama-3.3-70B-Instruct is the stronger model overall, scoring 30.3 to 27.9 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: 35.3 versus 30.6 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.