Gemma 2 27B vs GPT-5.3-Codex
Updated October 21, 2026. 1 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Only GPT-5.3-Codex has a coding score, 60.0 on the Vibe Coding Index; Gemma 2 27B has none, so the two cannot be ranked on coding.
Gemma 2 27B has the edge where output tokens cost 21.5x less.
- Shared results
- 1
- Gemma 2 27B only
- 35
- GPT-5.3-Codex only
- 33
- Like-for-like categories
- 1 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, six public coding benchmarks
No call
Only GPT-5.3-Codex has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only GPT-5.3-Codex has a AA Intelligence Index result, 32.5.
Directional only
Web design
Design Arena: websites
No call
Only GPT-5.3-Codex has a Design Arena: websites result, 1163.
Directional only
Long documents
Listed context window
GPT-5.3-Codex
GPT-5.3-Codex takes 400K tokens against 8K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Gemma 2 27B
Gemma 2 27B $0.0010, GPT-5.3-Codex $0.0088 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.3-Codex has the number, $0.59.
Directional only
At a glance
- Coding index
- Gemma 2 27B not benchmarked, GPT-5.3-Codex 60.0 from 3 benchmarks.
- Widest category
- Intelligence, 43.7 points apart on average over 1 shared benchmark.
- Widest single gap
- Epoch Capabilities Index: Gemma 2 27B 122.1 against GPT-5.3-Codex 157.1, 43.7 points apart on a 0 to 100 scale.
- Output price
- Gemma 2 27B $0.65 against GPT-5.3-Codex $14.00 per million output tokens, 21.5x apart.
- Context window
- Gemma 2 27B takes 8K tokens, GPT-5.3-Codex 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 2 27B | Bars | GPT-5.3-Codex | Ties |
|---|---|---|---|---|
| Intelligence | 0 | 1 | ||
| All shared boards | 0 | 1 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index14 boards, none shared
| Benchmark | Gemma 2 27B | GPT-5.3-Codex | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1407 | Only GPT-5.3-Codex measured |
| LiveCodeBench (Vals AI) | Not measured | 87.3% | Only GPT-5.3-Codex measured |
| SWE-bench Verified (Vals AI) | Not measured | 78.0% | Only GPT-5.3-Codex measured |
| Vibe Code Bench (Vals AI) | Not measured | 61.8% | Only GPT-5.3-Codex measured |
| IOI (Vals AI) | Not measured | 53.8% | Only GPT-5.3-Codex measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 53.0% | Only GPT-5.3-Codex measured |
| Terminal-Bench | Not measured | 78.4% | Only GPT-5.3-Codex measured |
| WeirdML | Not measured | 79.3% | Only GPT-5.3-Codex measured |
| ALE-Bench | Not measured | 1655.2 | Only GPT-5.3-Codex measured |
| LMArena Text: coding | 1211 | Not measured | Only Gemma 2 27B measured |
| LMArena Text: software and IT | 1228 | Not measured | Only Gemma 2 27B measured |
| LMArena WebDev: React | Not measured | 1395 | Only GPT-5.3-Codex measured |
| LMArena WebDev: HTML | Not measured | 1422 | Only GPT-5.3-Codex measured |
| LMArena WebDev: from an image | Not measured | 1443 | Only GPT-5.3-Codex measured |
| Neither model has a result on the other 24 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Gemma 2 27B | GPT-5.3-Codex | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 32.5 | Only GPT-5.3-Codex measured |
| Epoch Capabilities Index | 122.1 | 157.1 | GPT-5.3-Codex |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | Gemma 2 27B | GPT-5.3-Codex | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | Not measured | 1032 | Only GPT-5.3-Codex measured |
| Design Arena: agents building full-stack apps | Not measured | 983 | Only GPT-5.3-Codex measured |
| Design Arena: agents building mobile apps | Not measured | 1053 | Only GPT-5.3-Codex measured |
| Design Arena: agents building Android apps | Not measured | 1084 | Only GPT-5.3-Codex measured |
| Design Arena: agents building Godot games | Not measured | 1124 | Only GPT-5.3-Codex measured |
| τ²-Bench Telecom | Not measured | 86.0% | Only GPT-5.3-Codex measured |
| METR time horizons | Not measured | 74.5% | Only GPT-5.3-Codex measured |
| Vending-Bench 2 | Not measured | $5,940 | Only GPT-5.3-Codex measured |
| Neither model has a result on the other 31 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Gemma 2 27B | GPT-5.3-Codex | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 91.5% | Only GPT-5.3-Codex measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 42.5% | Only GPT-5.3-Codex measured |
| GPQA Diamond (Epoch AI run) | 36.5% | Not measured | Only Gemma 2 27B measured |
| LMArena Text: hard prompts | 1198 | Not measured | Only Gemma 2 27B measured |
| LMArena Text: expert | 1171 | Not measured | Only Gemma 2 27B measured |
| LMArena Text: hard prompts in English | 1206 | Not measured | Only Gemma 2 27B measured |
| Neither model has a result on the other 15 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | Gemma 2 27B | GPT-5.3-Codex | Leader |
|---|---|---|---|
| OTIS Mock AIME | 1.4% | Not measured | Only Gemma 2 27B measured |
| MATH level 5 | 27.9% | Not measured | Only Gemma 2 27B measured |
| LMArena Text: math | 1212 | Not measured | Only Gemma 2 27B measured |
