Gemma 2 27B vs GPT-4 Turbo
Gemma 2 27B costs 46.2x less per output token than GPT-4 Turbo ($0.65 vs $30.00 per million). Updated October 22, 2026; 33 benchmarks have a result for both.
Gemma 2 27B or GPT-4 Turbo: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Gemma 2 27B for output tokens 46.2x cheaper ($0.65 vs $30.00 per million) and work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.
- Shared results
- 33
- Gemma 2 27B only
- 3
- GPT-4 Turbo only
- 9
- 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
No call
Only GPT-4 Turbo has a AA Intelligence Index result, 7.0.
Directional only
Long documents
Listed context window
GPT-4 Turbo
GPT-4 Turbo takes 128K tokens against 8K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Gemma 2 27B
Gemma 2 27B $0.0010, GPT-4 Turbo $0.025 per turn.
List prices
At a glance
- Coding index
- Gemma 2 27B not benchmarked, GPT-4 Turbo not benchmarked.
- Widest category
- Reasoning, 9.1 points apart on average over 3 shared benchmarks.
- Widest single gap
- DTBench: Gemma 2 27B 48.0% against GPT-4 Turbo 61.6%, 13.6 points apart on a 0 to 100 scale.
- Output price
- Gemma 2 27B $0.65 against GPT-4 Turbo $30.00 per million output tokens, 46.2x apart.
- Context window
- Gemma 2 27B takes 8K tokens, GPT-4 Turbo 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 2 27B | Bars | GPT-4 Turbo | Ties |
|---|---|---|---|---|
| Coding | 0 | 2 | ||
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 3 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 6 | ||
| General | 0 | 19 | ||
| All shared boards | 0 | 33 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
| Benchmark | Gemma 2 27B | GPT-4 Turbo | Leader |
|---|---|---|---|
| AA Coding Index | Not measured | 21.5 | Only GPT-4 Turbo measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 29.1% | Only GPT-4 Turbo measured |
| LMArena Text: coding | 1211 | 1268 | GPT-4 Turbo |
| LMArena Text: software and IT | 1228 | 1267 | GPT-4 Turbo |
| 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 2 27B | GPT-4 Turbo | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 7.0 | Only GPT-4 Turbo measured |
| Epoch Capabilities Index | 122.1 | 127.3 | GPT-4 Turbo |
Agentic workNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Gemma 2 27B | GPT-4 Turbo | Leader |
|---|---|---|---|
| Neither model has a result on 41 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | Gemma 2 27B | GPT-4 Turbo | Leader |
|---|---|---|---|
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 3.1% | Only GPT-4 Turbo measured |
| GPQA Diamond (Epoch AI run) | 36.5% | Not measured | Only Gemma 2 27B measured |
| LMArena Text: hard prompts | 1198 | 1251 | GPT-4 Turbo |
| LMArena Text: expert | 1171 | 1223 | GPT-4 Turbo |
| LMArena Text: hard prompts in English | 1206 | 1266 | GPT-4 Turbo |
| Neither model has a result on the other 17 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Gemma 2 27B | GPT-4 Turbo | Leader |
|---|---|---|---|
| OTIS Mock AIME | 1.4% | Not measured | Only Gemma 2 27B measured |
| MATH level 5 | 27.9% | Not measured | Only Gemma 2 27B measured |
| LMArena Text: math | 1212 | 1272 | GPT-4 Turbo |
| LMArena Text: mathematical work | 1221 | 1268 | GPT-4 Turbo |
| Neither model has a result on the other 9 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | Gemma 2 27B | GPT-4 Turbo | Leader |
|---|---|---|---|
| LegalBench (Vals AI) | 68.0% | 80.5% | GPT-4 Turbo |
