Model comparison
Gemma 3 27B vs GPT-5.3 Codex
GPT-5.3 Codex is the stronger model overall, scoring 45.5 to 30.5 on the Noometry Index. Gemma 3 27B costs 48× less per token, which makes it the better buy when GPT-5.3 Codex's lead doesn't matter for your workload.
Last verified . 1 shared benchmarks.
GoogleSummary
- They share 1 benchmark with published results for both. Gemma 3 27B scores higher in 0 categories and GPT-5.3 Codex in 2 categories; 2 gaps are clear of the uncertainty.
- The widest gap is in coding, where GPT-5.3 Codex leads 48.5 to 22.1.
- Gemma 3 27B is cheaper at $0.08 / $0.16 per million input/output tokens, against $1.75 / $14 for GPT-5.3 Codex.
- GPT-5.3 Codex accepts more context: 400K tokens versus 131K.
- Gemma 3 27B has downloadable open weights; the other is API-only.
Side by side
| Gemma 3 27B | GPT-5.3 Codex | |
|---|---|---|
| Provider | OpenAI | |
| Noometry Index | 30.5 | 45.5 |
| Released | 2025-03-11 | 2026-02-05 |
| Weights | Open | Proprietary |
| Context window | 131K | 400K |
| Max output | 8K | 128K |
| Input $ / M tokens | $0.08 | $1.75 |
| Output $ / M tokens | $0.16 | $14 |
| Results tracked | 43 | 8 |
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Category by category
Coding GPT-5.3 Codex leads
Gemma 3 27B: 22.1 (#329), GPT-5.3 Codex: 48.5 (#51)
| Benchmark | Gemma 3 27B | GPT-5.3 Codex |
|---|---|---|
| SWE-bench Verified | — | 74.8% |
| Aider Polyglot | 4.9% | — |
| LMArena WebDev | — | 1408 |
| SciCode | 21.2% | — |
| WeirdML | — | 79.3% |
| LiveBench Coding | 39.9% | — |
| LMArena Coding | 1322 | — |
| ALE-Bench | — | 1,655 |
Agentic & Tool Use GPT-5.3 Codex leads
Gemma 3 27B: 24.9 (#105), GPT-5.3 Codex: 47.8 (#9)
| Benchmark | Gemma 3 27B | GPT-5.3 Codex |
|---|---|---|
| Terminal-Bench | — | 78.4% |
| Berkeley Function Calling Leaderboard | 29.5% | — |
| METR Time Horizons | — | 74.5% |
| Vending-Bench 2 | — | 5,940 |
Reasoning Not comparable
Gemma 3 27B: 16.3 (#297), GPT-5.3 Codex: —
| Benchmark | Gemma 3 27B | GPT-5.3 Codex |
|---|---|---|
| Epoch Capabilities Index | 130.04 | 156.84 |
| Kagi LLM Benchmark | 40.4% | — |
| CritPt | 0% | — |
| Chess Puzzles | 0% | — |
| LiveBench Reasoning | 43.8% | — |
| LMArena Hard Prompts | 1340 | — |
| DTBench | 52.5% | — |
| LiveBench Data Analysis | 51.5% | — |
| LMCA | 12.3% | — |
| LiveBench | 50% | — |
Math Not comparable
Gemma 3 27B: 24.4 (#261), GPT-5.3 Codex: —
| Benchmark | Gemma 3 27B | GPT-5.3 Codex |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 22.5% | — |
| LiveBench Math | 55.4% | — |
| LMArena Math | 1312 | — |
| MATH Level 5 | 74% | — |
Knowledge Not comparable
Gemma 3 27B: 25.4 (#256), GPT-5.3 Codex: —
| Benchmark | Gemma 3 27B | GPT-5.3 Codex |
|---|---|---|
| GPQA Diamond | 47.7% | — |
| Confabulations | 40.3% | — |
| Vectara Hallucination Rate | 7.4% | — |
| LMArena Expert | 1303 | — |
Multimodal Not comparable
Gemma 3 27B: 32.0 (#94), GPT-5.3 Codex: —
| Benchmark | Gemma 3 27B | GPT-5.3 Codex |
|---|---|---|
| LMArena Vision | 1165 | — |
| GeoBench | 52% | — |
Multilingual Not comparable
Gemma 3 27B: 47.0 (#150), GPT-5.3 Codex: —
| Benchmark | Gemma 3 27B | GPT-5.3 Codex |
|---|---|---|
| LMArena Non-English | 1334 | — |
| LMArena Chinese | 1347 | — |
| LMArena French | 1368 | — |
| LMArena German | 1362 | — |
| LMArena Japanese | 1287 | — |
| LMArena Korean | 1308 | — |
| LMArena Russian | 1349 | — |
| LMArena Spanish | 1348 | — |
Instruction Following Not comparable
Gemma 3 27B: 70.8 (#155), GPT-5.3 Codex: —
| Benchmark | Gemma 3 27B | GPT-5.3 Codex |
|---|---|---|
| LiveBench Instruction Following | 74.9% | — |
| LMArena Instruction Following | 1321 | — |
Long Context Not comparable
Gemma 3 27B: 27.6 (#288), GPT-5.3 Codex: —
| Benchmark | Gemma 3 27B | GPT-5.3 Codex |
|---|---|---|
| Fiction.LiveBench | 33.3% | — |
| LMArena Longer Query | 1333 | — |
Writing & Preference Not comparable
Gemma 3 27B: 52.7 (#161), GPT-5.3 Codex: —
| Benchmark | Gemma 3 27B | GPT-5.3 Codex |
|---|---|---|
| LMArena Text | 1358 | — |
| LMArena Creative Writing | 1346 | — |
| Short-Story Creative Writing | 79.9% | — |
| EQ-Bench Creative Writing | 1266 | — |
| LMArena Multi-Turn | 1344 | — |
| LiveBench Language | 34.6% | — |
Frequently asked questions
Is Gemma 3 27B better than GPT-5.3 Codex?
GPT-5.3 Codex is the stronger model overall, scoring 45.5 to 30.5 on the Noometry Index. Gemma 3 27B costs 48× less per token, which makes it the better buy when GPT-5.3 Codex's lead doesn't matter for your workload.
Which is cheaper, Gemma 3 27B or GPT-5.3 Codex?
Gemma 3 27B is cheaper. It lists at $0.08 per million input tokens and $0.16 per million output tokens; GPT-5.3 Codex lists at $1.75 and $14.
Is Gemma 3 27B or GPT-5.3 Codex better for coding?
GPT-5.3 Codex scores higher on coding benchmarks: 48.5 versus 22.1 in the Noometry coding category.
Which has the bigger context window?
GPT-5.3 Codex does, with 400K tokens against 131K.
How many benchmarks do Gemma 3 27B and GPT-5.3 Codex share?
1 benchmark has published results for both models. Gemma 3 27B has 43 scored results on Noometry and GPT-5.3 Codex has 8.