DeepSeek V3.2 Exp vs GPT-5.6 Sol
Head-to-head specifications
| Metric | DeepSeek V3.2 Exp | GPT-5.6 Sol | Difference |
|---|---|---|---|
| Intelligence Index | 26.0 | 59.0 | -55.9% |
| Context window | 200K tokens | 1M tokens | — |
| Blended price ($/1M tokens) | $0.12 | $1.54 | -92.2% |
| Access | Open weights | Proprietary API | — |
- GPT-5.6 Sol leads overall capability (Intelligence Index 59.0 vs 26.0).
- DeepSeek V3.2 Exp is the cheaper model to run at $0.12/1M blended tokens — about 12.8× cheaper.
- GPT-5.6 Sol offers the larger context window (1M tokens), useful for long documents and codebases.
Verdict: DeepSeek V3.2 Exp or GPT-5.6 Sol?
DeepSeek V3.2 Exp advantages
- Affordability (+92%)
GPT-5.6 Sol advantages
- General intelligence (+56%)
- Context window (+80%)
Which should you choose?
- Choose the DeepSeek V3.2 Exp if you want the lowest cost per token at scale.
- Choose the GPT-5.6 Sol if you need the strongest overall reasoning and accuracy.
Value for money
DeepSeek V3.2 Exp offers more intelligence per dollar (5.7× 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 Exp vs GPT-5.6 Sol: which should you choose?
DeepSeek V3.2 Exp — DeepSeek text model with an Intelligence Index of 26, a 200K-token context window and a blended price of $0.12/1M tokens (open weights).
GPT-5.6 Sol — OpenAI multimodal model with an Intelligence Index of 59, a 1M-token context window and a blended price of $1.54/1M tokens.
DeepSeek V3.2 Exp vs GPT-5.6 Sol: GPT-5.6 Sol scores higher on the Intelligence Index. GPT-5.6 Sol leads overall capability (Intelligence Index 59.0 vs 26.0). DeepSeek V3.2 Exp is the cheaper model to run at $0.12/1M blended tokens — about 12.8× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the GPT-5.6 Sol scores 59.0 versus 26.0. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The GPT-5.6 Sol accepts up to 1 million 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 Exp is the cheaper model to run ($0.12 vs $1.54 per 1M tokens). DeepSeek V3.2 Exp is open weights and GPT-5.6 Sol 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 Exp better than the GPT-5.6 Sol?
GPT-5.6 Sol takes the overall edge, though DeepSeek V3.2 Exp wins in specific areas worth weighing. GPT-5.6 Sol leads overall capability (Intelligence Index 59.0 vs 26.0).
What is the main difference between the DeepSeek V3.2 Exp and the GPT-5.6 Sol?
GPT-5.6 Sol leads overall capability (Intelligence Index 59.0 vs 26.0). DeepSeek V3.2 Exp is the cheaper model to run at $0.12/1M blended tokens — about 12.8× cheaper.
Which is better value?
DeepSeek V3.2 Exp offers more intelligence per dollar (5.7× 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 Exp if you want the lowest cost per token at scale. Choose the GPT-5.6 Sol if you need the strongest overall reasoning and accuracy.
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.