Gemma 3 12B vs GPT-3.5 Turbo
GPT-3.5 Turbo leads Gemma 3 12B on the Artificial Analysis Intelligence Index, 5.5 to 3.8, and Gemma 3 12B costs 9.2x less per output token ($0.1625 vs $1.50 per million). Updated October 22, 2026; 7 benchmarks have a result for both.
Gemma 3 12B or GPT-3.5 Turbo: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Gemma 3 12B for output tokens 9.2x cheaper ($0.1625 vs $1.50 per million) and a larger context window (131K against 16K tokens).
- Shared results
- 7
- Gemma 3 12B only
- 36
- GPT-3.5 Turbo only
- 6
- Like-for-like categories
- 5 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-3.5 Turbo
GPT-3.5 Turbo leads, 5.5 against 3.8.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Gemma 3 12B has a AA Agentic Index result, 0.1.
Directional only
Reasoning
GPQA Diamond
No call
Only GPT-3.5 Turbo has a GPQA Diamond result, 30.6%.
Directional only
Long documents
Listed context window
Gemma 3 12B
Gemma 3 12B takes 131K tokens against 16K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Gemma 3 12B
Gemma 3 12B $0.0002, GPT-3.5 Turbo $0.0013 per turn.
List prices
At a glance
- Coding index
- Gemma 3 12B not benchmarked, GPT-3.5 Turbo not benchmarked.
- Widest category
- Knowledge, 13.3 points apart on average over 1 shared benchmark.
- Widest single gap
- MMLU Pro (Artificial Analysis run): Gemma 3 12B 59.5% against GPT-3.5 Turbo 46.2%, 13.3 points apart on a 0 to 100 scale.
- Output price
- Gemma 3 12B $0.1625 against GPT-3.5 Turbo $1.50 per million output tokens, 9.2x apart.
- Context window
- Gemma 3 12B takes 131K tokens, GPT-3.5 Turbo 16K.
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 12B | Bars | GPT-3.5 Turbo | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | 1 tie | |
| Intelligence | 0 | 1 | ||
| Reasoning | 1 | 0 | ||
| Knowledge | 1 | 0 | ||
| General | 1 | 1 | ||
| All shared boards | 3 | 3 | 1 tie |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
| Benchmark | Gemma 3 12B | GPT-3.5 Turbo | Leader |
|---|---|---|---|
| AA Coding Index | 5.8 | 10.7 | GPT-3.5 Turbo |
| LiveCodeBench (Artificial Analysis run) | 13.7% | Not measured | Only Gemma 3 12B measured |
| SciCode | 16.4% | Not measured | Only Gemma 3 12B measured |
| Terminal-Bench Hard (Artificial Analysis run) | 0.8% | Not measured | Only Gemma 3 12B measured |
| LMArena Text: coding | 1281 | Not measured | Only Gemma 3 12B measured |
| LMArena Text: software and IT | 1313 | Not measured | Only Gemma 3 12B measured |
| SciCode | 0.0% | 0.0% | Tie |
| Neither model has a result on the other 36 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Gemma 3 12B | GPT-3.5 Turbo | Leader |
|---|---|---|---|
| AA Intelligence Index | 3.8 | 5.5 | GPT-3.5 Turbo |
| Epoch Capabilities Index | 123.5 | Not measured | Only Gemma 3 12B measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | Gemma 3 12B | GPT-3.5 Turbo | Leader |
|---|---|---|---|
| AA Agentic Index | 0.1 | Not measured | Only Gemma 3 12B measured |
| τ²-Bench Telecom | 10.8% | Not measured | Only Gemma 3 12B measured |
| Berkeley Function Calling Leaderboard (BFCL) | 30.4% | Not measured | Only Gemma 3 12B measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Gemma 3 12B | GPT-3.5 Turbo | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 30.6% | Only GPT-3.5 Turbo measured |
| GPQA Diamond (Artificial Analysis run) | 34.9% | 29.7% | Gemma 3 12B |
| Humanity's Last Exam (Artificial Analysis run) | 4.2% | Not measured | Only Gemma 3 12B measured |
| GPQA Diamond (Epoch AI run) | 39.5% | Not measured | Only Gemma 3 12B measured |
| BIG-Bench Hard | Not measured | 61.6% | Only GPT-3.5 Turbo measured |
| LMArena Text: hard prompts | 1309 | Not measured | Only Gemma 3 12B measured |
| LMArena Text: expert | 1248 | Not measured | Only Gemma 3 12B measured |
| LMArena Text: hard prompts in English | 1318 | Not measured | Only Gemma 3 12B measured |
| Neither model has a result on the other 15 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Gemma 3 12B | GPT-3.5 Turbo | Leader |
|---|---|---|---|
