GPT-5.6 Sol vs DeepSeek V3.2 Exp
Head-to-head specifications
| Metric | GPT-5.6 Sol | DeepSeek V3.2 Exp | Difference |
|---|---|---|---|
| Intelligence Index | 59.0 | 26.0 | +126.9% |
| Context window | 1M tokens | 200K tokens | — |
| Blended price ($/1M tokens) | $1.54 | $0.12 | +1,183.3% |
| Access | Proprietary API | Open weights | — |
- 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: GPT-5.6 Sol or DeepSeek V3.2 Exp?
GPT-5.6 Sol advantages
- General intelligence (+56%)
- Context window (+80%)
DeepSeek V3.2 Exp advantages
- Affordability (+92%)
Which should you choose?
- Choose the GPT-5.6 Sol if you need the strongest overall reasoning and accuracy.
- Choose the DeepSeek V3.2 Exp if you want the lowest cost per token at scale.
- Choose the GPT-5.6 Sol if you work with long documents or large codebases.
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.
GPT-5.6 Sol vs DeepSeek V3.2 Exp: which should you choose?
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 — 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 vs DeepSeek V3.2 Exp: 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). GPT-5.6 Sol is proprietary api and DeepSeek V3.2 Exp 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 GPT-5.6 Sol better than the DeepSeek V3.2 Exp?
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 GPT-5.6 Sol and the DeepSeek V3.2 Exp?
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 GPT-5.6 Sol if you need the strongest overall reasoning and accuracy. Choose the DeepSeek V3.2 Exp 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.