What is the difference between Claude Haiku 4.5 and GPT-4.5?

Claude Haiku 4.5 is developed by Anthropic while GPT-4.5 is developed by OpenAI. Claude Haiku 4.5 has a 200K token context window vs GPT-4.5's 128K. in 1 community votes on RIVAL, Claude Haiku 4.5 wins 100% of head-to-head matchups. These results are based on blind head-to-head voting across 25 challenges.

Which is better, Claude Haiku 4.5 or GPT-4.5?

Based on 1 community votes on RIVAL, Claude Haiku 4.5 wins 100% of head-to-head matchups against GPT-4.5.

How much does Claude Haiku 4.5 cost compared to GPT-4.5?

Claude Haiku 4.5 costs $1/M input tokens and GPT-4.5 costs $75/M input tokens. Claude Haiku 4.5 is $74.00/M cheaper per input. Claude Haiku 4.5 also wins more often in community votes, making it the better value.

How are Claude Haiku 4.5 vs GPT-4.5 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. 1 votes have been collected for this pair across 25 challenges. All vote data is part of RIVAL's open dataset.

Rival

Claude Haiku 4.5vsGPT-4.5

25 challenges

Why Claude Haiku 4.5?

35.3x cheaper overall$1.00/M in · $5.00/M out

1.6x more context200K

Leads 1 of 1 benchmarks

Stronger on SWE-bench Verified73.3% vs 28.0%

8 months newerOct 2025

Why GPT-4.5?

No standout advantages

Favorites

Claude Haiku 4.5GPT-4.5

Movie

Arrival

20167.6

—

Album

OK Computer

Radiohead

—

Book

Don't Look Now and Other Stories

Daphne du Maurier

—

City

Venice

Italy

—

Game

Portal 2

Shooter, Puzzle4.6

—

12 / 25

Estimate Complexityreasoning

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

Claude Haiku 4.5

Anthropic · Oct 2025text

No content available for this output.

GPT-4.5

Openai · Feb 2025text

Let's estimate the total number of floating-point operations (FLOPs) required to train GPT-3 step by step, using reasonable assumptions and approximations:

Step 1: Identify GPT-3 Parameters and Training Setup

GPT-3 has approximately (175 ext{ billion}) parameters.
Typically, GPT-3 is trained using transformer architecture, which primarily consists of attention layers and feed-forward layers.
Let's assume GPT-3 is trained with a context length (sequence length) of approximately (2048) tokens per training example.
Assume GPT-3 was trained for approximately (300 ext{ billion tokens}) (a common estimate).

Step 2: Estimate FLOPs per Token

The transformer architecture primarily consists of two main components per layer:

Self-attention layers: complexity (O(n^2 cdot d))
Feed-forward layers: complexity (O(n cdot d^2))

Where:

(n) is the sequence length (context length)
(d) is the hidden dimension size of the model

GPT-3 (175B) typically has:

Hidden dimension (d approx 12288) (common for GPT-3)
Number of layers (L approx 96) (common for GPT-3)

Let's verify quickly:

Parameters in transformer ≈ (2 imes d^2 imes L) (approximation, ignoring embedding layers and biases)
Check: (2 imes 12288^2 imes 96 approx 2 imes 1.51 imes 10^8 imes 96 approx 2.9 imes 10^{10}) parameters. This is about 29 billion parameters. GPT-3 has 175 billion parameters, meaning the actual GPT-3 has more complexity (due to embedding layers, attention heads, biases, and output layers). Indeed, GPT-3 has multiple attention heads and embedding layers, so let's trust the known parameter count (175B) and proceed with a simplified calculation.

Step 3: Simplified FLOPs per Token Estimation

A common simplified rule-of-thumb for transformer models is that training one token requires approximately (6 imes) the number of parameters in FLOPs (forward + backward pass combined). This heuristic is widely used in practice.

Thus, for GPT-3 (175B parameters):

FLOPs per token ≈ (6 imes 175 imes 10^9 = 1.05 imes 10^{12}) FLOPs per token.

Step 4: Total FLOPs for Training

Given GPT-3 was trained on approximately (300 ext{ billion tokens}):

[ ext{Total FLOPs} = ( ext{FLOPs per token}) imes ( ext{number of tokens}) ]

[ = (1.05 imes 10^{12}) imes (3.0 imes 10^{11}) ]

[ = 3.15 imes 10^{23} ext{ FLOPs} ]

Step 5: Final Approximation

Thus, the total number of FLOPs required to train GPT-3 is approximately:

[ oxed{3.15 imes 10^{23} ext{ FLOPs}} ]

Summary of Reasoning:

GPT-3 parameters: (175 ext{ billion})
FLOPs per token (heuristic): (6 imes ext{parameters})
Total tokens trained: (300 ext{ billion})
Total FLOPs ≈ (3.15 imes 10^{23})

No content available for this output.

