Gemma 3 27B vs GPT-5.1-Codex-Mini
Updated October 21, 2026. 11 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Gemma 3 27B and GPT-5.1-Codex-Mini are level for coding, 4.9 against 6.2 on the Vibe Coding Index, so price and context decide.
Pick Gemma 3 27B when output tokens cost 5.7x less. Pick GPT-5.1-Codex-Mini when the context window is larger, 400K against 131K tokens, or when what matters is LiveCodeBench (Artificial Analysis run) or Terminal-Bench Hard (Artificial Analysis run), where it leads.
- Shared results
- 11
- Gemma 3 27B only
- 51
- GPT-5.1-Codex-Mini only
- 11
- Like-for-like categories
- 7 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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, four public coding benchmarks
No call
Level: 4.9 against 6.2, inside 2 points.
One benchmark behind each score
Intelligence
AA Intelligence Index
GPT-5.1-Codex-Mini
GPT-5.1-Codex-Mini leads, 20.4 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
Web design
Design Arena: websites
No call
Only GPT-5.1-Codex-Mini has a Design Arena: websites result, 1111.
Directional only
Long documents
Listed context window
GPT-5.1-Codex-Mini
GPT-5.1-Codex-Mini takes 400K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Gemma 3 27B
Gemma 3 27B $0.0003, GPT-5.1-Codex-Mini $0.0013 per turn.
List prices
At a glance
- Coding index
- Gemma 3 27B 4.9 from 1 benchmark, GPT-5.1-Codex-Mini 6.2 from 1 benchmark.
- Widest category
- Math, 71.0 points apart on average over 2 shared benchmarks.
- Widest single gap
- AA Math Index: Gemma 3 27B 20.7 against GPT-5.1-Codex-Mini 91.7, 71.0 points apart on a 0 to 100 scale.
- Output price
- Gemma 3 27B $0.35 against GPT-5.1-Codex-Mini $2.00 per million output tokens, 5.7x apart.
- Context window
- Gemma 3 27B takes 131K tokens, GPT-5.1-Codex-Mini 400K.
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-5.1-Codex-Mini | Ties |
|---|---|---|---|---|
| Coding | 0 | 2 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 2 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 1 | ||
| General | 0 | 2 | ||
| All shared boards | 0 | 11 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index02
| Benchmark | Gemma 3 27B | GPT-5.1-Codex-Mini | Leader |
|---|---|---|---|
| Aider polyglot | 4.9% | Not measured | Only Gemma 3 27B measured |
| LMArena WebDev | Not measured | 1244 | Only GPT-5.1-Codex-Mini measured |
| AA Coding Index | 10.1 | Not measured | Only Gemma 3 27B measured |
| LiveCodeBench (Artificial Analysis run) | 13.7% | 83.6% | GPT-5.1-Codex-Mini |
| SciCode | 23.3% | Not measured | Only Gemma 3 27B measured |
| Terminal-Bench Hard (Artificial Analysis run) | 3.8% | 33.3% | GPT-5.1-Codex-Mini |
| Terminal-Bench | Not measured | 61.6% | Only GPT-5.1-Codex-Mini measured |
| LMArena Text: coding | 1322 | Not measured | Only Gemma 3 27B measured |
| LMArena Text: software and IT | 1354 | Not measured | Only Gemma 3 27B measured |
| LMArena WebDev: HTML | Not measured | 1253 | Only GPT-5.1-Codex-Mini measured |
| Neither model has a result on the other 28 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Gemma 3 27B | GPT-5.1-Codex-Mini | Leader |
|---|---|---|---|
| AA Intelligence Index | 4.9 | 20.4 | GPT-5.1-Codex-Mini |
| Epoch Capabilities Index | 130.0 | Not measured | Only Gemma 3 27B measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Gemma 3 27B | GPT-5.1-Codex-Mini | Leader |
|---|---|---|---|
| AA Agentic Index | 0.1 | Not measured | Only Gemma 3 27B measured |
| τ²-Bench Telecom | 10.5% | 62.9% | GPT-5.1-Codex-Mini |
| Neither model has a result on the other 37 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Gemma 3 27B | GPT-5.1-Codex-Mini | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 42.8% | 81.3% | GPT-5.1-Codex-Mini |
| Humanity's Last Exam (Artificial Analysis run) | 4.4% | 18.5% | GPT-5.1-Codex-Mini |
| GPQA Diamond (Epoch AI run) | 47.7% | Not measured | Only Gemma 3 27B measured |
| LMArena Text: hard prompts | 1340 | Not measured | Only Gemma 3 27B measured |
| LMArena Text: expert | 1304 | Not measured | Only Gemma 3 27B measured |
| LMArena Text: hard prompts in English | 1353 | Not measured | Only Gemma 3 27B measured |
| LMArena Vision: diagrams | 1156 | Not measured | Only Gemma 3 27B measured |
| LMArena Vision: homework | 1160 | 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 index02
| Benchmark | Gemma 3 27B | GPT-5.1-Codex-Mini | Leader |
|---|---|---|---|
| AA Math Index | 20.7 | 91.7 | GPT-5.1-Codex-Mini |
| AIME 2025 (Artificial Analysis run) | 20.7% | 91.7% | GPT-5.1-Codex-Mini |
