DeepSeek V3.2 vs Hy3-preview
Head-to-head specifications
| Metric | DeepSeek V3.2 | Hy3-preview | Difference |
|---|---|---|---|
| Intelligence Index | 28.0 | 29.0 | -3.4% |
| Context window | 200K tokens | 262K tokens | — |
| Blended price ($/1M tokens) | $0.11 | $0.10 | +10.0% |
| Access | Open weights | Open weights | — |
- Hy3-preview leads overall capability (Intelligence Index 29.0 vs 28.0).
- Hy3-preview is the cheaper model to run at $0.10/1M blended tokens — about 1.1× cheaper.
- Hy3-preview offers the larger context window (262K tokens), useful for long documents and codebases.
Verdict: DeepSeek V3.2 or Hy3-preview?
DeepSeek V3.2 advantages
- No decisive advantage on the tracked metrics.
Hy3-preview advantages
- Context window (+24%)
- Affordability (+9%)
Which should you choose?
- Choose the Hy3-preview if you work with long documents or large codebases.
Value for money
Hy3-preview offers more intelligence per dollar (1.1× 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 Hy3-preview: 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).
Hy3-preview — Hy3 multimodal model with an Intelligence Index of 29, a 262K-token context window and a blended price of $0.1/1M tokens (open weights).
DeepSeek V3.2 vs Hy3-preview: Hy3-preview scores higher on the Intelligence Index. Hy3-preview leads overall capability (Intelligence Index 29.0 vs 28.0). Hy3-preview is the cheaper model to run at $0.10/1M blended tokens — about 1.1× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the Hy3-preview scores 29.0 versus 28.0. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The Hy3-preview accepts up to 262K 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, Hy3-preview is the cheaper model to run ($0.10 vs $0.11 per 1M tokens). DeepSeek V3.2 is open weights and Hy3-preview 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 Hy3-preview?
Hy3-preview is the clearly stronger overall choice, winning most of the dimensions that matter. Hy3-preview leads overall capability (Intelligence Index 29.0 vs 28.0).
What is the main difference between the DeepSeek V3.2 and the Hy3-preview?
Hy3-preview leads overall capability (Intelligence Index 29.0 vs 28.0). Hy3-preview is the cheaper model to run at $0.10/1M blended tokens — about 1.1× cheaper.
Which is better value?
Hy3-preview offers more intelligence per dollar (1.1× 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 Hy3-preview 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.