What is the difference between Claude 3.7 Thinking Sonnet and GPT-4o (Omni)?

Claude 3.7 Thinking Sonnet is developed by Anthropic while GPT-4o (Omni) is developed by OpenAI. Claude 3.7 Thinking Sonnet has a 200K token context window vs GPT-4o (Omni)'s 128K. in 5 community votes on Rival, Claude 3.7 Thinking Sonnet wins 100% of head-to-head matchups. These results are based on blind head-to-head voting across 47 challenges.

Which is better, Claude 3.7 Thinking Sonnet or GPT-4o (Omni)?

Based on 5 community votes on Rival, Claude 3.7 Thinking Sonnet wins 100% of head-to-head matchups against GPT-4o (Omni). Claude 3.7 Thinking Sonnet is strongest in Image Generation.

How much does Claude 3.7 Thinking Sonnet cost compared to GPT-4o (Omni)?

Claude 3.7 Thinking Sonnet costs $6/M input tokens and GPT-4o (Omni) costs $2.5/M input tokens. GPT-4o (Omni) is $3.50/M cheaper per input. The more expensive model wins 100% of duels, so the premium may be justified by quality.

How are Claude 3.7 Thinking Sonnet vs GPT-4o (Omni) 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. 5 votes have been collected for this pair across 47 challenges. All vote data is part of Rival's open dataset.

Rival

Updated Feb 26, 2025

Claude 3.7 Thinking SonnetvsGPT-4o (Omni)

42 fights queued

Why Claude 3.7 Thinking Sonnet?

1.6x more context200K

Stronger on MATH96.2% vs 75.9%

10 months newerFeb 2025

Why GPT-4o (Omni)?

2.9x cheaper overall$2.50/M in · $10.00/M out

Stronger on MMLU88.7% vs 77.1%

Claude 3.7 Thinking SonnetGPT-4o (Omni)

Input price

$6.00/M

$2.50/M

Output price

$30.00/M

$10.00/M

Context

200K

128K

Released

Feb 2025

May 2024

Benchmarks2 common

MMLU

77.1%

88.7%+13.1%

MATH

96.2%+21.1%

75.9%

Favorites

Claude 3.7 Thinking SonnetGPT-4o (Omni)

Movie

The Shawshank Redemption

19948.7

Inception

20108.4

Album

Kind of Blue

Miles Davis

Dark Side Of The Moon

suisside

Book

Frankenstein or The Modern Prometheus

Mary Shelley

To Kill a Mockingbird

Harper Lee

City

Amsterdam

Netherlands

Kyoto

Japan

Game

Portal 2

Shooter, Puzzle4.6

The Legend of Zelda: Breath of the Wild

Adventure, Action4.5

12 / 42

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

GPT-4o (Omni)

Openai · May 2024text

To estimate the total number of FLOPs required to train GPT-3, let's break it down into key components.

1. Model Size and Parameters

GPT-3 has 175 billion parameters.

2. FLOPs per Forward and Backward Pass

In deep learning, the FLOPs required for a forward pass and backward pass are estimated as:

Forward pass: ( 2 imes ) the number of parameters
Backward pass: ( 2 imes ) the FLOPs of the forward pass

Thus, total FLOPs per training step:

[ ext{Total FLOPs per step} = 6 imes ext{number of parameters} imes ext{tokens per batch} ]

For GPT-3, let's assume 175B parameters and a batch size of 2048 tokens (common for large-scale training).

[ ext{FLOPs per training step} = 6 imes 175 imes 10^9 imes 2048 ]

[ = 2.15 imes 10^{15} ext{ FLOPs per step} ]

3. Total Training Steps

GPT-3 was trained with 300 billion tokens (as per OpenAI's paper).

If the batch size is 2048 tokens, the total number of steps is:

[ ext{Total steps} = rac{300 imes 10^9}{2048} approx 1.46 imes 10^8 ]

4. Total FLOPs Required

Multiplying the FLOPs per step by the total number of steps:

[ ext{Total FLOPs} = (2.15 imes 10^{15}) imes (1.46 imes 10^8) ]

[ approx 3.14 imes 10^{23} ext{ FLOPs} ]

Final Answer:

The estimated total number of FLOPs required to train GPT-3 is about ( 3.14 imes 10^{23} ) floating point operations.

Try this prompt

Vote

2 / 42

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

GPT-4o (Omni)

Openai · May 2024svg

Try this prompt

Vote

11 / 42

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

GPT-4o (Omni)

Openai · May 2024text

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

GPT-4o (Omni)Runner-up

Pick Claude 3.7 Thinking Sonnet. In 5 blind votes, Claude 3.7 Thinking Sonnet wins 100% of the time. That's not luck.

Claude 3.7 Thinking Sonnet particularly excels in Image Generation. GPT-4o (Omni) is 3.0x cheaper per token — worth considering if cost matters.

Clear winner

Ask them anything yourself

FAQ

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Claude 3.7 Thinking Sonnet vs Llama 4 MaverickNew provider

Claude 3.7 Thinking Sonnet vs GPT-5Cross-provider

Claude 3.7 Thinking Sonnet vs MiniMax M2.5New provider

To estimate the total number of FLOPs required to train GPT-3, let's break it down into key components.

1. Model Size and Parameters

GPT-3 has 175 billion parameters.

2. FLOPs per Forward and Backward Pass

In deep learning, the FLOPs required for a forward pass and backward pass are estimated as:

Forward pass: ( 2 imes ) the number of parameters
Backward pass: ( 2 imes ) the FLOPs of the forward pass

Thus, total FLOPs per training step:

[ ext{Total FLOPs per step} = 6 imes ext{number of parameters} imes ext{tokens per batch} ]

For GPT-3, let's assume 175B parameters and a batch size of 2048 tokens (common for large-scale training).

[ ext{FLOPs per training step} = 6 imes 175 imes 10^9 imes 2048 ]

[ = 2.15 imes 10^{15} ext{ FLOPs per step} ]

3. Total Training Steps

GPT-3 was trained with 300 billion tokens (as per OpenAI's paper).

If the batch size is 2048 tokens, the total number of steps is:

[ ext{Total steps} = rac{300 imes 10^9}{2048} approx 1.46 imes 10^8 ]

4. Total FLOPs Required

Multiplying the FLOPs per step by the total number of steps:

[ ext{Total FLOPs} = (2.15 imes 10^{15}) imes (1.46 imes 10^8) ]

[ approx 3.14 imes 10^{23} ext{ FLOPs} ]

Final Answer:

The estimated total number of FLOPs required to train GPT-3 is about ( 3.14 imes 10^{23} ) floating point operations.

Claude 3.7 Thinking Sonnet vs GPT-4o (Omni)

Which is better, Claude 3.7 Thinking Sonnet or GPT-4o (Omni)?