GPT-6 Rosalind vs Qwen3-235B-A22B-Instruct-2507
Comparing GPT-6 Rosalind and Qwen3-235B-A22B-Instruct-2507 across benchmarks, pricing, and capabilities.
OpenAI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
GPT-6 Rosalind and Qwen3-235B-A22B-Instruct-2507 trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.
On price, Qwen3-235B-A22B-Instruct-2507 is roughly 146.3x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose GPT-6 Rosalind
- you want the most recent training data — it shipped Oct 2026
Choose Qwen3-235B-A22B-Instruct-2507
- cost matters — it's about 146.3x cheaper per token
- you need open weights you can self-host or fine-tune
At a glance
The differences that matter most.
Individual benchmarks
0 reported for GPT-6 Rosalind · 25 for Qwen3-235B-A22B-Instruct-2507
GPT-6 Rosalind and Qwen3-235B-A22B-Instruct-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, GPT-6 Rosalind ($15.00/1M tokens) is 166.7x more expensive than Qwen3-235B-A22B-Instruct-2507 ($0.09/1M tokens).
For output processing, GPT-6 Rosalind ($75.00/1M tokens) is 136.4x more expensive than Qwen3-235B-A22B-Instruct-2507 ($0.55/1M tokens).
In conclusion, GPT-6 Rosalind is more expensive than Qwen3-235B-A22B-Instruct-2507.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
Only Qwen3-235B-A22B-Instruct-2507 specifies input context (262,144 tokens). Only Qwen3-235B-A22B-Instruct-2507 specifies output context (262,144 tokens).
Input capabilities
Documented input modalities across available providers
GPT-6 Rosalind supports multimodal inputs, whereas Qwen3-235B-A22B-Instruct-2507 does not.
GPT-6 Rosalind can handle both text and other forms of data like images, making it suitable for multimodal applications.
GPT-6 Rosalind
Qwen3-235B-A22B-Instruct-2507
License
Usage and distribution terms
GPT-6 Rosalind is licensed under a proprietary license, while Qwen3-235B-A22B-Instruct-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
GPT-6 Rosalind was released on 2026-10-30, while Qwen3-235B-A22B-Instruct-2507 was released on 2025-07-22.
GPT-6 Rosalind is 16 months newer than Qwen3-235B-A22B-Instruct-2507.
Oct 30, 2026
1 weeks ago
1.3yr newerJul 22, 2025
1.3 years ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
GPT-6 Rosalind is available from OpenAI. Qwen3-235B-A22B-Instruct-2507 is available from DeepInfra, Fireworks, Novita.
GPT-6 Rosalind
Qwen3-235B-A22B-Instruct-2507
Outputs Comparison
Judge for yourself.
Run your own prompts against GPT-6 Rosalind and Qwen3-235B-A22B-Instruct-2507 side-by-side, then vote on the output you prefer.
FAQ
Common questions about GPT-6 Rosalind vs Qwen3-235B-A22B-Instruct-2507.