DeepSeek V3.2 vs GPT-5.6 Sol
Head-to-head specifications
| Metric | DeepSeek V3.2 | GPT-5.6 Sol | Difference |
|---|---|---|---|
| Intelligence Index | 28.0 | 59.0 | -52.5% |
| Coding Index | 44.2 | 77.4 | -42.9% |
| Agentic Index | 18.3 | 54.0 | — |
| Context window | 200K tokens | 1M tokens | — |
| Blended price ($/1M tokens) | $0.11 | $1.54 | -92.9% |
| Access | Open weights | Proprietary API | — |
- GPT-5.6 Sol leads overall capability (Intelligence Index 59.0 vs 28.0).
- DeepSeek V3.2 is the cheaper model to run at $0.11/1M blended tokens — about 14.0× cheaper.
- GPT-5.6 Sol offers the larger context window (1M tokens), useful for long documents and codebases.
Verdict: DeepSeek V3.2 or GPT-5.6 Sol?
DeepSeek V3.2 advantages
- Affordability (+93%)
GPT-5.6 Sol advantages
- General intelligence (+53%)
- Coding ability (+43%)
- Agentic task performance (+66%)
- Context window (+80%)
Which should you choose?
- Choose the DeepSeek V3.2 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 offers more intelligence per dollar (6.6× 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 GPT-5.6 Sol: 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).
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 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 28.0). DeepSeek V3.2 is the cheaper model to run at $0.11/1M blended tokens — about 14.0× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the GPT-5.6 Sol scores 59.0 versus 28.0. For software development, the Coding Index puts GPT-5.6 Sol ahead (77.4 vs 44.2). On agentic, multi-step tool-use tasks, GPT-5.6 Sol measures stronger. 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 is the cheaper model to run ($0.11 vs $1.54 per 1M tokens). DeepSeek V3.2 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 better than the GPT-5.6 Sol?
GPT-5.6 Sol takes the overall edge, though DeepSeek V3.2 wins in specific areas worth weighing. GPT-5.6 Sol leads overall capability (Intelligence Index 59.0 vs 28.0).
What is the main difference between the DeepSeek V3.2 and the GPT-5.6 Sol?
GPT-5.6 Sol leads overall capability (Intelligence Index 59.0 vs 28.0). DeepSeek V3.2 is the cheaper model to run at $0.11/1M blended tokens — about 14.0× cheaper.
Which is better value?
DeepSeek V3.2 offers more intelligence per dollar (6.6× 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 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.