Gemma 3 4B vs Llama 3.1 8B Instruct
Llama 3.1 8B Instruct leads Gemma 3 4B on the Artificial Analysis Intelligence Index, 6.9 to 4.8, at about the same output price ($0.08 vs $0.075 per million). Updated October 21, 2026; 40 benchmarks have a result for both.
Gemma 3 4B or Llama 3.1 8B 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 1.1x cheaper ($0.075 vs $0.08 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 3.1 8B Instruct only
- 11
- 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 3.1 8B Instruct
Llama 3.1 8B Instruct leads, 6.9 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 3.1 8B Instruct $0.0001 per turn.
List prices
At a glance
- Coding index
- Gemma 3 4B not benchmarked, Llama 3.1 8B Instruct not benchmarked.
- Widest category
- Knowledge, 19.1 points apart on average over 5 shared benchmarks.
- Widest single gap
- LMArena Text: medicine and healthcare: Gemma 3 4B 1342 against Llama 3.1 8B Instruct 1182, 26.7 points apart on a 0 to 100 scale.
- Output price
- Gemma 3 4B $0.075 against Llama 3.1 8B Instruct $0.08 per million output tokens, 1.1x 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 3.1 8B Instruct | Ties |
|---|---|---|---|---|
| Coding | 2 | 2 | 1 tie | |
| Intelligence | 0 | 2 | ||
| Agentic work | 0 | 2 | ||
| Reasoning | 4 | 1 | 1 tie | |
| Math | 5 | 0 | ||
| Knowledge | 4 | 1 | ||
| General | 11 | 3 | 1 tie | |
| All shared boards | 26 | 11 | 3 ties |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index22
| Benchmark | Gemma 3 4B | Llama 3.1 8B Instruct | Leader |
|---|---|---|---|
| AA Coding Index | 2.7 | 5.4 | Llama 3.1 8B Instruct |
| LiveCodeBench (Artificial Analysis run) | 11.2% | 11.6% | Llama 3.1 8B Instruct |
| Terminal-Bench Hard (Artificial Analysis run) | 0.8% | 0.8% | Tie |
| WeirdML | Not measured | 1.7% | Only Llama 3.1 8B Instruct measured |
| LMArena Text: coding | 1230 | 1195 | Gemma 3 4B |
| LMArena Text: software and IT | 1269 | 1195 | Gemma 3 4B |
| 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 3.1 8B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 4.8 | 6.9 | Llama 3.1 8B Instruct |
| Epoch Capabilities Index | 115.9 | 116.5 | Llama 3.1 8B Instruct |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Gemma 3 4B | Llama 3.1 8B Instruct | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 5.0% | 16.4% | Llama 3.1 8B Instruct |
| BALROG | Not measured | 15.1% | Only Llama 3.1 8B Instruct measured |
| Berkeley Function Calling Leaderboard (BFCL) | 19.6% | 25.8% | Llama 3.1 8B Instruct |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index41
| Benchmark | Gemma 3 4B | Llama 3.1 8B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 29.1% | 25.9% | Gemma 3 4B |
| Humanity's Last Exam (Artificial Analysis run) | 5.3% | 5.3% | Tie |
| GPQA Diamond (Epoch AI run) | 23.2% | 27.0% | Llama 3.1 8B Instruct |
| LMArena Text: hard prompts | 1253 | 1175 | Gemma 3 4B |
| LMArena Text: expert | 1223 | 1144 | Gemma 3 4B |
| LMArena Text: hard prompts in English | 1253 | 1196 | Gemma 3 4B |
| Kagi LLM Benchmark | 25.2% | Not measured | Only Gemma 3 4B measured |
| Neither model has a result on the other 15 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | Gemma 3 4B | Llama 3.1 8B Instruct | Leader |
|---|---|---|---|
| AA Math Index | 12.7 | 4.3 | Gemma 3 4B |
| AIME 2025 (Artificial Analysis run) | 12.7% | 4.3% | Gemma 3 4B |
| OTIS Mock AIME | 7.5% | 1.7% | Gemma 3 4B |
