Analysis of DeepSeek V4 Flash 0731: Intelligence, Performance, and Cost

Executive Overview

The DeepSeek V4 Flash 0731 (Reasoning, Max Effort) is a high-performance, open weights model released in July 2026. It is designed to balance extreme intelligence with cost-efficiency, positioning itself as a leader among open-source models of its size class.

Summary: This model is a top-tier contender in terms of cognitive ability and is priced very aggressively compared to other open-weight alternatives of similar scale.

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📊 Performance & Ranking Metrics

The following table breaks down how the model ranks against 101 other models in its specific class.

Intelligence Index Deep Dive

The model achieved a score of 50 on the Artificial Analysis Intelligence Index. To put this in perspective:

• Model Score: 50
• Median Score: 25
• Evaluation Cost: The total expenditure to test this model on the index was \72.02$.

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⚙️ Technical Specifications

The DeepSeek V4 Flash 0731 utilizes a Mixture-of-Experts (MoE) style architecture to maintain high intelligence while reducing inference costs.

Model Architecture

Core Details

• Modality:
  • Input: Text
  • Output: Text
• Context Window: $1,000,000$ tokens (roughly equivalent to $\approx 1500$ A4 pages using 12pt Arial font).
• License: MIT
• Weights Availability: Hosted on Hugging Face.

{
  "model_id": "deepseek-v4-flash-0731",
  "version": "Reasoning-Max-Effort",
  "params_total": "284B",
  "params_active": "13B",
  "context_limit": 1000000
}

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💰 Pricing Analysis

The pricing structure is highly competitive, particularly for those utilizing cached prompts.

• Standard Input: \0.14$per 1M tokens (Median:$$0.43$)
• Standard Output: \0.28$per 1M tokens (Median:$$1.20$)
• Cache Hit Discount: The price drops to \0.003$ per 1M tokens, representing a -98% reduction.

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🧠 Intelligence Evaluation Framework

The model was tested using the Artificial Analysis Intelligence Index v4.1, which aggregates results from 9 distinct evaluations.

Evaluation Checklist

• GDPval-AA v2 (Real-world agentic tasks)
• $\tau^3$-Banking (Agentic tool utilization)
• Terminal-Bench v2.1 (Coding terminal agency)
• SciCode (Advanced coding)
• Humanity's Last Exam (Reasoning knowledge)
• GPQA Diamond (Scientific reasoning)
• CritPt (Physics-based reasoning)
• AA-Omniscience (Accuracy & Hallucination)
• AA-LCR (Long context reasoning)

Additional Benchmarks

Beyond the core index, the model is measured against:

• Agentic Work: AA-Briefcase, AutomationBench-AA, EnterpriseOps-Gym-AA.
• Specialized Fields: Harvey LAB-AA (Legal), ITBench-AA (Kubernetes root-cause).
• General Capability: IFBench (Instruction following), APEX-Agents-AA (Long-horizon tasks), and MMMU-Pro (Visual reasoning).

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🔍 The AA-Omniscience Metric

The AA-Omniscience Index is a specialized measurement of knowledge reliability. It is calculated based on:

1. Accuracy: Rewarding correct responses.
2. Hallucination Rate: Penalizing false information.
3. Refusal: No penalty for admitting ignorance.

The score is expressed as a range: $-100 \leq \text{Score} \leq 100$ Where $0$ indicates an equal balance of correct and incorrect answers.