What is the difference between Claude 3.7 Thinking Sonnet and OpenAI o3?

Claude 3.7 Thinking Sonnet is developed by Anthropic while OpenAI o3 is developed by OpenAI. in 41 community votes on Rival, Claude 3.7 Thinking Sonnet wins 79% of head-to-head matchups. These results are based on blind head-to-head voting across 46 challenges.

Which is better, Claude 3.7 Thinking Sonnet or OpenAI o3?

Based on 41 community votes on Rival, Claude 3.7 Thinking Sonnet wins 79% of head-to-head matchups against OpenAI o3. Claude 3.7 Thinking Sonnet is strongest in Web Design, Image Generation, Reasoning.

How much does Claude 3.7 Thinking Sonnet cost compared to OpenAI o3?

Claude 3.7 Thinking Sonnet costs $6/M input tokens and OpenAI o3 costs $10/M input tokens. Claude 3.7 Thinking Sonnet is $4.00/M cheaper per input. Claude 3.7 Thinking Sonnet also wins more often in community votes, making it the better value.

How are Claude 3.7 Thinking Sonnet vs OpenAI o3 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, eliminating brand bias. 41 votes have been collected for this pair across 46 challenges. All vote data is part of Rival's open dataset.

Rival

Updated Apr 16, 2025

Claude 3.7 Thinking SonnetvsOpenAI o3

41 fights queued

Why Claude 3.7 Thinking Sonnet?

1.4x cheaper overall$6.00/M in · $30.00/M out

Why OpenAI o3?

2 months newerApr 2025

Claude 3.7 Thinking SonnetOpenAI o3

Input price

$6.00/M

$10.00/M

Output price

$30.00/M

$40.00/M

Context

200K

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Released

Feb 2025

Apr 2025

Favorites

Claude 3.7 Thinking SonnetOpenAI o3

Movie

The Shawshank Redemption

19948.7

Blade Runner

19827.9

Album

Kind of Blue

Miles Davis

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Book

Frankenstein or The Modern Prometheus

Mary Shelley

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City

Amsterdam

Netherlands

Kyoto

Japan

Game

Portal 2

Shooter, Puzzle4.6

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12 / 41

Estimate Complexityreasoning

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

Claude 3.7 Thinking Sonnet

Anthropic · Feb 2025text

Nothing here. The model returned empty. We stared at it for a while.

Try this prompt

OpenAI o3

Openai · Apr 2025text

Below is a simple "back-of-the-envelope" estimate that uses only a few public facts about GPT-3 and very generic rules of thumb for counting operations.

Step 1. Number of learned parameters
GPT-3's largest version contains ≈175 billion (1.75 × 10¹¹) trainable parameters.

Step 2. How much data was seen during training
OpenAI reports that the model was trained on about 300 billion tokens (3 × 10¹¹).

Step 3. How many floating-point operations does one parameter cost per token?
A practical rule of thumb for Transformer language models is
Training FLOPs ≈ 6 × (# parameters) × (# training tokens)

Where does the factor 6 come from?

• Forward pass: every parameter is involved in one multiply-accumulate → 2 FLOPs
• Back-prop gradient computation: roughly another 2 FLOPs
• Parameter-update (optimizer) and miscellaneous overhead: ≈2 FLOPs
Total ≈6 FLOPs per parameter per token.

Step 4. Plug in the numbers
Total FLOPs = 6 × (1.75 × 10¹¹) × (3 × 10¹¹)
= 6 × 5.25 × 10²²
≈ 3.15 × 10²³ FLOPs.

Step 5. Express the answer in more familiar units
3.15 × 10²³ ≈ 315 zettaFLOPs (1 ZFLOP = 10²¹ FLOPs).

Step 6. Sanity check with hardware-time intuition
An NVIDIA A100 delivers ≈ 312 teraFLOP/s (0.312 PFLOP/s) in mixed precision.
Wall-clock time on a single A100:
3.15 × 10²³ / 3.12 × 10¹⁴ ≈ 1.0 × 10⁹ s ≈ 32 years.
Running on 1,000 A100s in parallel would take ≈12 days, which is the right ballpark for a large industrial training run.

Final estimate
Training GPT-3 (175 B parameters, 300 B tokens) requires on the order of 3 × 10²³ floating-point operations, i.e. roughly 300 zettaFLOPs.

Try this prompt

Vote

2 / 41

AI Board Game Logicreasoning

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

Claude 3.7 Thinking Sonnet

Anthropic · Feb 2025svg

Nothing here. The model returned empty. We stared at it for a while.

Try this prompt

OpenAI o3

Openai · Apr 2025svg

Try this prompt

Vote

11 / 41

Character Voice Testconversation

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

Claude 3.7 Thinking Sonnet

Anthropic · Feb 2025text

Nothing here. The model returned empty. We stared at it for a while.

Try this prompt

OpenAI o3

Openai · Apr 2025text

Nothing here. The model returned empty. We stared at it for a while.

Try this prompt

Vote

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Our Verdict

Claude 3.7 Thinking SonnetWinner

OpenAI o3Runner-up

Pick Claude 3.7 Thinking Sonnet. In 41 blind votes, Claude 3.7 Thinking Sonnet wins 79% of the time. That's not luck.

Claude 3.7 Thinking Sonnet particularly excels in Conversation, Image Generation, Reasoning.

Clear winner

Ask them anything yourself

FAQ

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Below is a simple "back-of-the-envelope" estimate that uses only a few public facts about GPT-3 and very generic rules of thumb for counting operations.

Step 1. Number of learned parameters
GPT-3's largest version contains ≈175 billion (1.75 × 10¹¹) trainable parameters.

Step 2. How much data was seen during training
OpenAI reports that the model was trained on about 300 billion tokens (3 × 10¹¹).

Where does the factor 6 come from?

Step 4. Plug in the numbers
Total FLOPs = 6 × (1.75 × 10¹¹) × (3 × 10¹¹)
= 6 × 5.25 × 10²²
≈ 3.15 × 10²³ FLOPs.

Step 5. Express the answer in more familiar units
3.15 × 10²³ ≈ 315 zettaFLOPs (1 ZFLOP = 10²¹ FLOPs).

Final estimate
Training GPT-3 (175 B parameters, 300 B tokens) requires on the order of 3 × 10²³ floating-point operations, i.e. roughly 300 zettaFLOPs.

Claude 3.7 Thinking Sonnet vs OpenAI o3

Which is better, Claude 3.7 Thinking Sonnet or OpenAI o3?