Gemma 3 4B vs Llama 3.3 70B Instruct
Llama 3.3 70B Instruct leads Gemma 3 4B on the Artificial Analysis Intelligence Index, 7.7 to 4.8, and Gemma 3 4B costs 9.6x less per output token ($0.075 vs $0.72 per million). Updated October 21, 2026; 40 benchmarks have a result for both.
Gemma 3 4B or Llama 3.3 70B Instruct: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Gemma 3 4B for output tokens 9.6x cheaper ($0.075 vs $0.72 per million) and work measured by AA Coding Index or LiveCodeBench (Artificial Analysis run), where it leads.
- Shared results
- 40
- Gemma 3 4B only
- 2
- Llama 3.3 70B Instruct only
- 12
- Like-for-like categories
- 7 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
Llama 3.3 70B Instruct
Llama 3.3 70B Instruct leads, 7.7 against 4.8.
Same board, both measured
Long documents
Listed context window
No call
Both take 131K tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Gemma 3 4B
Gemma 3 4B $0.0001, Llama 3.3 70B Instruct $0.0008 per turn.
List prices
At a glance
- Coding index
- Gemma 3 4B not benchmarked, Llama 3.3 70B Instruct not benchmarked.
- Widest category
- Agentic work, 17.0 points apart on average over 2 shared benchmarks.
- Widest single gap
- MMLU Pro (Artificial Analysis run): Gemma 3 4B 41.7% against Llama 3.3 70B Instruct 71.3%, 29.6 points apart on a 0 to 100 scale.
- Output price
- Gemma 3 4B $0.075 against Llama 3.3 70B Instruct $0.72 per million output tokens, 9.6x apart.
- Context window
- Both take 131K tokens.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | Gemma 3 4B | Bars | Llama 3.3 70B Instruct | Ties |
|---|---|---|---|---|
| Coding | 0 | 5 | ||
| Intelligence | 0 | 2 | ||
| Agentic work | 0 | 2 | ||
| Reasoning | 1 | 5 | ||
| Math | 3 | 2 | ||
| Knowledge | 4 | 1 | ||
| General | 7 | 8 | ||
| All shared boards | 15 | 25 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index05
| Benchmark | Gemma 3 4B | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| AA Coding Index | 2.7 | 11.9 | Llama 3.3 70B Instruct |
| LiveCodeBench (Artificial Analysis run) | 11.2% | 28.8% | Llama 3.3 70B Instruct |
| Terminal-Bench Hard (Artificial Analysis run) | 0.8% | 3.0% | Llama 3.3 70B Instruct |
| WeirdML | Not measured | 14.4% | Only Llama 3.3 70B Instruct measured |
| LMArena Text: coding | 1230 | 1268 | Llama 3.3 70B Instruct |
| LMArena Text: software and IT | 1269 | 1273 | Llama 3.3 70B Instruct |
| SciCode | 0.0% | Not measured | Only Gemma 3 4B measured |
| Neither model has a result on the other 32 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Gemma 3 4B | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 4.8 | 7.7 | Llama 3.3 70B Instruct |
| Epoch Capabilities Index | 115.9 | 127.3 | Llama 3.3 70B Instruct |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Gemma 3 4B | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 5.0% | 26.6% | Llama 3.3 70B Instruct |
| BALROG | Not measured | 23.0% | Only Llama 3.3 70B Instruct measured |
| Berkeley Function Calling Leaderboard (BFCL) | 19.6% | 31.9% | Llama 3.3 70B Instruct |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index15
| Benchmark | Gemma 3 4B | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 29.1% | 49.8% | Llama 3.3 70B Instruct |
| Humanity's Last Exam (Artificial Analysis run) | 5.3% | 3.6% | Gemma 3 4B |
| GPQA Diamond (Epoch AI run) | 23.2% | 47.4% | Llama 3.3 70B Instruct |
| SimpleBench | Not measured | 19.9% | Only Llama 3.3 70B Instruct measured |
| LMArena Text: hard prompts | 1253 | 1257 | Llama 3.3 70B Instruct |
| LMArena Text: expert | 1223 | 1225 | Llama 3.3 70B Instruct |
| LMArena Text: hard prompts in English | 1253 | 1277 | Llama 3.3 70B Instruct |
| Kagi LLM Benchmark | 25.2% | Not measured | Only Gemma 3 4B measured |
| Neither model has a result on the other 14 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index32
| Benchmark | Gemma 3 4B | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| AA Math Index | 12.7 | 7.7 | Gemma 3 4B |
| AIME 2025 (Artificial Analysis run) | 12.7% | 7.7% | Gemma 3 4B |
| OTIS Mock AIME | 7.5% | 5.1% | Gemma 3 4B |
