What is the difference between Gemma 3 12B and GPT-5?

Gemma 3 12B is developed by Google AI while GPT-5 is developed by OpenAI. in 11 community votes on RIVAL, Gemma 3 12B wins 60% of head-to-head matchups. These results are based on blind head-to-head voting across 44 challenges.

Which is better, Gemma 3 12B or GPT-5?

Based on 11 community votes on RIVAL, Gemma 3 12B wins 60% of head-to-head matchups against GPT-5.

How much does Gemma 3 12B cost compared to GPT-5?

Gemma 3 12B costs $0.03/M input tokens and GPT-5 costs $1.25/M input tokens. Gemma 3 12B is $1.22/M cheaper per input. Gemma 3 12B also wins more often in community votes, making it the better value.

How are Gemma 3 12B vs GPT-5 votes collected on RIVAL?

RIVAL presents both models' outputs side-by-side in blind duels — voters see the responses but don't know which model produced each one. This eliminates brand bias. 11 votes have been collected for this pair across 44 challenges. All vote data is part of RIVAL's open dataset.

Rival

Gemma 3 12BvsGPT-5

44 challenges

Why Gemma 3 12B?

216.7x cheaper overall$0.03/M in · $0.03/M out

Why GPT-5?

5 months newerAug 2025

Favorites

Gemma 3 12BGPT-5

Movie

Blade Runner

19827.9

—

Album

OK Computer

Radiohead

—

Book

Dune

Frank Herbert

—

City

Kyoto

Japan

—

Game

TheLegendofZeldaOcarinaofTime

—

12 / 44

Estimate Complexityreasoning

Tests an AI's ability to make educated estimates based on technical knowledge

Gemma 3 12B

Google · Mar 2025text

No content available for this output.

GPT-5

Openai · Aug 2025text

Here’s a back‑of‑the‑envelope estimate using standard LM training-compute heuristics.

Assumptions

Model size (N): GPT-3 has about 175 billion parameters (N ≈ 1.75 × 10^11).
Training tokens (T): GPT-3 was trained on roughly 300 billion tokens (T ≈ 3.0 × 10^11).
FLOPs per token per parameter: A common rule of thumb for transformer LM training is FLOPs ≈ 6 × N × T The factor ~6 comes from counting forward and backward passes for the parameter-heavy matmuls (roughly 2N for forward + 4N for backward). It ignores small terms from layernorms, softmax, embeddings, and optimizer updates (which we’ll account for with a small overhead factor later).

Core calculation

6 × N × T = 6 × (1.75 × 10^11) × (3.0 × 10^11)
First multiply N × T: 1.75 × 3.0 = 5.25; powers: 10^11 × 10^11 = 10^22 So N × T = 5.25 × 10^22
Now multiply by 6: 6 × 5.25 × 10^22 = 31.5 × 10^22 = 3.15 × 10^23 FLOPs

Overheads and uncertainty

Non-matmul ops (attention softmax, masking, residuals, layernorms), embeddings, and optimizer update steps add some overhead. A 10–20% uplift is a reasonable allowance.
Applying +10–20%: 3.15 × 10^23 × (1.1 to 1.2) ≈ (3.5 ± 0.3) × 10^23 FLOPs.

Answer

Point estimate: ~3.15 × 10^23 FLOPs
With a modest overhead allowance: roughly (3.3–3.8) × 10^23 FLOPs

This is a coarse estimate; the exact number depends on details like sequence length, activation checkpointing, implementation efficiency, and the precise token count.

Vote

2 / 44

AI Board Game Logicreasoning

Tests an AI's ability to understand game rules and strategy

Gemma 3 12B

Google · Mar 2025svg

No content available for this output.

GPT-5

Openai · Aug 2025svg

Vote

3 / 44

Logic Puzzlereasoning

Tests an AI's ability to solve a simple but potentially confusing logic puzzle

Gemma 3 12B

Google · Mar 2025text

No content available for this output.

GPT-5

Openai · Aug 2025text

Reason: If each of the 3 brothers has 2 sisters, there are exactly 2 girls in the family. One is Sally, so she has 1 sister.

