What is the difference between Z.AI: GLM 4.5 and GPT-5?

Z.AI: GLM 4.5 is developed by Zhipu AI while GPT-5 is developed by OpenAI. Z.AI: GLM 4.5 has a 128K token context window vs GPT-5's 400K. in 19 community votes on RIVAL, GPT-5 wins 82% of head-to-head matchups. These results are based on blind head-to-head voting across 43 challenges.

Which is better, Z.AI: GLM 4.5 or GPT-5?

Based on 19 community votes on RIVAL, GPT-5 wins 82% of head-to-head matchups against Z.AI: GLM 4.5. GPT-5 is strongest in Reasoning, Image Generation, Web Design.

How much does Z.AI: GLM 4.5 cost compared to GPT-5?

Z.AI: GLM 4.5 costs $0.6/M input tokens and GPT-5 costs $1.25/M input tokens. Z.AI: GLM 4.5 is $0.65/M cheaper per input. The more expensive model wins 82% of duels, so the premium may be justified by quality.

How are Z.AI: GLM 4.5 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. 19 votes have been collected for this pair across 43 challenges. All vote data is part of RIVAL's open dataset.

Rival

Z.AI: GLM 4.5vsGPT-5

43 challenges

Why Z.AI: GLM 4.5?

4.2x cheaper overall$0.60/M in · $2.20/M out

Why GPT-5?

3.1x more context400K

Leads 1 of 1 benchmarks

Stronger on SWE-bench Verified74.9% vs 64.2%

Favorites

Z.AI: GLM 4.5GPT-5

Movie

Inception

20108.4

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12 / 43

Estimate Complexityreasoning

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

Z.AI: GLM 4.5

Zhipu · Jul 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 / 43

AI Board Game Logicreasoning

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

Z.AI: GLM 4.5

Zhipu · Jul 2025svg

No content available for this output.

GPT-5

Openai · Aug 2025svg

Vote

3 / 43

Logic Puzzlereasoning

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

Z.AI: GLM 4.5

Zhipu · Jul 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 / 43

Math Misconception Testreasoning

Tests an AI's understanding of number representation

Z.AI: GLM 4.5

Zhipu · Jul 2025text

No content available for this output.

GPT-5

Openai · Aug 2025text

No content available for this output.

Vote

5 / 43

Stochastic Consistency Testanalysis

Tests an AI's randomness and creativity

Z.AI: GLM 4.5

Zhipu · Jul 2025text

No content available for this output.

GPT-5

Openai · Aug 2025text

No content available for this output.

Vote

6 / 43

SVG Layout Challengeimage generation

Tests an AI's ability to generate vector graphics

Z.AI: GLM 4.5

Zhipu · Jul 2025svg

No content available for this output.

GPT-5

Openai · Aug 2025svg

Vote

7 / 43

Xbox Controller SVG Artimage generation

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

Z.AI: GLM 4.5

Zhipu · Jul 2025svg

No content available for this output.

GPT-5

Openai · Aug 2025svg

Vote

8 / 43

Generate a Stand-Up Routineconversation

Tests an AI's humor and creative writing ability

Z.AI: GLM 4.5

Zhipu · Jul 2025text

No content available for this output.

GPT-5

Openai · Aug 2025text

No content available for this output.

Vote

9 / 43

Realistic AI Interviewconversation

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

Z.AI: GLM 4.5

Zhipu · Jul 2025text

No content available for this output.

GPT-5

Openai · Aug 2025text

No content available for this output.

Vote

10 / 43

Satirical Fake News Headlineconversation

Tests an AI's humor and understanding of current events

Z.AI: GLM 4.5

Zhipu · Jul 2025text

No content available for this output.

GPT-5

Openai · Aug 2025text

No content available for this output.

Vote

11 / 43

Character Voice Testconversation

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

Z.AI: GLM 4.5

Zhipu · Jul 2025text

No content available for this output.

GPT-5

Openai · Aug 2025text

No content available for this output.

Vote

12 / 43

Minimalist Landing Pageweb design

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

Z.AI: GLM 4.5

Zhipu · Jul 2025website

GPT-5

Openai · Aug 2025website

Vote

12 of 43

FAQ

Continue exploring

Z.AI: GLM 4.5 vs Llama 4 MaverickNew provider

Z.AI: GLM 4.5 vs MiniMax M2.5New provider

Z.AI: GLM 4.5 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.