Gemma 3 12B vs GPT-5.4 Pro
Gemma 3 12B costs 1107.7x less per output token than GPT-5.4 Pro ($0.1625 vs $180.00 per million). Updated October 22, 2026; 2 benchmarks have a result for both.
Gemma 3 12B or GPT-5.4 Pro: 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 1107.7x cheaper ($0.1625 vs $180.00 per million) and work measured by Epoch Capabilities Index or GPQA Diamond (Epoch AI run), where it leads.
- Shared results
- 2
- Gemma 3 12B only
- 41
- GPT-5.4 Pro only
- 8
- Like-for-like categories
- 2 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 Gemma 3 12B has a AA Intelligence Index result, 3.8.
Directional only
Agentic work
AA Agentic Index
No call
Only Gemma 3 12B has a AA Agentic Index result, 0.1.
Directional only
Long documents
Listed context window
GPT-5.4 Pro
GPT-5.4 Pro takes 1.05M tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Gemma 3 12B
Gemma 3 12B $0.0002, GPT-5.4 Pro $0.120 per turn.
List prices
At a glance
- Coding index
- Gemma 3 12B not benchmarked, GPT-5.4 Pro not benchmarked.
- Widest category
- Reasoning, 55.1 points apart on average over 1 shared benchmark.
- Widest single gap
- GPQA Diamond (Epoch AI run): Gemma 3 12B 39.5% against GPT-5.4 Pro 94.6%, 55.1 points apart on a 0 to 100 scale.
- Output price
- Gemma 3 12B $0.1625 against GPT-5.4 Pro $180.00 per million output tokens, 1107.7x apart.
- Context window
- Gemma 3 12B takes 131K tokens, GPT-5.4 Pro 1.05M.
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-5.4 Pro | Ties |
|---|---|---|---|---|
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 1 | ||
| All shared boards | 0 | 2 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index8 boards, none shared
| Benchmark | Gemma 3 12B | GPT-5.4 Pro | Leader |
|---|---|---|---|
| AA Coding Index | 5.8 | Not measured | Only Gemma 3 12B measured |
| 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 |
| WeirdML | Not measured | 57.4% | Only GPT-5.4 Pro 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% | Not measured | Only Gemma 3 12B 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 12B | GPT-5.4 Pro | Leader |
|---|---|---|---|
| AA Intelligence Index | 3.8 | Not measured | Only Gemma 3 12B measured |
| Epoch Capabilities Index | 123.5 | 159.6 | GPT-5.4 Pro |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | Gemma 3 12B | GPT-5.4 Pro | 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 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Gemma 3 12B | GPT-5.4 Pro | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 34.9% | Not measured | Only Gemma 3 12B measured |
| Humanity's Last Exam (Artificial Analysis run) | 4.2% | Not measured | Only Gemma 3 12B measured |
| GPQA Diamond (Epoch AI run) | 39.5% | 94.6% | GPT-5.4 Pro |
| ARC-AGI | Not measured | 94.5% | Only GPT-5.4 Pro measured |
| ARC-AGI-2 | Not measured | 83.3% | Only GPT-5.4 Pro measured |
| SimpleBench | Not measured | 74.1% | Only GPT-5.4 Pro measured |
| Chess puzzles | Not measured | 59.0% | Only GPT-5.4 Pro 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 12 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | Gemma 3 12B | GPT-5.4 Pro | 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 |
| FrontierMath, tiers 1 to 3 | Not measured | 82.5% | Only GPT-5.4 Pro measured |
| FrontierMath, tier 4 | Not measured | 58.5% | Only GPT-5.4 Pro 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 6 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Gemma 3 12B | GPT-5.4 Pro | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 59.5% | Not measured | Only Gemma 3 12B measured |
| SimpleQA Verified | Not measured | 46.3% | Only GPT-5.4 Pro 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-5.4 Pro | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index15 boards, none shared
| Benchmark | Gemma 3 12B | GPT-5.4 Pro | 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% | Not measured | Only Gemma 3 12B measured |
| LMCA | 4.5% | Not measured | Only Gemma 3 12B measured |
| 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 31 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Gemma 3 12B | GPT-5.4 Pro | 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-5.4 Pro |
|---|---|---|
| Maker | OpenAI | |
| Weights | Open weights | No public weights listed |
| Released | March 13, 2025 | March 5, 2026 |
| Context window | 131K | 1.05M |
| Input $/Mtok | $0.075 | $30.00 |
| Output $/Mtok | $0.1625 | $180.00 |
| 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 12B better than GPT-5.4 Pro for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Gemma 3 12B for output tokens 1107.7x cheaper ($0.1625 vs $180.00 per million) and work measured by Epoch Capabilities Index or GPQA Diamond (Epoch AI run), where it leads.
Which is cheaper, Gemma 3 12B or GPT-5.4 Pro?
Gemma 3 12B costs $0.075 per million input tokens and $0.1625 per million output tokens; GPT-5.4 Pro costs $30.00 and $180.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Gemma 3 12B or GPT-5.4 Pro?
GPT-5.4 Pro, with 1.05M tokens against 131K.
How many benchmarks is the Gemma 3 12B vs GPT-5.4 Pro comparison based on?
2 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-5.4 Pro's on 0. Where only one model has a result, the page says so instead of naming a winner.