Vote

4 / 44

Math Misconception Testreasoning

Tests an AI's understanding of number representation

Gemma 3 12B

Google · Mar 2025text

No content available for this output.

GPT-5

Openai · Aug 2025text

No content available for this output.

Vote

5 / 44

Stochastic Consistency Testanalysis

Tests an AI's randomness and creativity

Gemma 3 12B

Google · Mar 2025text

No content available for this output.

GPT-5

Openai · Aug 2025text

No content available for this output.

Vote

6 / 44

SVG Layout Challengeimage generation

Tests an AI's ability to generate vector graphics

Gemma 3 12B

Google · Mar 2025svg

No content available for this output.

GPT-5

Openai · Aug 2025svg

No content available for this output.

Vote

7 / 44

Xbox Controller SVG Artimage generation

Tests an AI's ability to create detailed SVG illustrations of gaming hardware

Gemma 3 12B

Google · Mar 2025svg

No content available for this output.

GPT-5

Openai · Aug 2025svg

No content available for this output.

Vote

8 / 44

Generate a Stand-Up Routineconversation

Tests an AI's humor and creative writing ability

Gemma 3 12B

Google · Mar 2025text

No content available for this output.

GPT-5

Openai · Aug 2025text

No content available for this output.

Vote

9 / 44

Realistic AI Interviewconversation

Tests an AI's ability to simulate personalities and predict future trends

Gemma 3 12B

Google · Mar 2025text

No content available for this output.

GPT-5

Openai · Aug 2025text

No content available for this output.

Vote

10 / 44

Satirical Fake News Headlineconversation

Tests an AI's humor and understanding of current events

Gemma 3 12B

Google · Mar 2025text

No content available for this output.

GPT-5

Openai · Aug 2025text

No content available for this output.

Vote

11 / 44

Character Voice Testconversation

Tests an AI's ability to write in distinct character voices

Gemma 3 12B

Google · Mar 2025text

No content available for this output.

GPT-5

Openai · Aug 2025text

No content available for this output.

Vote

12 / 44

Minimalist Landing Pageweb design

Tests an AI's ability to generate a complete, working landing page

Gemma 3 12B

Google · Mar 2025website

GPT-5

Openai · Aug 2025website

Vote

12 of 44

FAQ

Continue exploring

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Gemma 3 12B vs Claude Opus 4New provider

Here’s a back‑of‑the‑envelope estimate using standard LM training-compute heuristics.

Assumptions

Model size (N): GPT-3 has about 175 billion parameters (N ≈ 1.75 × 10^11).
Training tokens (T): GPT-3 was trained on roughly 300 billion tokens (T ≈ 3.0 × 10^11).
FLOPs per token per parameter: A common rule of thumb for transformer LM training is FLOPs ≈ 6 × N × T The factor ~6 comes from counting forward and backward passes for the parameter-heavy matmuls (roughly 2N for forward + 4N for backward). It ignores small terms from layernorms, softmax, embeddings, and optimizer updates (which we’ll account for with a small overhead factor later).

Core calculation

6 × N × T = 6 × (1.75 × 10^11) × (3.0 × 10^11)
First multiply N × T: 1.75 × 3.0 = 5.25; powers: 10^11 × 10^11 = 10^22 So N × T = 5.25 × 10^22
Now multiply by 6: 6 × 5.25 × 10^22 = 31.5 × 10^22 = 3.15 × 10^23 FLOPs

Overheads and uncertainty

Non-matmul ops (attention softmax, masking, residuals, layernorms), embeddings, and optimizer update steps add some overhead. A 10–20% uplift is a reasonable allowance.
Applying +10–20%: 3.15 × 10^23 × (1.1 to 1.2) ≈ (3.5 ± 0.3) × 10^23 FLOPs.

Answer

Point estimate: ~3.15 × 10^23 FLOPs
With a modest overhead allowance: roughly (3.3–3.8) × 10^23 FLOPs

This is a coarse estimate; the exact number depends on details like sequence length, activation checkpointing, implementation efficiency, and the precise token count.