o1 vs Qwen3-235B-A22B-Instruct-2507
o1 and Qwen3-235B-A22B-Instruct-2507 are closely matched at 20.9 and 24.2 on the LLM Stats Score. Qwen3-235B-A22B-Instruct-2507 is 128.0x cheaper per token.
OpenAI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
o1 and Qwen3-235B-A22B-Instruct-2507 are closely matched on the overall LLM Stats Score at 20.9 and 24.2.
The models split the 2 individual benchmarks reported for both models evenly.
On price, Qwen3-235B-A22B-Instruct-2507 is roughly 128.0x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Qwen3-235B-A22B-Instruct-2507 also accepts a larger context window (262,144 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 o1
- you want predictable pricing at $15.00/M input and $60.00/M output
Choose Qwen3-235B-A22B-Instruct-2507
- cost matters — it's about 128.0x cheaper per token
- you process long inputs — it offers a 262,144 token context window
- you want the most recent training data — it shipped Jul 2025
- you need open weights you can self-host or fine-tune
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
19 reported for o1 · 25 for Qwen3-235B-A22B-Instruct-2507
o1 outperforms in 1 benchmarks (GPQA), while Qwen3-235B-A22B-Instruct-2507 is better at 1 benchmark (SimpleQA).
Both models are evenly matched across the benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, o1 ($15.00/1M tokens) is 166.7x more expensive than Qwen3-235B-A22B-Instruct-2507 ($0.09/1M tokens).
For output processing, o1 ($60.00/1M tokens) is 109.1x more expensive than Qwen3-235B-A22B-Instruct-2507 ($0.55/1M tokens).
In conclusion, o1 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
Qwen3-235B-A22B-Instruct-2507 accepts 262,144 input tokens compared to o1's 200,000 tokens. Qwen3-235B-A22B-Instruct-2507 can generate longer responses up to 262,144 tokens, while o1 is limited to 100,000 tokens.
License
Usage and distribution terms
o1 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
o1 was released on 2024-12-17, while Qwen3-235B-A22B-Instruct-2507 was released on 2025-07-22.
Qwen3-235B-A22B-Instruct-2507 is 7 months newer than o1.
Dec 17, 2024
1.7 years ago
Jul 22, 2025
1.1 years ago
7mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
o1 is available from Azure, OpenAI. Qwen3-235B-A22B-Instruct-2507 is available from DeepInfra, Fireworks, Novita.
o1
Qwen3-235B-A22B-Instruct-2507
Outputs Comparison
Judge for yourself.
Run your own prompts against o1 and Qwen3-235B-A22B-Instruct-2507 side-by-side, then vote on the output you prefer.
FAQ
Common questions about o1 vs Qwen3-235B-A22B-Instruct-2507.