| MATH level 5 | Not measured | 41.6% | Only Llama 3.3 70B Instruct measured |
| LMArena Text: math | 1239 | 1267 | Llama 3.3 70B Instruct |
| LMArena Text: mathematical work | 1256 | 1267 | Llama 3.3 70B Instruct |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index41
| Benchmark | Gemma 3 4B | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 41.7% | 71.3% | Llama 3.3 70B Instruct |
| MMLU | Not measured | 86.3% | Only Llama 3.3 70B Instruct measured |
| LMArena Text: science | 1306 | 1284 | Gemma 3 4B |
| LMArena Text: medicine and healthcare | 1342 | 1275 | Gemma 3 4B |
| LMArena Text: legal and government | 1327 | 1284 | Gemma 3 4B |
| LMArena Text: business and finance | 1311 | 1243 | Gemma 3 4B |
| Neither model has a result on the other 12 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Gemma 3 4B | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index78
| Benchmark | Gemma 3 4B | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| IFBench | 28.3% | 47.1% | Llama 3.3 70B Instruct |
| Long context reasoning (AA-LCR) | 6.7% | 15.7% | Llama 3.3 70B Instruct |
| DTBench | 50.9% | 59.5% | Llama 3.3 70B Instruct |
| LMCA | 2.8% | 17.5% | Llama 3.3 70B Instruct |
| Fiction.LiveBench | Not measured | 33.3% | Only Llama 3.3 70B Instruct measured |
| LMArena Text | 1291 | 1274 | Gemma 3 4B |
| LMArena Text: instruction following | 1239 | 1242 | Llama 3.3 70B Instruct |
| LMArena Text: creative writing | 1270 | 1250 | Gemma 3 4B |
| LMArena Text: English | 1300 | 1303 | Llama 3.3 70B Instruct |
| LMArena Text: non-English | 1273 | 1236 | Gemma 3 4B |
| LMArena Text: Chinese | Not measured | 1217 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: German | 1281 | 1251 | Gemma 3 4B |
| LMArena Text: Japanese | Not measured | 1150 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: Korean | Not measured | 1142 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: French | Not measured | 1282 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: Spanish | Not measured | 1269 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: Russian | 1294 | 1252 | Gemma 3 4B |
| LMArena Text: Polish | Not measured | 1259 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: multi-turn | 1255 | 1280 | Llama 3.3 70B Instruct |
| LMArena Text: longer queries | 1273 | 1255 | Gemma 3 4B |
| LMArena Text: writing and language | 1288 | 1255 | Gemma 3 4B |
| LMArena Text: entertainment and media | 1233 | 1241 | Llama 3.3 70B Instruct |
| Neither model has a result on the other 24 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Gemma 3 4B | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
Gemma 3 4B costs less
Repository review
50K input + 3K output tokens
Gemma 3 4B costs less
Agent loop
200K input + 10K output tokens
Gemma 3 4B costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | Google Gemma 3 4B | Meta Llama 3.3 70B Instruct |
|---|---|---|
| Maker | Meta Llama | |
| Weights | Open weights | Open weights |
| Released | March 13, 2025 | December 6, 2024 |
| Context window | 131K | 131K |
| Input $/Mtok | $0.045 | $0.45 |
| Output $/Mtok | $0.075 | $0.72 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Gemma 3 4B better than Llama 3.3 70B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Gemma 3 4B for output tokens 9.6x cheaper ($0.075 vs $0.72 per million) and work measured by AA Coding Index or LiveCodeBench (Artificial Analysis run), where it leads.
Which is cheaper, Gemma 3 4B or Llama 3.3 70B Instruct?
Gemma 3 4B costs $0.045 per million input tokens and $0.075 per million output tokens; Llama 3.3 70B Instruct costs $0.45 and $0.72. Cost per solved task is known for neither of them.
Which has the bigger context window, Gemma 3 4B or Llama 3.3 70B Instruct?
Neither: both take 131K tokens.
How many benchmarks is the Gemma 3 4B vs Llama 3.3 70B Instruct comparison based on?
40 benchmarks have a result for both models as of October 21, 2026. Gemma 3 4B's coding index rests on 0 independent benchmarks and Llama 3.3 70B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.