Grok 4.7 vs Qwen3-235B-A22B-Thinking-2507
Grok 4.7 leads the LLM Stats Score 48.5 to 28.1. Qwen3-235B-A22B-Thinking-2507 is 3.1x cheaper per token.
xAI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
Grok 4.7 leads the overall LLM Stats Score 48.5 to 28.1, ranking #22 overall.
On price, Qwen3-235B-A22B-Thinking-2507 is roughly 3.1x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Grok 4.7 also accepts a larger context window (500,000 input tokens), making it the stronger choice for long documents and large codebases.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose Grok 4.7
- overall performance matters — it scores 48.5 and ranks #22 on LLM Stats
- your work emphasizes reasoning and agents — it leads those capability indexes
- you process long inputs — it offers a 500,000 token context window
- you want the most recent training data — it shipped Sep 2026
Choose Qwen3-235B-A22B-Thinking-2507
- cost matters — it's about 3.1x cheaper per token
- you need open weights you can self-host or fine-tune
At a glance
The differences that matter most.
Individual benchmarks
23 reported for Grok 4.7 · 25 for Qwen3-235B-A22B-Thinking-2507
Grok 4.7 and Qwen3-235B-A22B-Thinking-2507don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, Grok 4.7 ($2.00/1M tokens) is 6.7x more expensive than Qwen3-235B-A22B-Thinking-2507 ($0.30/1M tokens).
For output processing, Grok 4.7 ($6.00/1M tokens) is 2.0x more expensive than Qwen3-235B-A22B-Thinking-2507 ($3.00/1M tokens).
In conclusion, Grok 4.7 is more expensive than Qwen3-235B-A22B-Thinking-2507.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
Grok 4.7 accepts 500,000 input tokens compared to Qwen3-235B-A22B-Thinking-2507's 262,144 tokens. Only Qwen3-235B-A22B-Thinking-2507 specifies output context (131,072 tokens).
Input capabilities
Documented input modalities across available providers
Grok 4.7 supports multimodal inputs, whereas Qwen3-235B-A22B-Thinking-2507 does not.
Grok 4.7 can handle both text and other forms of data like images, making it suitable for multimodal applications.
Grok 4.7
Qwen3-235B-A22B-Thinking-2507
License
Usage and distribution terms
Grok 4.7 is licensed under a proprietary license, while Qwen3-235B-A22B-Thinking-2507 uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
Proprietary
Closed source
Apache 2.0
Open weights
Release Timeline
When each model was launched
Grok 4.7 was released on 2026-09-21, while Qwen3-235B-A22B-Thinking-2507 was released on 2025-07-25.
Grok 4.7 is 14 months newer than Qwen3-235B-A22B-Thinking-2507.
Sep 21, 2026
0 days ago
1.2yr newerJul 25, 2025
1.2 years ago
Knowledge Cutoff
When training data ends
Grok 4.7 has a documented knowledge cutoff of 2026-05-01, while Qwen3-235B-A22B-Thinking-2507's cutoff date is not specified.
We can confirm Grok 4.7's training data extends to 2026-05-01, but cannot make a direct comparison without Qwen3-235B-A22B-Thinking-2507's cutoff date.
May 2026
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Provider Availability
Grok 4.7 is available from xAI. Qwen3-235B-A22B-Thinking-2507 is available from Fireworks, Novita.
Grok 4.7
Qwen3-235B-A22B-Thinking-2507
Outputs Comparison
Judge for yourself.
Run your own prompts against Grok 4.7 and Qwen3-235B-A22B-Thinking-2507 side-by-side, then vote on the output you prefer.
FAQ
Common questions about Grok 4.7 vs Qwen3-235B-A22B-Thinking-2507.