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

Both are developed by Zhipu AI but target different use cases. Z.AI: GLM 4.5 has a 128K token context window vs Z.ai: GLM 5's 203K. in 4 community votes on RIVAL, Z.ai: GLM 5 wins 100% 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 Z.ai: GLM 5?

Based on 4 community votes on RIVAL, Z.ai: GLM 5 wins 100% of head-to-head matchups against Z.AI: GLM 4.5. Z.ai: GLM 5 is strongest in Reasoning, Image Generation.

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

Z.AI: GLM 4.5 costs $0.6/M input tokens and Z.ai: GLM 5 costs $0.8/M input tokens. Z.AI: GLM 4.5 is $0.20/M cheaper per input. The more expensive model wins 100% of duels, so the premium may be justified by quality.

How are Z.AI: GLM 4.5 vs Z.ai: GLM 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. 4 votes have been collected for this pair across 43 challenges. All vote data is part of RIVAL's open dataset.

Rival

Z.AI: GLM 4.5vsZ.ai: GLM 5

43 challenges

Why Z.AI: GLM 4.5?

1.2x cheaper overall$0.60/M in · $2.20/M out

Why Z.ai: GLM 5?

1.6x more context203K

Leads 1 of 1 benchmarks

Stronger on SWE-bench Verified77.8% vs 64.2%

7 months newerFeb 2026

Favorites

Z.AI: GLM 4.5Z.ai: GLM 5

Movie

Inception

20108.4

The Shawshank Redemption

19948.7

City

—

Istanbul

Turkey

Game

—

Tetris (1984)

Puzzle4.4

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.

Z.ai: GLM 5

Zhipu · Feb 2026text

The total number of FLOPs required to train GPT‑3 is approximately 3.15 × 10²³.

Step‑by‑step reasoning:

Model size: GPT‑3 has 175 billion (1.75 × 10¹¹) trainable parameters.
Training data: According to the paper abstract, the model was trained on 300 billion (3 × 10¹¹) tokens.
FLOPs per token for training a transformer:
- Forward pass: each parameter is used in one multiply‑add operation → ~2 FLOPs per parameter → 2 N FLOPs per token.
- Backward pass (gradient computation): roughly twice the forward cost → ~4 N FLOPs per token.
- Total: ~6 N FLOPs per token.
  This approximation is standard for large transformer models and neglects small overheads like attention quadratic terms (which add only ~1–2% for GPT‑3’s sequence length).
Calculation:
Total FLOPs ≈ 6 × (1.75 × 10¹¹) × (3 × 10¹¹)
= 6 × 5.25 × 10²²
= 3.15 × 10²³.
Consistency check: The GPT‑3 paper reports a training compute of 3,640 PF‑days. Converting:
1 PF‑day = 10¹⁵ FLOP/s × 86400 s = 8.64 × 10¹⁹ FLOPs.
3,640 PF‑days = 3,640 × 8.64 × 10¹⁹ ≈ 3.14 × 10²³ FLOPs, matching the estimate.

Thus, the total FLOPs required to train GPT‑3 is about 3.15 × 10²³.

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.

Z.ai: GLM 5

Zhipu · Feb 2026svg

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.

Z.ai: GLM 5

Zhipu · Feb 2026text

No content available for this output.

Vote

4 / 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.

Z.ai: GLM 5

Zhipu · Feb 2026text

No content available for this output.

Vote

5 / 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.

Z.ai: GLM 5

Zhipu · Feb 2026svg

No content available for this output.

Vote

6 / 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.

Z.ai: GLM 5

Zhipu · Feb 2026svg

No content available for this output.

Vote

7 / 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.

Z.ai: GLM 5

Zhipu · Feb 2026text

No content available for this output.

Vote

8 / 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.

Z.ai: GLM 5

Zhipu · Feb 2026text

No content available for this output.

Vote

9 / 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.

Z.ai: GLM 5

Zhipu · Feb 2026text

No content available for this output.

Vote

10 / 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.

Z.ai: GLM 5

Zhipu · Feb 2026text

No content available for this output.

Vote

11 / 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

Z.ai: GLM 5

Zhipu · Feb 2026website

Vote

12 / 43

Pokémon Battle UI Recreationweb design

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

Z.AI: GLM 4.5

Zhipu · Jul 2025website

No content available for this output.

Z.ai: GLM 5

Zhipu · Feb 2026website

No content available for this output.

Vote

12 of 43

FAQ

Continue exploring

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The total number of FLOPs required to train GPT‑3 is approximately 3.15 × 10²³.

Step‑by‑step reasoning:

Model size: GPT‑3 has 175 billion (1.75 × 10¹¹) trainable parameters.
Training data: According to the paper abstract, the model was trained on 300 billion (3 × 10¹¹) tokens.
FLOPs per token for training a transformer:
- Forward pass: each parameter is used in one multiply‑add operation → ~2 FLOPs per parameter → 2 N FLOPs per token.
- Backward pass (gradient computation): roughly twice the forward cost → ~4 N FLOPs per token.
- Total: ~6 N FLOPs per token.
  This approximation is standard for large transformer models and neglects small overheads like attention quadratic terms (which add only ~1–2% for GPT‑3’s sequence length).
Calculation:
Total FLOPs ≈ 6 × (1.75 × 10¹¹) × (3 × 10¹¹)
= 6 × 5.25 × 10²²
= 3.15 × 10²³.
Consistency check: The GPT‑3 paper reports a training compute of 3,640 PF‑days. Converting:
1 PF‑day = 10¹⁵ FLOP/s × 86400 s = 8.64 × 10¹⁹ FLOPs.
3,640 PF‑days = 3,640 × 8.64 × 10¹⁹ ≈ 3.14 × 10²³ FLOPs, matching the estimate.

Thus, the total FLOPs required to train GPT‑3 is about 3.15 × 10²³.