Gemma 3 27B vs GPT-4o (2024-05-13)
GPT-4o (2024-05-13) leads Gemma 3 27B on the Artificial Analysis Intelligence Index, 7.3 to 4.9, and Gemma 3 27B costs 50.0x less per output token ($0.30 vs $15.00 per million). Updated October 22, 2026; 40 benchmarks have a result for both.
Gemma 3 27B or GPT-4o (2024-05-13): 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 50.0x cheaper ($0.30 vs $15.00 per million) and a larger context window (131K against 128K tokens).
- Shared results
- 40
- Gemma 3 27B only
- 26
- GPT-4o (2024-05-13) only
- 4
- Like-for-like categories
- 6 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
GPT-4o (2024-05-13)
GPT-4o (2024-05-13) leads, 7.3 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
Reasoning
GPQA Diamond
No call
Only GPT-4o (2024-05-13) has a GPQA Diamond result, 50.2%.
Directional only
Long documents
Listed context window
Gemma 3 27B
Gemma 3 27B takes 131K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Gemma 3 27B
Gemma 3 27B $0.0002, GPT-4o (2024-05-13) $0.013 per turn.
List prices
At a glance
- Coding index
- Gemma 3 27B not benchmarked, GPT-4o (2024-05-13) not benchmarked.
- Widest category
- Knowledge, 13.4 points apart on average over 5 shared benchmarks.
- Widest single gap
- MATH level 5: Gemma 3 27B 74.0% against GPT-4o (2024-05-13) 51.0%, 23.0 points apart on a 0 to 100 scale.
- Output price
- Gemma 3 27B $0.30 against GPT-4o (2024-05-13) $15.00 per million output tokens, 50.0x apart.
- Context window
- Gemma 3 27B takes 131K tokens, GPT-4o (2024-05-13) 128K.
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 | GPT-4o (2024-05-13) | Ties |
|---|---|---|---|---|
| Coding | 2 | 2 | ||
| Intelligence | 0 | 1 | ||
| Reasoning | 4 | 2 | ||
| Math | 4 | 0 | ||
| Knowledge | 4 | 1 | ||
| General | 19 | 1 | ||
| All shared boards | 33 | 7 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index22
| Benchmark | Gemma 3 27B | GPT-4o (2024-05-13) | Leader |
|---|---|---|---|
| Aider polyglot | 4.9% | Not measured | Only Gemma 3 27B measured |
| AA Coding Index | 10.1 | 24.2 | GPT-4o (2024-05-13) |
| LiveCodeBench (Artificial Analysis run) | 13.7% | 33.4% | GPT-4o (2024-05-13) |
| 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 | 1297 | Gemma 3 27B |
| LMArena Text: software and IT | 1354 | 1297 | Gemma 3 27B |
| SciCode | 0.0% | Not measured | Only Gemma 3 27B measured |
| Neither model has a result on the other 35 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Gemma 3 27B | GPT-4o (2024-05-13) | Leader |
|---|---|---|---|
| AA Intelligence Index | 4.9 | 7.3 | GPT-4o (2024-05-13) |
| Epoch Capabilities Index | 130.0 | Not measured | Only Gemma 3 27B measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | Gemma 3 27B | GPT-4o (2024-05-13) | Leader |
|---|---|---|---|
| AA Agentic Index | 0.1 | Not measured | Only Gemma 3 27B measured |
| τ²-Bench Telecom | 10.5% | Not measured | Only Gemma 3 27B measured |
| METR time horizons | Not measured | 40.8% | Only GPT-4o (2024-05-13) measured |
| BALROG | Not measured | 32.3% | Only GPT-4o (2024-05-13) measured |
| Berkeley Function Calling Leaderboard (BFCL) | 29.5% | Not measured | Only Gemma 3 27B measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index42
| Benchmark | Gemma 3 27B | GPT-4o (2024-05-13) | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 50.2% | Only GPT-4o (2024-05-13) measured |
| GPQA Diamond (Artificial Analysis run) | 42.8% | 52.6% | GPT-4o (2024-05-13) |
| Humanity's Last Exam (Artificial Analysis run) | 4.4% | 1.8% | Gemma 3 27B |
| GPQA Diamond (Epoch AI run) | 47.7% | 48.9% | GPT-4o (2024-05-13) |
| LMArena Text: hard prompts | 1340 | 1281 | Gemma 3 27B |
| LMArena Text: expert | 1304 | 1250 | Gemma 3 27B |
| LMArena Text: hard prompts in English | 1353 | 1293 | 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 | GPT-4o (2024-05-13) | Leader |
|---|---|---|---|
| AA Math Index | 20.7 | Not measured | Only Gemma 3 27B measured |
| AIME 2025 (Artificial Analysis run) | 20.7% | Not measured | Only Gemma 3 27B measured |
