Intelligence
41 scoreDeepSeek · Flagships Analysis
DeepSeek V4 Pro (high)
Intelligence, Performance & Price Analysis
Canonical slug: deepseek-v4-pro-high · Canonical model registry at build time
Speed
64.8 output tokens/secLatency
1.66s TTFTInput Price
$0.43 / 1M tokensOutput Price
$0.87 / 1M tokensExecutive Assessment
Routing Verdict & Tradeoffs
Ethen Routing Verdict
Frontier research / complex reasoning
DeepSeek V4 Pro (Reasoning, High Effort) scores 41 (estimated) on the Artificial Analysis Intelligence Index, placing it well above average among other open weight models of similar size (median: 25). DeepSeek V4 Pro (Reasoning, High Effort) generates output at 64.8 tokens per second (based on DeepSeek's API), which is above average compared to other open weight models of similar size (median: 64.8 t/s). DeepSeek V4 Pro (Reasoning, High Effort) costs $0.43 per 1M input tokens (better than average, median: $0.59) and $0.87 per 1M output tokens (very competitive, median: $2.20), based on DeepSeek's API.
Reserve this model for the hardest requests; simpler or repetitive work should stay on cheaper routes.
Task Fit Assessment
| Workload | Rating | Notes |
|---|---|---|
| Complex reasoning & agentic workflows | optimal | Intelligence score 41 places this model among top performers. Suitable for multi-step analysis and agentic loops. |
| High-volume chat & customer-facing | optimal | Output speed 64.8 tokens/sec is adequate for chat. |
| Latency-sensitive applications | optimal | TTFT 1.66s — adequate latency for most interactive use cases. |
| Cost-sensitive pipelines | optimal | Output pricing at $0.87 is reasonable for moderate volume. |
Cost Pressure Analysis
Low
Pricing is competitive — input $0.43, output $0.87. Suitable for sustained production use.
Technical Specifications
Architecture and Limits
| Specification | Value | Validation Authority |
|---|---|---|
| Model type | Open weights | inferred |
| Reasoning | Yes | faq |
| Input modalities | DeepSeek V4 Pro (Reasoning, High Effort) supports text input. | faq |
| Output modalities | DeepSeek V4 Pro (Reasoning, High Effort) supports text output. | faq |
| Context window | DeepSeek V4 Pro (Reasoning, High Effort) has a context window of 1.0M tokens. This determines how much text and conversation history the model can process in a single request. | faq |
| Open weights / source | Yes, DeepSeek V4 Pro (Reasoning, High Effort) is open weights. The model weights are publicly available and can be downloaded for self-hosting. | faq |
| Parameters | DeepSeek V4 Pro (Reasoning, High Effort) has 1.6 trillion parameters (49 billion active). | faq |
| Active parameters | DeepSeek V4 Pro (Reasoning, High Effort) is a Mixture of Experts (MoE) model with 1.6 trillion total parameters, but only 49 billion active parameters are used during inference. | faq |
| License | DeepSeek V4 Pro (Reasoning, High Effort) is released under the MIT license. This license allows commercial use. | faq |
| API availability | Yes, DeepSeek V4 Pro (Reasoning, High Effort) is available via API through 10 providers. | faq |
Evidence charts
Profile visualizations
Charts are the same canonical evidence cards previously published for this slug, contained inside the D18D page grammar.
AA-Omniscience Index
AA-Omniscience Index (higher is better) measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer. Scores range from -100 to 100, where 0 means as many correct as incorrect answers, and negative scores mean more incorrect than correct. · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Bars intentionally show many models at once so the page feels like a benchmark report rather than a dashboard.
Artificial Analysis Intelligence Index by Open Weights / Proprietary
Artificial Analysis Intelligence Index v4.1 incorporates 9 evaluations: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Artificial Analysis Intelligence Index
Artificial Analysis Intelligence Index v4.1 incorporates 9 evaluations: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Artificial Analysis Openness Index: Score
Openness Index assesses model openness on a 0 to 100 normalized scale (higher is more open) · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Bars intentionally show many models at once so the page feels like a benchmark report rather than a dashboard.
Intelligence
Artificial Analysis Intelligence Index · Higher is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Output Speed
Output tokens per second · Higher is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Speed should be read together with latency and cost; fast output does not always mean better user-perceived performance.
Speed
Output tokens per second · Higher is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Speed should be read together with latency and cost; fast output does not always mean better user-perceived performance.
End-to-End Response Time
Seconds to output 500 tokens, including reasoning model 'thinking' time · Lower is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Bars intentionally show many models at once so the page feels like a benchmark report rather than a dashboard.
