GPT-5.3 Codex vs Qwen3-235B-A22B-Thinking-2507
GPT-5.3 Codex leads the LLM Stats Score 36.5 to 28.1. Qwen3-235B-A22B-Thinking-2507 is 4.9x cheaper per token.
OpenAI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
GPT-5.3 Codex leads the overall LLM Stats Score 36.5 to 28.1, ranking #74 overall.
On price, Qwen3-235B-A22B-Thinking-2507 is roughly 4.9x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
GPT-5.3 Codex also accepts a larger context window (400,000 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 GPT-5.3 Codex
- overall performance matters — it scores 36.5 and ranks #74 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you process long inputs — it offers a 400,000 token context window
- you want the most recent training data — it shipped Feb 2026
Choose Qwen3-235B-A22B-Thinking-2507
- cost matters — it's about 4.9x cheaper per token
- you need open weights you can self-host or fine-tune
At a glance
The differences that matter most.
Individual benchmarks
6 reported for GPT-5.3 Codex · 25 for Qwen3-235B-A22B-Thinking-2507
GPT-5.3 Codex 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, GPT-5.3 Codex ($1.75/1M tokens) is 5.8x more expensive than Qwen3-235B-A22B-Thinking-2507 ($0.30/1M tokens).
For output processing, GPT-5.3 Codex ($14.00/1M tokens) is 4.7x more expensive than Qwen3-235B-A22B-Thinking-2507 ($3.00/1M tokens).
In conclusion, GPT-5.3 Codex 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
GPT-5.3 Codex accepts 400,000 input tokens compared to Qwen3-235B-A22B-Thinking-2507's 262,144 tokens. Qwen3-235B-A22B-Thinking-2507 can generate longer responses up to 131,072 tokens, while GPT-5.3 Codex is limited to 128,000 tokens.
Input capabilities
Documented input modalities across available providers
GPT-5.3 Codex supports multimodal inputs, whereas Qwen3-235B-A22B-Thinking-2507 does not.
GPT-5.3 Codex can handle both text and other forms of data like images, making it suitable for multimodal applications.
GPT-5.3 Codex
Qwen3-235B-A22B-Thinking-2507
License
Usage and distribution terms
GPT-5.3 Codex 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
GPT-5.3 Codex was released on 2026-02-05, while Qwen3-235B-A22B-Thinking-2507 was released on 2025-07-25.
GPT-5.3 Codex is 7 months newer than Qwen3-235B-A22B-Thinking-2507.
Feb 5, 2026
7 months ago
6mo newerJul 25, 2025
1.1 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-5.3 Codex is available from OpenAI. Qwen3-235B-A22B-Thinking-2507 is available from Fireworks, Novita.
GPT-5.3 Codex
Qwen3-235B-A22B-Thinking-2507
Outputs Comparison
Judge for yourself.
Run your own prompts against GPT-5.3 Codex and Qwen3-235B-A22B-Thinking-2507 side-by-side, then vote on the output you prefer.
FAQ
Common questions about GPT-5.3 Codex vs Qwen3-235B-A22B-Thinking-2507.