| LMArena Text: mathematical work | 1221 | Not measured | Only Gemma 2 27B measured |
| Neither model has a result on the other 9 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Gemma 2 27B | GPT-5.3-Codex | Leader |
|---|---|---|---|
| LegalBench (Vals AI) | 68.0% | Not measured | Only Gemma 2 27B measured |
| MMLU | 75.7% | Not measured | Only Gemma 2 27B measured |
| LMArena Text: science | 1228 | Not measured | Only Gemma 2 27B measured |
| LMArena Text: medicine and healthcare | 1198 | Not measured | Only Gemma 2 27B measured |
| LMArena Text: legal and government | 1241 | Not measured | Only Gemma 2 27B measured |
| LMArena Text: business and finance | 1208 | Not measured | Only Gemma 2 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 2 27B | GPT-5.3-Codex | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1163 | Only GPT-5.3-Codex measured |
| Design Arena: UI components | Not measured | 1137 | Only GPT-5.3-Codex measured |
| Design Arena: game dev | Not measured | 1161 | Only GPT-5.3-Codex measured |
| Design Arena: data viz | Not measured | 1163 | Only GPT-5.3-Codex measured |
| Design Arena: 3D | Not measured | 1008 | Only GPT-5.3-Codex measured |
| Design Arena: all code categories | Not measured | 1145 | Only GPT-5.3-Codex measured |
| Design Arena: SVG | Not measured | 1129 | Only GPT-5.3-Codex measured |
| Design Arena: ASCII art | Not measured | 1151 | Only GPT-5.3-Codex measured |
| Neither model has a result on the other 1 board here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index21 boards, none shared
| Benchmark | Gemma 2 27B | GPT-5.3-Codex | Leader |
|---|---|---|---|
| IFBench | Not measured | 75.4% | Only GPT-5.3-Codex measured |
| Long context reasoning (AA-LCR) | Not measured | 83.3% | Only GPT-5.3-Codex measured |
| DTBench | 48.0% | Not measured | Only Gemma 2 27B measured |
| LMCA | 7.1% | Not measured | Only Gemma 2 27B measured |
| LMArena Text | 1231 | Not measured | Only Gemma 2 27B measured |
| LMArena Text: instruction following | 1206 | Not measured | Only Gemma 2 27B measured |
| LMArena Text: creative writing | 1240 | Not measured | Only Gemma 2 27B measured |
| LMArena Text: English | 1242 | Not measured | Only Gemma 2 27B measured |
| LMArena Text: non-English | 1217 | Not measured | Only Gemma 2 27B measured |
| LMArena Text: Chinese | 1221 | Not measured | Only Gemma 2 27B measured |
| LMArena Text: German | 1209 | Not measured | Only Gemma 2 27B measured |
| LMArena Text: Japanese | 1175 | Not measured | Only Gemma 2 27B measured |
| LMArena Text: Korean | 1174 | Not measured | Only Gemma 2 27B measured |
| LMArena Text: French | 1248 | Not measured | Only Gemma 2 27B measured |
| LMArena Text: Spanish | 1228 | Not measured | Only Gemma 2 27B measured |
| LMArena Text: Russian | 1234 | Not measured | Only Gemma 2 27B measured |
| LMArena Text: Polish | 1277 | Not measured | Only Gemma 2 27B measured |
| LMArena Text: multi-turn | 1224 | Not measured | Only Gemma 2 27B measured |
| LMArena Text: longer queries | 1230 | Not measured | Only Gemma 2 27B measured |
| LMArena Text: writing and language | 1249 | Not measured | Only Gemma 2 27B measured |
| LMArena Text: entertainment and media | 1212 | Not measured | Only Gemma 2 27B measured |
| Neither model has a result on the other 25 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Gemma 2 27B | GPT-5.3-Codex | 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 2 27B costs less
Repository review
50K input + 3K output tokens
Gemma 2 27B costs less
Agent loop
200K input + 10K output tokens
Gemma 2 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 2 27B | OpenAI GPT-5.3-Codex |
|---|---|---|
| Maker | OpenAI | |
| Weights | Open weights | No public weights listed |
| Released | July 13, 2024 | February 24, 2026 |
| Context window | 8K | 400K |
| Input $/Mtok | $0.65 | $1.75 |
| Output $/Mtok | $0.65 | $14.00 |
| Coding index | Not benchmarked | 60.0 |
| Independent measurements behind it | 0 | 3 |
| Cost per solved task | Not computable | $0.59 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Gemma 2 27B better than GPT-5.3-Codex for coding?
Only GPT-5.3-Codex has a coding score, 60.0 on the Vibe Coding Index; Gemma 2 27B has none, so the two cannot be ranked on coding. Gemma 2 27B has the edge where output tokens cost 21.5x less.
Which is cheaper, Gemma 2 27B or GPT-5.3-Codex?
Gemma 2 27B costs $0.65 per million input tokens and $0.65 per million output tokens; GPT-5.3-Codex costs $1.75 and $14.00. Cost per solved task is known for GPT-5.3-Codex only.
Which has the bigger context window, Gemma 2 27B or GPT-5.3-Codex?
GPT-5.3-Codex, with 400K tokens against 8K.
How many benchmarks is the Gemma 2 27B vs GPT-5.3-Codex comparison based on?
1 benchmarks have a result for both models as of October 21, 2026. Gemma 2 27B's coding index rests on 0 independent benchmarks and GPT-5.3-Codex's on 3. Where only one model has a result, the page says so instead of naming a winner.