Latency: Time To First Answer Token
Seconds to first answer token received · Accounts for reasoning model 'thinking' time · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Latency includes thinking / reasoning time when applicable. Lower is better for interactive use cases.
Context Window
Context window: tokens limit · Higher is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Larger context windows enable complex multi-turn tasks but may increase latency and cost for long inputs.
Cost per Intelligence Index Task
Weighted average cost (USD) per Artificial Analysis Intelligence Index task, segmented by token type. Lower is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Ethen Gateway should route away from this model when the task is simple, repetitive, or likely to generate many output tokens.
Cost per Task
Weighted average cost (USD) per Intelligence Index task · Lower is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Ethen Gateway should route away from this model when the task is simple, repetitive, or likely to generate many output tokens.
Cost to Run Artificial Analysis Intelligence Index
Cost (USD) to run all evaluations in the Artificial Analysis Intelligence Index · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Ethen Gateway should route away from this model when the task is simple, repetitive, or likely to generate many output tokens.
Pricing: Cache Hit, Input, and Output
Price (USD per M Tokens) · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Bars intentionally show many models at once so the page feels like a benchmark report rather than a dashboard.
Time per Intelligence Index Task
Weighted average decode time (minutes) per task; excludes TTFT and overhead time · Lower is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Model Size: Total and Active Parameters
Comparison between total model parameters and parameters active during inference · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Larger models generally perform better but cost more to serve. Active parameter count matters for inference efficiency.
Methodology
Methodology & Provenance
This page is rendered from the normalized profile and page JSON for DeepSeek V4 Pro (high).
Benchmark values are preserved as normalized; only layout, disclosure ordering, and typography are adjusted for readability.
Frequently Asked Questions
Model FAQs & Technical Disclosures
DeepSeek V4 Pro (Reasoning, High Effort) was released on April 24, 2026.
DeepSeek V4 Pro (Reasoning, High Effort) was created by DeepSeek.
DeepSeek V4 Pro (Reasoning, High Effort) scores 41 (estimated) on the Artificial Analysis Intelligence Index, placing it well above average among other open weight models of similar size (median: 25).
DeepSeek V4 Pro (Reasoning, High Effort) generates output at 64.8 tokens per second (based on DeepSeek's API), which is above average compared to other open weight models of similar size (median: 64.8 t/s).
DeepSeek V4 Pro (Reasoning, High Effort) has a time to first token (TTFT) of 1.66s (based on DeepSeek's API), which is better than average compared to other open weight models of similar size (median: 2.03s).
DeepSeek V4 Pro (Reasoning, High Effort) costs $0.43 per 1M input tokens (better than average, median: $0.59) and $0.87 per 1M output tokens (very competitive, median: $2.20), based on DeepSeek's API.
DeepSeek V4 Pro (Reasoning, High Effort) costs $0.43 per 1M input tokens and $0.87 per 1M output tokens (based on DeepSeek's API). For a blended rate (7:2:1 cache hit/input/output ratio), this is $0.18 per 1M tokens. Pricing may vary by provider.
Yes, DeepSeek V4 Pro (Reasoning, High Effort) is a reasoning model. It uses extended thinking or chain-of-thought reasoning to work through complex problems before providing an answer.
DeepSeek V4 Pro (Reasoning, High Effort) supports text input.
DeepSeek V4 Pro (Reasoning, High Effort) supports text output.
No, DeepSeek V4 Pro (Reasoning, High Effort) does not support image input. It can only process text.
No, DeepSeek V4 Pro (Reasoning, High Effort) is not multimodal. It only supports text input.
DeepSeek V4 Pro (Reasoning, High Effort) has a context window of 1.0M tokens. This determines how much text and conversation history the model can process in a single request.
Yes, DeepSeek V4 Pro (Reasoning, High Effort) is open weights. The model weights are publicly available and can be downloaded for self-hosting.
DeepSeek V4 Pro (Reasoning, High Effort) has 1.6 trillion parameters (49 billion active).
DeepSeek V4 Pro (Reasoning, High Effort) is a Mixture of Experts (MoE) model with 1.6 trillion total parameters, but only 49 billion active parameters are used during inference.
DeepSeek V4 Pro (Reasoning, High Effort) is released under the MIT license. This license allows commercial use.
DeepSeek V4 Pro (Reasoning, High Effort) achieves a score of 41 on the Artificial Analysis Intelligence Index. This composite benchmark evaluates models across reasoning, knowledge, mathematics, and coding.
Yes, DeepSeek V4 Pro (Reasoning, High Effort) is available via API through 10 providers.
DeepSeek V4 Pro (Reasoning, High Effort) is available through 10 API providers.