DeepSeek V3.2 vs DeepSeek V3.1 Terminus
Head-to-head specifications
| Metric | DeepSeek V3.2 | DeepSeek V3.1 Terminus | Difference |
|---|---|---|---|
| Intelligence Index | 28.0 | 26.0 | +7.7% |
| Coding Index | 44.2 | 43.5 | +1.6% |
| Agentic Index | 18.3 | 18.1 | — |
| Context window | 200K tokens | 200K tokens | — |
| Blended price ($/1M tokens) | $0.11 | $0.31 | -64.5% |
| Access | Open weights | Open weights | — |
- DeepSeek V3.2 leads overall capability (Intelligence Index 28.0 vs 26.0).
- DeepSeek V3.2 is the cheaper model to run at $0.11/1M blended tokens — about 2.8× cheaper.
Verdict: DeepSeek V3.2 or DeepSeek V3.1 Terminus?
DeepSeek V3.2 advantages
- General intelligence (+7%)
- Affordability (+65%)
DeepSeek V3.1 Terminus advantages
- No decisive advantage on the tracked metrics.
Which should you choose?
- Choose the DeepSeek V3.2 if you need the strongest overall reasoning and accuracy.
- 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 (3.0× 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 DeepSeek V3.1 Terminus: 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).
DeepSeek V3.1 Terminus — DeepSeek text model with an Intelligence Index of 26, a 200K-token context window and a blended price of $0.31/1M tokens (open weights).
DeepSeek V3.2 vs DeepSeek V3.1 Terminus: DeepSeek V3.2 scores higher on the Intelligence Index. DeepSeek V3.2 leads overall capability (Intelligence Index 28.0 vs 26.0). DeepSeek V3.2 is the cheaper model to run at $0.11/1M blended tokens — about 2.8× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the DeepSeek V3.2 scores 28.0 versus 26.0. For software development, the Coding Index puts DeepSeek V3.2 ahead (44.2 vs 43.5). On agentic, multi-step tool-use tasks, DeepSeek V3.2 measures stronger. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The DeepSeek V3.2 accepts up to 200K 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 $0.31 per 1M tokens). DeepSeek V3.2 is open weights and DeepSeek V3.1 Terminus is open weights. 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 DeepSeek V3.1 Terminus?
DeepSeek V3.2 is the clearly stronger overall choice, winning most of the dimensions that matter. DeepSeek V3.2 leads overall capability (Intelligence Index 28.0 vs 26.0).
What is the main difference between the DeepSeek V3.2 and the DeepSeek V3.1 Terminus?
DeepSeek V3.2 leads overall capability (Intelligence Index 28.0 vs 26.0). DeepSeek V3.2 is the cheaper model to run at $0.11/1M blended tokens — about 2.8× cheaper.
Which is better value?
DeepSeek V3.2 offers more intelligence per dollar (3.0× 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 you need the strongest overall reasoning and accuracy. Choose the DeepSeek V3.2 if you want the lowest cost per token at scale.
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.