DeepSeek-V3.2 (Thinking) vs GPT OSS 120B
DeepSeek-V3.2 (Thinking) and GPT OSS 120B are closely matched at 32.6 and 28.8 on the LLM Stats Score. GPT OSS 120B is 4.5x cheaper per token.
DeepSeek · OpenAI · Updated for 2026
Which is better?
DeepSeek-V3.2 (Thinking) and GPT OSS 120B are closely matched on the overall LLM Stats Score at 32.6 and 28.8.
In the 3 individual benchmarks reported for both models, DeepSeek-V3.2 (Thinking) wins 2; this is a narrower head-to-head signal than the composite indexes.
On price, GPT OSS 120B is roughly 4.5x 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 DeepSeek-V3.2 (Thinking)
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 2 of 3 exact shared results
- you want the most recent training data — it shipped Dec 2025
Choose GPT OSS 120B
- cost matters — it's about 4.5x cheaper per token
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
14 reported for DeepSeek-V3.2 (Thinking) · 7 for GPT OSS 120B
DeepSeek-V3.2 (Thinking) outperforms in 2 benchmarks (GPQA, Humanity's Last Exam), while GPT OSS 120B is better at 1 benchmark (CodeForces).
DeepSeek-V3.2 (Thinking) shows notably better performance in the majority of benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V3.2 (Thinking) ($0.28/1M tokens) is 7.6x more expensive than GPT OSS 120B ($0.04/1M tokens).
For output processing, DeepSeek-V3.2 (Thinking) ($0.42/1M tokens) is 2.5x more expensive than GPT OSS 120B ($0.17/1M tokens).
In conclusion, DeepSeek-V3.2 (Thinking) is more expensive than GPT OSS 120B.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V3.2 (Thinking) has 568.2B more parameters than GPT OSS 120B, making it 486.5% larger.
Context Window
Maximum input and output token capacity
Both models have the same input context window of 131,072 tokens. GPT OSS 120B can generate longer responses up to 131,072 tokens, while DeepSeek-V3.2 (Thinking) is limited to 65,536 tokens.
License
Usage and distribution terms
DeepSeek-V3.2 (Thinking) is licensed under MIT, while GPT OSS 120B uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
DeepSeek-V3.2 (Thinking) was released on 2025-12-01, while GPT OSS 120B was released on 2025-08-05.
DeepSeek-V3.2 (Thinking) is 4 months newer than GPT OSS 120B.
Dec 1, 2025
9 months ago
3mo newerAug 5, 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
DeepSeek-V3.2 (Thinking) is available from DeepSeek. GPT OSS 120B is available from DeepInfra, Novita, OpenAI, Fireworks, Groq.
DeepSeek-V3.2 (Thinking)
GPT OSS 120B
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V3.2 (Thinking) and GPT OSS 120B side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V3.2 (Thinking) vs GPT OSS 120B.