Gemma 3 27B vs GPT-5.1-Codex-Max
Gemma 3 27B costs 33.3x less per output token than GPT-5.1-Codex-Max ($0.30 vs $10.00 per million). Updated October 21, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- 4.9
- Output $/M
- $0.30
- Coding
- No data
- Overall
- No data
- Output $/M
- $10.00
Gemma 3 27B or GPT-5.1-Codex-Max: 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 33.3x cheaper ($0.30 vs $10.00 per million).
No benchmark has tested both yet. Gemma 3 27B has results on Aider polyglot, AA Coding Index and LiveCodeBench (Artificial Analysis run); GPT-5.1-Codex-Max on LiveCodeBench (Vals AI), Vibe Code Bench (Vals AI) and Terminal-Bench. Compare them on price and context instead.
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 27B has a AA Intelligence Index result, 4.9.
Directional only
Agentic work
AA Agentic Index
No call
Only Gemma 3 27B has a AA Agentic Index result, 0.1.
Directional only
Long documents
Listed context window
GPT-5.1-Codex-Max
GPT-5.1-Codex-Max takes 400K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Gemma 3 27B
Gemma 3 27B $0.0002, GPT-5.1-Codex-Max $0.0063 per turn.
List prices
At a glance
- Coding index
- Gemma 3 27B not benchmarked, GPT-5.1-Codex-Max not benchmarked.
- Widest category
- None yet: no category has been tested on both models.
- Widest single gap
- None yet: no benchmark has tested both models.
- Output price
- Gemma 3 27B $0.30 against GPT-5.1-Codex-Max $10.00 per million output tokens, 33.3x apart.
- Context window
- Gemma 3 27B takes 131K tokens, GPT-5.1-Codex-Max 400K.
Benchmark by benchmark
Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.
CodingDirectional onlySix of these boards feed the Vibe Coding Index11 boards measured
| Benchmark | Gemma 3 27B | GPT-5.1-Codex-Max | Leader |
|---|---|---|---|
| Aider polyglot | 4.9% | Not measured | Only Gemma 3 27B measured |
| AA Coding Index | 10.1 | Not measured | Only Gemma 3 27B measured |
| LiveCodeBench (Vals AI) | Not measured | 83.6% | Only GPT-5.1-Codex-Max measured |
| Vibe Code Bench (Vals AI) | Not measured | 22.2% | Only GPT-5.1-Codex-Max measured |
| LiveCodeBench (Artificial Analysis run) | 13.7% | Not measured | Only Gemma 3 27B measured |
| 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 |
| Terminal-Bench | Not measured | 60.4% | Only GPT-5.1-Codex-Max measured |
| ALE-Bench | Not measured | 1228.3 | Only GPT-5.1-Codex-Max 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 |
| Neither model has a result on the other 27 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Gemma 3 27B | GPT-5.1-Codex-Max | Leader |
|---|---|---|---|
| AA Intelligence Index | 4.9 | Not measured | Only Gemma 3 27B measured |
| Epoch Capabilities Index | 130.0 | Not measured | Only Gemma 3 27B measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards measured
| Benchmark | Gemma 3 27B | GPT-5.1-Codex-Max | 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 | 70.8% | Only GPT-5.1-Codex-Max measured |
| Neither model has a result on the other 36 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index8 boards measured
| Benchmark | Gemma 3 27B | GPT-5.1-Codex-Max | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 42.8% | Not measured | Only Gemma 3 27B measured |
| Humanity's Last Exam (Artificial Analysis run) | 4.4% | Not measured | Only Gemma 3 27B measured |
| 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 | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index7 boards measured
| Benchmark | Gemma 3 27B | GPT-5.1-Codex-Max | 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% | Not measured | Only Gemma 3 27B measured |
| ProofBench | Not measured | 9.0% | Only GPT-5.1-Codex-Max 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 6 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index6 boards measured
| Benchmark | Gemma 3 27B | GPT-5.1-Codex-Max | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 66.9% | Not measured | Only Gemma 3 27B measured |
| 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 | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index31 boards measured
| Benchmark | Gemma 3 27B | GPT-5.1-Codex-Max | 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 | 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 | |||
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-Max |
|---|---|---|
| Maker | OpenAI | |
| Weights | Open weights | No public weights listed |
| Released | March 12, 2025 | December 4, 2025 |
| Context window | 131K | 400K |
| Input $/Mtok | $0.08 | $1.25 |
| Output $/Mtok | $0.30 | $10.00 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 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-Max for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Gemma 3 27B for output tokens 33.3x cheaper ($0.30 vs $10.00 per million).
Which is cheaper, Gemma 3 27B or GPT-5.1-Codex-Max?
Gemma 3 27B costs $0.08 per million input tokens and $0.30 per million output tokens; GPT-5.1-Codex-Max costs $1.25 and $10.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Gemma 3 27B or GPT-5.1-Codex-Max?
GPT-5.1-Codex-Max, with 400K tokens against 131K.
Has any benchmark tested both Gemma 3 27B and GPT-5.1-Codex-Max?
No benchmark has tested both yet. Gemma 3 27B has results on Aider polyglot, AA Coding Index and LiveCodeBench (Artificial Analysis run); GPT-5.1-Codex-Max on LiveCodeBench (Vals AI), Vibe Code Bench (Vals AI) and Terminal-Bench. Compare them on price and context instead. Data as of October 21, 2026.