| 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 | Not measured | Only Gemma 3 27B measured |
| LMArena Text: mathematical work | 1327 | Not measured | Only Gemma 3 27B measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Gemma 3 27B | GPT-5.1-Codex-Mini | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 66.9% | 82.0% | GPT-5.1-Codex-Mini |
| GeoBench | 52.0% | Not measured | Only Gemma 3 27B measured |
| LMArena Text: science | 1383 | Not measured | Only Gemma 3 27B measured |
| LMArena Text: medicine and healthcare | 1391 | Not measured | Only Gemma 3 27B measured |
| LMArena Text: legal and government | 1375 | Not measured | Only Gemma 3 27B measured |
| LMArena Text: business and finance | 1360 | Not measured | Only Gemma 3 27B measured |
| Neither model has a result on the other 11 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | Gemma 3 27B | GPT-5.1-Codex-Mini | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1111 | Only GPT-5.1-Codex-Mini measured |
| Design Arena: UI components | Not measured | 1081 | Only GPT-5.1-Codex-Mini measured |
| Design Arena: game dev | Not measured | 1093 | Only GPT-5.1-Codex-Mini measured |
| Design Arena: data viz | Not measured | 1097 | Only GPT-5.1-Codex-Mini measured |
| Design Arena: 3D | Not measured | 988 | Only GPT-5.1-Codex-Mini measured |
| Design Arena: all code categories | Not measured | 1094 | Only GPT-5.1-Codex-Mini measured |
| Design Arena: SVG | Not measured | 977 | Only GPT-5.1-Codex-Mini measured |
| Design Arena: ASCII art | Not measured | 1104 | Only GPT-5.1-Codex-Mini measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Gemma 3 27B | GPT-5.1-Codex-Mini | Leader |
|---|---|---|---|
| IFBench | 31.8% | 67.9% | GPT-5.1-Codex-Mini |
| Long context reasoning (AA-LCR) | 7.3% | 66.7% | GPT-5.1-Codex-Mini |
| 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 | Not measured | Only Gemma 3 27B measured |
| LMArena Text: instruction following | 1321 | Not measured | Only Gemma 3 27B measured |
| LMArena Text: creative writing | 1345 | Not measured | Only Gemma 3 27B measured |
| LMArena Vision | 1165 | Not measured | Only Gemma 3 27B measured |
| LMArena Text: English | 1377 | Not measured | Only Gemma 3 27B measured |
| LMArena Text: non-English | 1334 | Not measured | Only Gemma 3 27B measured |
| LMArena Text: Chinese | 1346 | Not measured | Only Gemma 3 27B measured |
| LMArena Text: German | 1362 | Not measured | Only Gemma 3 27B measured |
| 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 | Not measured | Only Gemma 3 27B measured |
| LMArena Text: Polish | 1345 | Not measured | Only Gemma 3 27B measured |
| LMArena Text: multi-turn | 1345 | Not measured | Only Gemma 3 27B measured |
| LMArena Text: longer queries | 1333 | Not measured | Only Gemma 3 27B measured |
| LMArena Text: writing and language | 1347 | Not measured | Only Gemma 3 27B measured |
| LMArena Text: entertainment and media | 1320 | Not measured | Only Gemma 3 27B measured |
| 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 | GPT-5.1-Codex-Mini | 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-5.1-Codex-Mini |
|---|---|---|
| Maker | OpenAI | |
| Weights | Open weights | No public weights listed |
| Released | March 12, 2025 | November 13, 2025 |
| Context window | 131K | 400K |
| Input $/Mtok | $0.08 | $0.25 |
| Output $/Mtok | $0.35 | $2.00 |
| Coding index | 4.9 | 6.2 |
| Independent measurements behind it | 1 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Gemma 3 27B better than GPT-5.1-Codex-Mini for coding?
Gemma 3 27B and GPT-5.1-Codex-Mini are level for coding, 4.9 against 6.2 on the Vibe Coding Index, so price and context decide. Pick Gemma 3 27B when output tokens cost 5.7x less. Pick GPT-5.1-Codex-Mini when the context window is larger, 400K against 131K tokens, or when what matters is LiveCodeBench (Artificial Analysis run) or Terminal-Bench Hard (Artificial Analysis run), where it leads.
Which is cheaper, Gemma 3 27B or GPT-5.1-Codex-Mini?
Gemma 3 27B costs $0.08 per million input tokens and $0.35 per million output tokens; GPT-5.1-Codex-Mini costs $0.25 and $2.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Gemma 3 27B or GPT-5.1-Codex-Mini?
GPT-5.1-Codex-Mini, with 400K tokens against 131K.
How many benchmarks is the Gemma 3 27B vs GPT-5.1-Codex-Mini comparison based on?
11 benchmarks have a result for both models as of October 21, 2026. Gemma 3 27B's coding index rests on 1 independent benchmark and GPT-5.1-Codex-Mini's on 1. Where only one model has a result, the page says so instead of naming a winner.