Vote

8 / 25

Minimalist Landing Pageweb design

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

Claude Haiku 4.5

Anthropic · Oct 2025website

GPT-4.5

Openai · Feb 2025website

Vote

9 / 25

Pokémon Battle UI Recreationweb design

Recreate an interactive, nostalgic Pokémon battle UI in a single HTML file.

Claude Haiku 4.5

Anthropic · Oct 2025website

No content available for this output.

GPT-4.5

Openai · Feb 2025website

No content available for this output.

Vote

10 / 25

Mario Level UI Recreationweb design

Recreate an interactive, classic Mario level in a single HTML file.

Claude Haiku 4.5

Anthropic · Oct 2025website

GPT-4.5

Openai · Feb 2025website

Vote

11 / 25

Linear App Cloneweb design

Tests an AI's ability to replicate an existing UI with Tailwind CSS

Claude Haiku 4.5

Anthropic · Oct 2025website

No content available for this output.

GPT-4.5

Openai · Feb 2025website

No content available for this output.

Vote

12 / 25

Framer-Style Animationweb design

Tests an AI's ability to create smooth web animations

Claude Haiku 4.5

Anthropic · Oct 2025website

No content available for this output.

GPT-4.5

Openai · Feb 2025website

No content available for this output.

Vote

12 of 25

FAQ

Continue exploring

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Claude Haiku 4.5 vs GPT-5Cross-provider

GPT-4.5 vs MiniMax M2.5New provider

Let's estimate the total number of floating-point operations (FLOPs) required to train GPT-3 step by step, using reasonable assumptions and approximations:

Step 1: Identify GPT-3 Parameters and Training Setup

GPT-3 has approximately (175 ext{ billion}) parameters.
Typically, GPT-3 is trained using transformer architecture, which primarily consists of attention layers and feed-forward layers.
Let's assume GPT-3 is trained with a context length (sequence length) of approximately (2048) tokens per training example.
Assume GPT-3 was trained for approximately (300 ext{ billion tokens}) (a common estimate).

Step 2: Estimate FLOPs per Token

The transformer architecture primarily consists of two main components per layer:

Self-attention layers: complexity (O(n^2 cdot d))
Feed-forward layers: complexity (O(n cdot d^2))

Where:

(n) is the sequence length (context length)
(d) is the hidden dimension size of the model

GPT-3 (175B) typically has:

Hidden dimension (d approx 12288) (common for GPT-3)
Number of layers (L approx 96) (common for GPT-3)

Let's verify quickly:

Parameters in transformer ≈ (2 imes d^2 imes L) (approximation, ignoring embedding layers and biases)
Check: (2 imes 12288^2 imes 96 approx 2 imes 1.51 imes 10^8 imes 96 approx 2.9 imes 10^{10}) parameters. This is about 29 billion parameters. GPT-3 has 175 billion parameters, meaning the actual GPT-3 has more complexity (due to embedding layers, attention heads, biases, and output layers). Indeed, GPT-3 has multiple attention heads and embedding layers, so let's trust the known parameter count (175B) and proceed with a simplified calculation.

Step 3: Simplified FLOPs per Token Estimation

Thus, for GPT-3 (175B parameters):

FLOPs per token ≈ (6 imes 175 imes 10^9 = 1.05 imes 10^{12}) FLOPs per token.

Step 4: Total FLOPs for Training

Given GPT-3 was trained on approximately (300 ext{ billion tokens}):

[ ext{Total FLOPs} = ( ext{FLOPs per token}) imes ( ext{number of tokens}) ]

[ = (1.05 imes 10^{12}) imes (3.0 imes 10^{11}) ]

[ = 3.15 imes 10^{23} ext{ FLOPs} ]

Step 5: Final Approximation

Thus, the total number of FLOPs required to train GPT-3 is approximately:

[ oxed{3.15 imes 10^{23} ext{ FLOPs}} ]

Summary of Reasoning:

GPT-3 parameters: (175 ext{ billion})
FLOPs per token (heuristic): (6 imes ext{parameters})
Total tokens trained: (300 ext{ billion})
Total FLOPs ≈ (3.15 imes 10^{23})

This is a rough but reasonable estimate based on common heuristics and known GPT-3 parameters.