Gemma 3 27B vs Llama 3.2 3B Instruct
Llama 3.2 3B Instruct leads Gemma 3 27B on the Artificial Analysis Intelligence Index, 5.7 to 4.9, at about the same output price ($0.3325 vs $0.30 per million). Updated October 22, 2026; 34 benchmarks have a result for both.
Gemma 3 27B or Llama 3.2 3B Instruct: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Gemma 3 27B for output tokens 1.1x cheaper ($0.30 vs $0.3325 per million) and work measured by AA Intelligence Index or τ²-Bench Telecom, where it leads.
- Shared results
- 34
- Gemma 3 27B only
- 32
- Llama 3.2 3B Instruct only
- 1
- 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 3.2 3B Instruct
Llama 3.2 3B Instruct leads, 5.7 against 4.9.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Gemma 3 27B has a AA Agentic Index result, 0.1.
Directional only
Long documents
Listed context window
No call
Both take 131K tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Llama 3.2 3B Instruct
Gemma 3 27B $0.0002, Llama 3.2 3B Instruct $0.0002 per turn.
List prices
At a glance
- Coding index
- Gemma 3 27B not benchmarked, Llama 3.2 3B Instruct not benchmarked.
- Widest category
- Knowledge, 43.4 points apart on average over 5 shared benchmarks.
- Widest single gap
- LMArena Text: Russian: Gemma 3 27B 1349 against Llama 3.2 3B Instruct 949, 58.2 points apart on a 0 to 100 scale.
- Output price
- Gemma 3 27B $0.30 against Llama 3.2 3B Instruct $0.3325 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 27B | Bars | Llama 3.2 3B Instruct | Ties |
|---|---|---|---|---|
| Coding | 3 | 0 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 1 | 1 | ||
| Reasoning | 4 | 1 | ||
| Math | 4 | 0 | ||
| Knowledge | 5 | 0 | ||
| General | 14 | 0 | ||
| All shared boards | 31 | 3 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index30
| Benchmark | Gemma 3 27B | Llama 3.2 3B Instruct | Leader |
|---|---|---|---|
| Aider polyglot | 4.9% | Not measured | Only Gemma 3 27B measured |
| AA Coding Index | 10.1 | Not measured | Only Gemma 3 27B measured |
| LiveCodeBench (Artificial Analysis run) | 13.7% | 8.3% | Gemma 3 27B |
| SciCode | 23.3% | Not measured | Only Gemma 3 27B measured |
| Terminal-Bench Hard (Artificial Analysis run) | 3.8% | Not measured | Only Gemma 3 27B measured |
| LMArena Text: coding | 1322 | 1098 | Gemma 3 27B |
| LMArena Text: software and IT | 1354 | 1103 | Gemma 3 27B |
| SciCode | 0.0% | Not measured | Only Gemma 3 27B measured |
| Neither model has a result on the other 31 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Gemma 3 27B | Llama 3.2 3B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 4.9 | 5.7 | Llama 3.2 3B Instruct |
| Epoch Capabilities Index | 130.0 | Not measured | Only Gemma 3 27B measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | Gemma 3 27B | Llama 3.2 3B Instruct | Leader |
|---|---|---|---|
| AA Agentic Index | 0.1 | Not measured | Only Gemma 3 27B measured |
| τ²-Bench Telecom | 10.5% | 21.1% | Llama 3.2 3B Instruct |
| BALROG | Not measured | 10.1% | Only Llama 3.2 3B Instruct measured |
| Berkeley Function Calling Leaderboard (BFCL) | 29.5% | 21.9% | Gemma 3 27B |
| Neither model has a result on the other 37 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index41
| Benchmark | Gemma 3 27B | Llama 3.2 3B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 42.8% | 25.5% | Gemma 3 27B |
| Humanity's Last Exam (Artificial Analysis run) | 4.4% | 5.3% | Llama 3.2 3B Instruct |
| GPQA Diamond (Epoch AI run) | 47.7% | Not measured | Only Gemma 3 27B measured |
| LMArena Text: hard prompts | 1340 | 1095 | Gemma 3 27B |
| LMArena Text: expert | 1304 | 1090 | Gemma 3 27B |
| LMArena Text: hard prompts in English | 1353 | 1141 | Gemma 3 27B |
| LMArena Vision: diagrams | 1156 | Not measured | Only Gemma 3 27B measured |
| LMArena Vision: homework | 1160 | Not measured | Only Gemma 3 27B measured |
| Kagi LLM Benchmark | 35.1% | Not measured | Only Gemma 3 27B measured |
| Neither model has a result on the other 13 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | Gemma 3 27B | Llama 3.2 3B Instruct | Leader |
|---|---|---|---|
| AA Math Index | 20.7 | 3.3 | Gemma 3 27B |
| AIME 2025 (Artificial Analysis run) | 20.7% | 3.3% | Gemma 3 27B |
| OTIS Mock AIME | 22.5% | Not measured | Only Gemma 3 27B measured |
| MATH level 5 | 74.0% | Not measured | Only Gemma 3 27B measured |
| LMArena Text: math | 1312 | 1126 | Gemma 3 27B |