| MedQA (Vals AI) | Not measured | 82.0% | Only GPT-4 Turbo measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 69.4% | Only GPT-4 Turbo measured |
| MMLU | 75.7% | 81.3% | GPT-4 Turbo |
| LMArena Text: science | 1228 | 1256 | GPT-4 Turbo |
| LMArena Text: medicine and healthcare | 1198 | 1239 | GPT-4 Turbo |
| LMArena Text: legal and government | 1241 | 1265 | GPT-4 Turbo |
| LMArena Text: business and finance | 1208 | 1222 | GPT-4 Turbo |
| Neither model has a result on the other 10 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Gemma 2 27B | GPT-4 Turbo | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index019
| Benchmark | Gemma 2 27B | GPT-4 Turbo | Leader |
|---|---|---|---|
| DTBench | 48.0% | 61.6% | GPT-4 Turbo |
| LMCA | 7.1% | 9.8% | GPT-4 Turbo |
| LMArena Text | 1231 | 1272 | GPT-4 Turbo |
| LMArena Text: instruction following | 1206 | 1249 | GPT-4 Turbo |
| LMArena Text: creative writing | 1240 | 1268 | GPT-4 Turbo |
| LMArena Vision | Not measured | 1090 | Only GPT-4 Turbo measured |
| LMArena Text: English | 1242 | 1291 | GPT-4 Turbo |
| LMArena Text: non-English | 1217 | 1245 | GPT-4 Turbo |
| LMArena Text: Chinese | 1221 | 1242 | GPT-4 Turbo |
| LMArena Text: German | 1209 | 1259 | GPT-4 Turbo |
| LMArena Text: Japanese | 1175 | 1194 | GPT-4 Turbo |
| LMArena Text: Korean | 1174 | 1187 | GPT-4 Turbo |
| LMArena Text: French | 1248 | 1276 | GPT-4 Turbo |
| LMArena Text: Spanish | 1228 | 1260 | GPT-4 Turbo |
| LMArena Text: Russian | 1234 | 1259 | GPT-4 Turbo |
| LMArena Text: Polish | 1277 | 1302 | GPT-4 Turbo |
| LMArena Text: multi-turn | 1224 | 1267 | GPT-4 Turbo |
| LMArena Text: longer queries | 1230 | 1254 | GPT-4 Turbo |
| LMArena Text: writing and language | 1249 | 1279 | GPT-4 Turbo |
| LMArena Text: entertainment and media | 1212 | 1258 | GPT-4 Turbo |
| LMArena Vision: English | Not measured | 1097 | Only GPT-4 Turbo measured |
| LMArena Vision: Chinese | Not measured | 1045 | Only GPT-4 Turbo measured |
| 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 2 27B | GPT-4 Turbo | 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 2 27B costs less
Repository review
50K input + 3K output tokens
Gemma 2 27B costs less
Agent loop
200K input + 10K output tokens
Gemma 2 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 2 27B | OpenAI GPT-4 Turbo |
|---|---|---|
| Maker | OpenAI | |
| Weights | Open weights | No public weights listed |
| Released | July 13, 2024 | April 9, 2024 |
| Context window | 8K | 128K |
| Input $/Mtok | $0.65 | $10.00 |
| Output $/Mtok | $0.65 | $30.00 |
| 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 2 27B better than GPT-4 Turbo for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Gemma 2 27B for output tokens 46.2x cheaper ($0.65 vs $30.00 per million) and work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.
Which is cheaper, Gemma 2 27B or GPT-4 Turbo?
Gemma 2 27B costs $0.65 per million input tokens and $0.65 per million output tokens; GPT-4 Turbo costs $10.00 and $30.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Gemma 2 27B or GPT-4 Turbo?
GPT-4 Turbo, with 128K tokens against 8K.
How many benchmarks is the Gemma 2 27B vs GPT-4 Turbo comparison based on?
33 benchmarks have a result for both models as of October 22, 2026. Gemma 2 27B's coding index rests on 0 independent benchmarks and GPT-4 Turbo's on 0. Where only one model has a result, the page says so instead of naming a winner.