| AA Math Index | 18.3 | Not measured | Only Gemma 3 12B measured |
| AIME 2025 (Artificial Analysis run) | 18.3% | Not measured | Only Gemma 3 12B measured |
| OTIS Mock AIME | 16.7% | Not measured | Only Gemma 3 12B measured |
| GSM8K | Not measured | 57.8% | Only GPT-3.5 Turbo measured |
| LMArena Text: math | 1307 | Not measured | Only Gemma 3 12B measured |
| LMArena Text: mathematical work | 1336 | Not measured | Only Gemma 3 12B measured |
| Neither model has a result on the other 8 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Gemma 3 12B | GPT-3.5 Turbo | Leader |
|---|---|---|---|
| LegalBench (Vals AI) | Not measured | 64.4% | Only GPT-3.5 Turbo measured |
| MedQA (Vals AI) | Not measured | 58.5% | Only GPT-3.5 Turbo measured |
| MMLU Pro (Artificial Analysis run) | 59.5% | 46.2% | Gemma 3 12B |
| MMLU | Not measured | 68.5% | Only GPT-3.5 Turbo measured |
| LMArena Text: science | 1366 | Not measured | Only Gemma 3 12B measured |
| LMArena Text: medicine and healthcare | 1334 | Not measured | Only Gemma 3 12B measured |
| LMArena Text: legal and government | 1381 | Not measured | Only Gemma 3 12B measured |
| LMArena Text: business and finance | 1376 | Not measured | Only Gemma 3 12B 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 12B | GPT-3.5 Turbo | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | Gemma 3 12B | GPT-3.5 Turbo | Leader |
|---|---|---|---|
| IFBench | 36.7% | Not measured | Only Gemma 3 12B measured |
| Long context reasoning (AA-LCR) | 8.3% | Not measured | Only Gemma 3 12B measured |
| DTBench | 48.8% | 48.5% | Gemma 3 12B |
| LMCA | 4.5% | 9.7% | GPT-3.5 Turbo |
| LMArena Text | 1334 | Not measured | Only Gemma 3 12B measured |
| LMArena Text: instruction following | 1299 | Not measured | Only Gemma 3 12B measured |
| LMArena Text: creative writing | 1331 | Not measured | Only Gemma 3 12B measured |
| LMArena Text: English | 1349 | Not measured | Only Gemma 3 12B measured |
| LMArena Text: non-English | 1318 | Not measured | Only Gemma 3 12B measured |
| LMArena Text: German | 1370 | Not measured | Only Gemma 3 12B measured |
| LMArena Text: Russian | 1335 | Not measured | Only Gemma 3 12B measured |
| LMArena Text: multi-turn | 1334 | Not measured | Only Gemma 3 12B measured |
| LMArena Text: longer queries | 1317 | Not measured | Only Gemma 3 12B measured |
| LMArena Text: writing and language | 1307 | Not measured | Only Gemma 3 12B measured |
| LMArena Text: entertainment and media | 1297 | Not measured | Only Gemma 3 12B measured |
| Neither model has a result on the other 33 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Gemma 3 12B | GPT-3.5 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 3 12B costs less
Repository review
50K input + 3K output tokens
Gemma 3 12B costs less
Agent loop
200K input + 10K output tokens
Gemma 3 12B 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 12B | OpenAI GPT-3.5 Turbo |
|---|---|---|
| Maker | OpenAI | |
| Weights | Open weights | No public weights listed |
| Released | March 13, 2025 | May 28, 2023 |
| Context window | 131K | 16K |
| Input $/Mtok | $0.075 | $0.50 |
| Output $/Mtok | $0.1625 | $1.50 |
| 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 12B better than GPT-3.5 Turbo for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Gemma 3 12B for output tokens 9.2x cheaper ($0.1625 vs $1.50 per million) and a larger context window (131K against 16K tokens).
Which is cheaper, Gemma 3 12B or GPT-3.5 Turbo?
Gemma 3 12B costs $0.075 per million input tokens and $0.1625 per million output tokens; GPT-3.5 Turbo costs $0.50 and $1.50. Cost per solved task is known for neither of them.
Which has the bigger context window, Gemma 3 12B or GPT-3.5 Turbo?
Gemma 3 12B, with 131K tokens against 16K.
How many benchmarks is the Gemma 3 12B vs GPT-3.5 Turbo comparison based on?
7 benchmarks have a result for both models as of October 22, 2026. Gemma 3 12B's coding index rests on 0 independent benchmarks and GPT-3.5 Turbo's on 0. Where only one model has a result, the page says so instead of naming a winner.