| LMArena Text: mathematical work | 1327 | 1128 | Gemma 3 27B |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | Gemma 3 27B | Llama 3.2 3B Instruct | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 66.9% | 34.7% | Gemma 3 27B |
| GeoBench | 52.0% | Not measured | Only Gemma 3 27B measured |
| LMArena Text: science | 1383 | 1106 | Gemma 3 27B |
| LMArena Text: medicine and healthcare | 1391 | 1104 | Gemma 3 27B |
| LMArena Text: legal and government | 1375 | 1115 | Gemma 3 27B |
| LMArena Text: business and finance | 1360 | 1074 | Gemma 3 27B |
| FACTS Benchmark Suite | 29.1% | Not measured | Only Gemma 3 27B measured |
| Neither model has a result on the other 11 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Gemma 3 27B | Llama 3.2 3B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index140
| Benchmark | Gemma 3 27B | Llama 3.2 3B Instruct | Leader |
|---|---|---|---|
| IFBench | 31.8% | 26.2% | Gemma 3 27B |
| Long context reasoning (AA-LCR) | 7.3% | 4.3% | Gemma 3 27B |
| DTBench | 52.5% | Not measured | Only Gemma 3 27B measured |
| LMCA | 12.3% | Not measured | Only Gemma 3 27B measured |
| Fiction.LiveBench | 33.3% | Not measured | Only Gemma 3 27B measured |
| Creative writing (Lech Mazur) | 79.9% | Not measured | Only Gemma 3 27B measured |
| LMArena Text | 1358 | 1110 | Gemma 3 27B |
| LMArena Text: instruction following | 1321 | 1089 | Gemma 3 27B |
| LMArena Text: creative writing | 1345 | 1094 | Gemma 3 27B |
| LMArena Vision | 1165 | Not measured | Only Gemma 3 27B measured |
| LMArena Text: English | 1377 | 1180 | Gemma 3 27B |
| LMArena Text: non-English | 1334 | 1019 | Gemma 3 27B |
| LMArena Text: Chinese | 1346 | 1017 | Gemma 3 27B |
| LMArena Text: German | 1362 | 1056 | Gemma 3 27B |
| LMArena Text: Japanese | 1287 | Not measured | Only Gemma 3 27B measured |
| LMArena Text: Korean | 1308 | Not measured | Only Gemma 3 27B measured |
| LMArena Text: French | 1368 | Not measured | Only Gemma 3 27B measured |
| LMArena Text: Spanish | 1349 | Not measured | Only Gemma 3 27B measured |
| LMArena Text: Russian | 1349 | 949 | Gemma 3 27B |
| LMArena Text: Polish | 1345 | Not measured | Only Gemma 3 27B measured |
| LMArena Text: multi-turn | 1345 | 1105 | Gemma 3 27B |
| LMArena Text: longer queries | 1333 | 1100 | Gemma 3 27B |
| LMArena Text: writing and language | 1347 | 1096 | Gemma 3 27B |
| LMArena Text: entertainment and media | 1320 | 1083 | Gemma 3 27B |
| LMArena Vision: English | 1188 | Not measured | Only Gemma 3 27B measured |
| LMArena Vision: Chinese | 1164 | Not measured | Only Gemma 3 27B measured |
| LMArena Vision: reading text | 1156 | Not measured | Only Gemma 3 27B measured |
| LMArena Vision: humor | 1166 | Not measured | Only Gemma 3 27B measured |
| LMArena Vision: captioning | 1151 | Not measured | Only Gemma 3 27B measured |
| LMArena Vision: recognizing things | 1196 | Not measured | Only Gemma 3 27B measured |
| LMArena Vision: creative writing | 1177 | Not measured | Only Gemma 3 27B measured |
| Neither model has a result on the other 15 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Gemma 3 27B | Llama 3.2 3B 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
Llama 3.2 3B Instruct costs less
Repository review
50K input + 3K output tokens
Llama 3.2 3B Instruct costs less
Agent loop
200K input + 10K output tokens
Llama 3.2 3B Instruct 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 27B | Meta Llama 3.2 3B Instruct |
|---|---|---|
| Maker | Meta Llama | |
| Weights | Open weights | Open weights |
| Released | March 12, 2025 | September 25, 2024 |
| Context window | 131K | 131K |
| Input $/Mtok | $0.08 | $0.0505 |
| Output $/Mtok | $0.30 | $0.3325 |
| 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 27B better than Llama 3.2 3B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Gemma 3 27B for output tokens 1.1x cheaper ($0.30 vs $0.3325 per million) and work measured by AA Intelligence Index or τ²-Bench Telecom, where it leads.
Which is cheaper, Gemma 3 27B or Llama 3.2 3B Instruct?
Gemma 3 27B costs $0.08 per million input tokens and $0.30 per million output tokens; Llama 3.2 3B Instruct costs $0.0505 and $0.3325. Cost per solved task is known for neither of them.
Which has the bigger context window, Gemma 3 27B or Llama 3.2 3B Instruct?
Neither: both take 131K tokens.
How many benchmarks is the Gemma 3 27B vs Llama 3.2 3B Instruct comparison based on?
34 benchmarks have a result for both models as of October 22, 2026. Gemma 3 27B's coding index rests on 0 independent benchmarks and Llama 3.2 3B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.