| OTIS Mock AIME | 22.5% | 6.3% | Gemma 3 27B |
| MATH level 5 | 74.0% | 51.0% | Gemma 3 27B |
| LMArena Text: math | 1312 | 1284 | Gemma 3 27B |
| LMArena Text: mathematical work | 1327 | 1284 | Gemma 3 27B |
| Neither model has a result on the other 8 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index41
| Benchmark | Gemma 3 27B | GPT-4o (2024-05-13) | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 66.9% | 74.0% | GPT-4o (2024-05-13) |
| MMLU | Not measured | 84.2% | Only GPT-4o (2024-05-13) measured |
| GeoBench | 52.0% | Not measured | Only Gemma 3 27B measured |
| LMArena Text: science | 1383 | 1295 | Gemma 3 27B |
| LMArena Text: medicine and healthcare | 1391 | 1278 | Gemma 3 27B |
| LMArena Text: legal and government | 1375 | 1314 | Gemma 3 27B |
| LMArena Text: business and finance | 1360 | 1262 | Gemma 3 27B |
| FACTS Benchmark Suite | 29.1% | Not measured | Only Gemma 3 27B measured |
| 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 27B | GPT-4o (2024-05-13) | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index191
| Benchmark | Gemma 3 27B | GPT-4o (2024-05-13) | Leader |
|---|---|---|---|
| IFBench | 31.8% | Not measured | Only Gemma 3 27B measured |
| Long context reasoning (AA-LCR) | 7.3% | Not measured | Only Gemma 3 27B measured |
| 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 | 1300 | Gemma 3 27B |
| LMArena Text: instruction following | 1321 | 1278 | Gemma 3 27B |
| LMArena Text: creative writing | 1345 | 1291 | Gemma 3 27B |
| LMArena Vision | 1165 | 1137 | Gemma 3 27B |
| LMArena Text: English | 1377 | 1312 | Gemma 3 27B |
| LMArena Text: non-English | 1334 | 1283 | Gemma 3 27B |
| LMArena Text: Chinese | 1346 | 1277 | Gemma 3 27B |
| LMArena Text: German | 1362 | 1283 | Gemma 3 27B |
| LMArena Text: Japanese | 1287 | 1257 | Gemma 3 27B |
| LMArena Text: Korean | 1308 | 1234 | Gemma 3 27B |
| LMArena Text: French | 1368 | 1304 | Gemma 3 27B |
| LMArena Text: Spanish | 1349 | 1292 | Gemma 3 27B |
| LMArena Text: Russian | 1349 | 1285 | Gemma 3 27B |
| LMArena Text: Polish | 1345 | 1365 | GPT-4o (2024-05-13) |
| LMArena Text: multi-turn | 1345 | 1302 | Gemma 3 27B |
| LMArena Text: longer queries | 1333 | 1288 | Gemma 3 27B |
| LMArena Text: writing and language | 1347 | 1298 | Gemma 3 27B |
| LMArena Text: entertainment and media | 1320 | 1289 | Gemma 3 27B |
| LMArena Vision: English | 1188 | 1147 | Gemma 3 27B |
| LMArena Vision: Chinese | 1164 | 1132 | Gemma 3 27B |
| 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 17 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Gemma 3 27B | GPT-4o (2024-05-13) | 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 27B costs less
Repository review
50K input + 3K output tokens
Gemma 3 27B costs less
Agent loop
200K input + 10K output tokens
Gemma 3 27B 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 | OpenAI GPT-4o (2024-05-13) |
|---|---|---|
| Maker | OpenAI | |
| Weights | Open weights | No public weights listed |
| Released | March 12, 2025 | May 13, 2024 |
| Context window | 131K | 128K |
| Input $/Mtok | $0.08 | $5.00 |
| Output $/Mtok | $0.30 | $15.00 |
| 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 GPT-4o (2024-05-13) for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Gemma 3 27B for output tokens 50.0x cheaper ($0.30 vs $15.00 per million) and a larger context window (131K against 128K tokens).
Which is cheaper, Gemma 3 27B or GPT-4o (2024-05-13)?
Gemma 3 27B costs $0.08 per million input tokens and $0.30 per million output tokens; GPT-4o (2024-05-13) costs $5.00 and $15.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Gemma 3 27B or GPT-4o (2024-05-13)?
Gemma 3 27B, with 131K tokens against 128K.
How many benchmarks is the Gemma 3 27B vs GPT-4o (2024-05-13) comparison based on?
40 benchmarks have a result for both models as of October 22, 2026. Gemma 3 27B's coding index rests on 0 independent benchmarks and GPT-4o (2024-05-13)'s on 0. Where only one model has a result, the page says so instead of naming a winner.