GPT OSS 120B High vs Qwen3-235B-A22B-Thinking-2507
GPT OSS 120B High and Qwen3-235B-A22B-Thinking-2507 are closely matched at 25.4 and 28.0 on the LLM Stats Score. GPT OSS 120B High is 4.9x cheaper per token.
OpenAI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
GPT OSS 120B High and Qwen3-235B-A22B-Thinking-2507 are closely matched on the overall LLM Stats Score at 25.4 and 28.0.
The models split the 4 individual benchmarks reported for both models evenly.
On price, GPT OSS 120B High is roughly 4.9x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Qwen3-235B-A22B-Thinking-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 GPT OSS 120B High
- cost matters — it's about 4.9x cheaper per token
- you want the most recent training data — it shipped Aug 2025
Choose Qwen3-235B-A22B-Thinking-2507
- you process long inputs — it offers a 262,144 token context window
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
7 reported for GPT OSS 120B High · 25 for Qwen3-235B-A22B-Thinking-2507
GPT OSS 120B High outperforms in 2 benchmarks (AIME 2025, LiveCodeBench v6), while Qwen3-235B-A22B-Thinking-2507 is better at 2 benchmarks (GPQA, MMLU-Pro).
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, GPT OSS 120B High ($0.10/1M tokens) is 3.0x cheaper than Qwen3-235B-A22B-Thinking-2507 ($0.30/1M tokens).
For output processing, GPT OSS 120B High ($0.50/1M tokens) is 6.0x cheaper than Qwen3-235B-A22B-Thinking-2507 ($3.00/1M tokens).
In conclusion, Qwen3-235B-A22B-Thinking-2507 is more expensive than GPT OSS 120B High.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Qwen3-235B-A22B-Thinking-2507 has 118.2B more parameters than GPT OSS 120B High, making it 101.2% larger.
Context Window
Maximum input and output token capacity
Qwen3-235B-A22B-Thinking-2507 accepts 262,144 input tokens compared to GPT OSS 120B High's 131,072 tokens. Both models can generate responses up to 131,072 tokens.
License
Usage and distribution terms
Both models are licensed under Apache 2.0.
Both models share the same licensing terms, providing consistent usage rights.
Apache 2.0
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
GPT OSS 120B High was released on 2025-08-05, while Qwen3-235B-A22B-Thinking-2507 was released on 2025-07-25.
GPT OSS 120B High is 0 month newer than Qwen3-235B-A22B-Thinking-2507.
Aug 5, 2025
1.2 years ago
1w newerJul 25, 2025
1.2 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 OSS 120B High is available from OpenAI, Fireworks. Qwen3-235B-A22B-Thinking-2507 is available from Fireworks, Novita.
GPT OSS 120B High
Qwen3-235B-A22B-Thinking-2507
Outputs Comparison
Judge for yourself.
Run your own prompts against GPT OSS 120B High and Qwen3-235B-A22B-Thinking-2507 side-by-side, then vote on the output you prefer.
FAQ
Common questions about GPT OSS 120B High vs Qwen3-235B-A22B-Thinking-2507.