GPT-4.1 vs DeepSeek V3.1 Terminus
Head-to-head specifications
| Metric | GPT-4.1 | DeepSeek V3.1 Terminus | Difference |
|---|---|---|---|
| Intelligence Index | 24.0 | 26.0 | -7.7% |
| Context window | 1M tokens | 200K tokens | — |
| Blended price ($/1M tokens) | $0.90 | $0.31 | +190.3% |
| Access | Proprietary API | Open weights | — |
- DeepSeek V3.1 Terminus leads overall capability (Intelligence Index 26.0 vs 24.0).
- DeepSeek V3.1 Terminus is the cheaper model to run at $0.31/1M blended tokens — about 2.9× cheaper.
- GPT-4.1 offers the larger context window (1M tokens), useful for long documents and codebases.
Verdict: GPT-4.1 or DeepSeek V3.1 Terminus?
GPT-4.1 advantages
- Context window (+80%)
DeepSeek V3.1 Terminus advantages
- General intelligence (+8%)
- Affordability (+66%)
Which should you choose?
- Choose the GPT-4.1 if you work with long documents or large codebases.
- Choose the DeepSeek V3.1 Terminus if you need the strongest overall reasoning and accuracy.
Value for money
DeepSeek V3.1 Terminus offers more intelligence per dollar (3.1× 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.
GPT-4.1 vs DeepSeek V3.1 Terminus: which should you choose?
GPT-4.1 — OpenAI multimodal model with an Intelligence Index of 24, a 1M-token context window and a blended price of $0.9/1M tokens.
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).
GPT-4.1 vs DeepSeek V3.1 Terminus: DeepSeek V3.1 Terminus scores higher on the Intelligence Index. DeepSeek V3.1 Terminus leads overall capability (Intelligence Index 26.0 vs 24.0). DeepSeek V3.1 Terminus is the cheaper model to run at $0.31/1M blended tokens — about 2.9× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the DeepSeek V3.1 Terminus scores 26.0 versus 24.0. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The GPT-4.1 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 V3.1 Terminus is the cheaper model to run ($0.31 vs $0.90 per 1M tokens). GPT-4.1 is proprietary api 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 GPT-4.1 better than the DeepSeek V3.1 Terminus?
DeepSeek V3.1 Terminus takes the overall edge, though GPT-4.1 wins in specific areas worth weighing. DeepSeek V3.1 Terminus leads overall capability (Intelligence Index 26.0 vs 24.0).
What is the main difference between the GPT-4.1 and the DeepSeek V3.1 Terminus?
DeepSeek V3.1 Terminus leads overall capability (Intelligence Index 26.0 vs 24.0). DeepSeek V3.1 Terminus is the cheaper model to run at $0.31/1M blended tokens — about 2.9× cheaper.
Which is better value?
DeepSeek V3.1 Terminus offers more intelligence per dollar (3.1× 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 GPT-4.1 if you work with long documents or large codebases. Choose the DeepSeek V3.1 Terminus 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.