What is the difference between Qwen: Qwen3 235B A22B Thinking 2507 and Llama 4 Maverick?

Qwen: Qwen3 235B A22B Thinking 2507 is developed by Qwen while Llama 4 Maverick is developed by Meta AI. Qwen: Qwen3 235B A22B Thinking 2507 has a 131K token context window vs Llama 4 Maverick's 1.0M. You can compare their actual outputs across 39 challenges on Rival to see how they differ in practice.

Which is better, Qwen: Qwen3 235B A22B Thinking 2507 or Llama 4 Maverick?

It depends on your use case. Qwen: Qwen3 235B A22B Thinking 2507 and Llama 4 Maverick each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 39 challenges so you can judge which fits your needs best.

How much does Qwen: Qwen3 235B A22B Thinking 2507 cost compared to Llama 4 Maverick?

Qwen: Qwen3 235B A22B Thinking 2507 costs $0.11/M input tokens and Llama 4 Maverick costs $1.5/M input tokens. Qwen: Qwen3 235B A22B Thinking 2507 is $1.39/M cheaper per input. Check their side-by-side outputs on Rival to see if the price difference is justified by quality.

How can I compare Qwen: Qwen3 235B A22B Thinking 2507 and Llama 4 Maverick on Rival?

This page shows a side-by-side comparison of Qwen: Qwen3 235B A22B Thinking 2507 and Llama 4 Maverick across shared challenges. You can vote on which model produced the better output, and Pro users can create custom challenges to test both models with their own prompts.

Qwen: Qwen3 235B A22B Thinking 2507 vs Llama 4 Maverick: Which Is Better? [2026 Comparison]

Rival

Updated Jul 25, 2025

Our Verdict

Qwen: Qwen3 235B A22B Thinking 2507

Llama 4 Maverick

No community votes yet. On paper, these are closely matched - try both with your actual task to see which fits your workflow.

Qwen: Qwen3 235B A22B Thinking 2507 is 4.2x cheaper per token — worth considering if cost matters.

Too close to call

Writing DNA

Style Comparison

Similarity

91%

Qwen: Qwen3 235B A22B Thinking 2507 uses 53.9x more emoji

Qwen: Qwen3 235B A22B Thinking 2507

Llama 4 Maverick

56%Vocabulary42%

14wSentence Length24w

0.37Hedging0.76

5.7Bold3.7

4.0Lists6.3

0.54Emoji0.00

0.70Headings0.74

0.14Transitions0.11

Based on 22 + 12 text responses

Ask them anything yourself

279 AI models invented the same fake scientist.

We read every word. 250 models. 2.14 million words. This is what we found.

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Estimating the total number of FLOPs (Floating Point Operations) required to train a large language model like GPT-3 involves understanding the model's architecture, its size, and the training dataset. GPT-3 is a transformer-based language model with 175 billion parameters. Here's a step-by-step breakdown to estimate the FLOPs required for its training:

1. Understand GPT-3's Architecture

GPT-3 is based on the transformer architecture, which is primarily composed of self-attention mechanisms and feed-forward neural networks (FFNNs). The model has 96 layers, with a hidden dimension of 12288 and 96 attention heads.

2. Calculate FLOPs per Token for a Forward Pass

To simplify, let's consider the major components that contribute to FLOPs: the self-attention mechanism and the FFNNs.

Self-Attention: For a single token, the self-attention mechanism involves computing attention scores (query-key dot products), applying softmax, and then computing the weighted sum of the values. The complexity is roughly O(nd^2 + n^2d) where n is the sequence length and d is the hidden dimension. However, for a single token, the computation is more related to d. Specifically, for one token, the computation involves d^2 operations for Q, K, V projections and n*d operations for attention computation. For GPT-3, n=2048.
FFNNs: Each FFNN involves two linear layers with a GeLU activation in between. The first layer projects from d to 4d, and the second projects back from 4d to d. So, for one token, the FLOPs are approximately 2 * d * 4d = 8d^2.

For GPT-3, d = 12288. So, for one token, the FLOPs for self-attention are roughly 2*d^2 (simplifying and focusing on the dominant terms related to d) and for FFNNs are 8d^2.

3. Calculate Total FLOPs per Token for One Layer

For one layer, the total FLOPs per token = FLOPs for self-attention + FLOPs for FFNNs = 2d^2 + 8d^2 = 10*d^2.

4. Calculate Total FLOPs for One Forward Pass

GPT-3 has 96 layers. So, for one token, the total FLOPs for a forward pass = 96 * 10 * d^2.

Let's calculate: d = 12288, so d^2 = 12288^2 = 151,008,384.
Total FLOPs per token for one forward pass = 96 * 10 * 151,008,384 ≈ 1.45 * 10^{11}.

5. Consider the Backward Pass

The backward pass typically requires roughly the same number of FLOPs as the forward pass because the computations are similar but in reverse. So, for one token, the total FLOPs for both forward and backward pass = 2 * 1.45 * 10^{11} ≈ 2.9 * 10^{11}.

6. Training Dataset Size and Tokens

GPT-3 was trained on a dataset that includes a large corpus of text, roughly 300 billion tokens.

7. Calculate Total FLOPs for Training

Total FLOPs = Total FLOPs per token for forward and backward pass * Total number of tokens.
= 2.9 * 10^{11} * 300 * 10^9.

Let's calculate:
= 2.9 * 300 * 10^{20}
= 870 * 10^{20}
= 8.7 * 10^{22} FLOPs.

Adjustments and Considerations

The actual training process involves additional computations (like gradient updates, layer normalization, etc.), but these are relatively minor compared to the matrix multiplications in the self-attention and FFNNs. The above estimate focuses on the dominant operations.

Conclusion

The estimated total number of FLOPs required to train GPT-3 is approximately 8.7 * 10^{22}.

This is a simplified estimate and actual numbers may vary based on the specifics of the implementation, optimizations, and hardware used. The actual reported FLOPs for training GPT-3 was around 3.14 * 10^{23}, indicating that our simplified model underestimates the total FLOPs, likely due to not capturing all the nuances of the training process and the specific optimizations or inefficiencies in the actual training setup.

Qwen: Qwen3 235B A22B Thinking 2507 vs Llama 4 Maverick

Which is better, Qwen: Qwen3 235B A22B Thinking 2507 or Llama 4 Maverick?

Key Differences Between Qwen: Qwen3 235B A22B Thinking 2507 and Llama 4 Maverick

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Common questions

What is the difference between Qwen: Qwen3 235B A22B Thinking 2507 and Llama 4 Maverick?

Which is better, Qwen: Qwen3 235B A22B Thinking 2507 or Llama 4 Maverick?

How much does Qwen: Qwen3 235B A22B Thinking 2507 cost compared to Llama 4 Maverick?

How can I compare Qwen: Qwen3 235B A22B Thinking 2507 and Llama 4 Maverick on Rival?

Why Qwen: Qwen3 235B A22B Thinking 2507?

Why Llama 4 Maverick?

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