DeepSeek V3.2 vs o1
Head-to-head specifications
| Metric | DeepSeek V3.2 | o1 | Difference |
|---|---|---|---|
| Intelligence Index | 28.0 | 27.0 | +3.7% |
| Coding Index | 44.2 | 39.7 | +11.3% |
| Context window | 200K tokens | 256K tokens | — |
| Blended price ($/1M tokens) | $0.11 | $1.74 | -93.7% |
| Access | Open weights | Proprietary API | — |
- DeepSeek V3.2 leads overall capability (Intelligence Index 28.0 vs 27.0).
- DeepSeek V3.2 is the cheaper model to run at $0.11/1M blended tokens — about 15.8× cheaper.
- o1 offers the larger context window (256K tokens), useful for long documents and codebases.
Verdict: DeepSeek V3.2 or o1?
DeepSeek V3.2 advantages
- Coding ability (+10%)
- Affordability (+94%)
o1 advantages
- Context window (+22%)
Which should you choose?
- Choose the DeepSeek V3.2 if coding and software development are your main workload.
- Choose the o1 if you work with long documents or large codebases.
- Choose the DeepSeek V3.2 if you want the lowest cost per token at scale.
Value for money
DeepSeek V3.2 offers more intelligence per dollar (16.4× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use. It is also open-weight, so self-hosting can reduce costs further at scale.
DeepSeek V3.2 vs o1: which should you choose?
DeepSeek V3.2 — DeepSeek text model with an Intelligence Index of 28, a 200K-token context window and a blended price of $0.11/1M tokens (open weights).
o1 — OpenAI multimodal model with an Intelligence Index of 27, a 256K-token context window and a blended price of $1.74/1M tokens.
DeepSeek V3.2 vs o1: DeepSeek V3.2 scores higher on the Intelligence Index. DeepSeek V3.2 leads overall capability (Intelligence Index 28.0 vs 27.0). DeepSeek V3.2 is the cheaper model to run at $0.11/1M blended tokens — about 15.8× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the DeepSeek V3.2 scores 28.0 versus 27.0. For software development, the Coding Index puts DeepSeek V3.2 ahead (44.2 vs 39.7). Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The o1 accepts up to 256K tokens per request, which sets how much documentation, transcript or code it can reason over at once.
Pricing and access
At blended per-token rates, DeepSeek V3.2 is the cheaper model to run ($0.11 vs $1.74 per 1M tokens). DeepSeek V3.2 is open weights and o1 is proprietary api. Open-weight models can be self-hosted, trading per-call cost for infrastructure you manage; for production also weigh rate limits, throughput and data-residency requirements.
The verdict
Both are credible choices in the ai model comparison space; the specification table above lays out every metric so you can weigh the trade-offs that matter to you. Pick the one whose strengths line up with how you will actually use it.
Frequently asked questions
Is the DeepSeek V3.2 better than the o1?
DeepSeek V3.2 takes the overall edge, though o1 wins in specific areas worth weighing. DeepSeek V3.2 leads overall capability (Intelligence Index 28.0 vs 27.0).
What is the main difference between the DeepSeek V3.2 and the o1?
DeepSeek V3.2 leads overall capability (Intelligence Index 28.0 vs 27.0). DeepSeek V3.2 is the cheaper model to run at $0.11/1M blended tokens — about 15.8× cheaper.
Which is better value?
DeepSeek V3.2 offers more intelligence per dollar (16.4× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use. It is also open-weight, so self-hosting can reduce costs further at scale.
Which should I choose?
Choose the DeepSeek V3.2 if coding and software development are your main workload. Choose the o1 if you work with long documents or large codebases.
Methodology
Large language models are compared on independent leaderboard metrics: an Intelligence Index (a composite of reasoning and knowledge evaluations), Coding and Agentic indices where measured, community arena Elo, maximum context window, a blended API price per million tokens (weighted across cache-hit, input and output rates), and measured output speed in tokens per second. Where a model ships multiple reasoning-effort variants, we report its strongest variant. Benchmarks capture only part of real-world quality, which also depends on tool use, latency, safety and task fit — and this space moves quickly, so figures reflect the leaderboard snapshot on the page date.