Claude 4.1 Opus vs DeepSeek V4 Pro
Head-to-head specifications
| Metric | Claude 4.1 Opus | DeepSeek V4 Pro | Difference |
|---|---|---|---|
| Intelligence Index | 30.0 | 44.0 | -31.8% |
| Context window | 262K tokens | 1M tokens | — |
| Blended price ($/1M tokens) | $1.68 | $0.18 | +833.3% |
| Output speed (tokens/s) | 30 | 63 | -52.4% |
| Access | Proprietary API | Open weights | — |
- DeepSeek V4 Pro leads overall capability (Intelligence Index 44.0 vs 30.0).
- DeepSeek V4 Pro is the cheaper model to run at $0.18/1M blended tokens — about 9.3× cheaper.
- DeepSeek V4 Pro offers the larger context window (1M tokens), useful for long documents and codebases.
Verdict: Claude 4.1 Opus or DeepSeek V4 Pro?
Claude 4.1 Opus advantages
- No decisive advantage on the tracked metrics.
DeepSeek V4 Pro advantages
- General intelligence (+32%)
- Context window (+74%)
- Affordability (+89%)
- Output speed (+52%)
Which should you choose?
- Choose the DeepSeek V4 Pro if you need the strongest overall reasoning and accuracy.
Value for money
DeepSeek V4 Pro offers more intelligence per dollar (13.7× 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.
Claude 4.1 Opus vs DeepSeek V4 Pro: which should you choose?
Claude 4.1 Opus — Anthropic multimodal model with an Intelligence Index of 30, a 262K-token context window and a blended price of $1.68/1M tokens.
DeepSeek V4 Pro — DeepSeek text model with an Intelligence Index of 44, a 1M-token context window and a blended price of $0.18/1M tokens (open weights).
Claude 4.1 Opus vs DeepSeek V4 Pro: DeepSeek V4 Pro scores higher on the Intelligence Index. DeepSeek V4 Pro leads overall capability (Intelligence Index 44.0 vs 30.0). DeepSeek V4 Pro is the cheaper model to run at $0.18/1M blended tokens — about 9.3× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the DeepSeek V4 Pro scores 44.0 versus 30.0. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The DeepSeek V4 Pro accepts up to 1 million tokens per request, which sets how much documentation, transcript or code it can reason over at once. In measured throughput, DeepSeek V4 Pro generates faster (63 vs 30 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, DeepSeek V4 Pro is the cheaper model to run ($0.18 vs $1.68 per 1M tokens). Claude 4.1 Opus is proprietary api and DeepSeek V4 Pro 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 Claude 4.1 Opus better than the DeepSeek V4 Pro?
DeepSeek V4 Pro is the clearly stronger overall choice, winning most of the dimensions that matter. DeepSeek V4 Pro leads overall capability (Intelligence Index 44.0 vs 30.0).
What is the main difference between the Claude 4.1 Opus and the DeepSeek V4 Pro?
DeepSeek V4 Pro leads overall capability (Intelligence Index 44.0 vs 30.0). DeepSeek V4 Pro is the cheaper model to run at $0.18/1M blended tokens — about 9.3× cheaper.
Which is better value?
DeepSeek V4 Pro offers more intelligence per dollar (13.7× 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 V4 Pro 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.