| MATH level 5 | Not measured | 22.9% | Only Llama 3.1 8B Instruct measured |
| GSM8K | Not measured | 82.4% | Only Llama 3.1 8B Instruct measured |
| LMArena Text: math | 1239 | 1179 | Gemma 3 4B |
| LMArena Text: mathematical work | 1256 | 1180 | Gemma 3 4B |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index41
| Benchmark | Gemma 3 4B | Llama 3.1 8B Instruct | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 41.7% | 47.6% | Llama 3.1 8B Instruct |
| MMLU | Not measured | 56.1% | Only Llama 3.1 8B Instruct measured |
| LMArena Text: science | 1306 | 1193 | Gemma 3 4B |
| LMArena Text: medicine and healthcare | 1342 | 1182 | Gemma 3 4B |
| LMArena Text: legal and government | 1327 | 1206 | Gemma 3 4B |
| LMArena Text: business and finance | 1311 | 1166 | 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 3.1 8B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index113
| Benchmark | Gemma 3 4B | Llama 3.1 8B Instruct | Leader |
|---|---|---|---|
| IFBench | 28.3% | 28.6% | Llama 3.1 8B Instruct |
| Long context reasoning (AA-LCR) | 6.7% | 18.0% | Llama 3.1 8B Instruct |
| DTBench | 50.9% | 50.9% | Tie |
| LMCA | 2.8% | 5.4% | Llama 3.1 8B Instruct |
| PIQA | Not measured | 81.2% | Only Llama 3.1 8B Instruct measured |
| LMArena Text | 1291 | 1187 | Gemma 3 4B |
| LMArena Text: instruction following | 1239 | 1159 | Gemma 3 4B |
| LMArena Text: creative writing | 1270 | 1154 | Gemma 3 4B |
| LMArena Text: English | 1300 | 1218 | Gemma 3 4B |
| LMArena Text: non-English | 1273 | 1148 | Gemma 3 4B |
| LMArena Text: Chinese | Not measured | 1151 | Only Llama 3.1 8B Instruct measured |
| LMArena Text: German | 1281 | 1144 | Gemma 3 4B |
| LMArena Text: Japanese | Not measured | 1061 | Only Llama 3.1 8B Instruct measured |
| LMArena Text: Korean | Not measured | 1053 | Only Llama 3.1 8B Instruct measured |
| LMArena Text: French | Not measured | 1178 | Only Llama 3.1 8B Instruct measured |
| LMArena Text: Spanish | Not measured | 1169 | Only Llama 3.1 8B Instruct measured |
| LMArena Text: Russian | 1294 | 1158 | Gemma 3 4B |
| LMArena Text: multi-turn | 1255 | 1172 | Gemma 3 4B |
| LMArena Text: longer queries | 1273 | 1181 | Gemma 3 4B |
| LMArena Text: writing and language | 1288 | 1162 | Gemma 3 4B |
| LMArena Text: entertainment and media | 1233 | 1145 | Gemma 3 4B |
| Neither model has a result on the other 25 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Gemma 3 4B | Llama 3.1 8B 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 3.1 8B Instruct |
|---|---|---|
| Maker | Meta Llama | |
| Weights | Open weights | Open weights |
| Released | March 13, 2025 | July 23, 2024 |
| Context window | 131K | 131K |
| Input $/Mtok | $0.045 | $0.05 |
| Output $/Mtok | $0.075 | $0.08 |
| 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 3.1 8B 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 1.1x cheaper ($0.075 vs $0.08 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 3.1 8B Instruct?
Gemma 3 4B costs $0.045 per million input tokens and $0.075 per million output tokens; Llama 3.1 8B Instruct costs $0.05 and $0.08. Cost per solved task is known for neither of them.
Which has the bigger context window, Gemma 3 4B or Llama 3.1 8B Instruct?
Neither: both take 131K tokens.
How many benchmarks is the Gemma 3 4B vs Llama 3.1 8B 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 3.1 8B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.