DeepSeek V4 Flash 0731: Benchmarks, Features, and Inference Speed
DeepSeek V4 Flash 0731: post-training benchmarks across Terminal-Bench (82.7%) and DeepSWE (54.4%), 1M context efficiency, and $0.14/$0.28 token economics.
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DeepSeek V4 Flash 0731 represents a milestone post-training upgrade to DeepSeek’s high-efficiency V4 Flash foundation model. Detailed in DeepSeek’s research paper (DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence, arXiv:2606.19348), this release retains the sparse 284-billion-parameter Mixture-of-Experts (MoE) architecture with 13 billion active parameters per token, while dramatically enhancing agentic execution, terminal commands, and software engineering reasoning.
The update combines frontier-grade tool calling with unmatched token economics: $0.14 per million input tokens, $0.28 per million output tokens, and an ultra-low $0.0028 per million cached input tokens. Explore where DeepSeek models rank on our AI Model Leaderboard and run head-to-head tests in our Compare Arena.
DeepSeek V4 Flash 0731 at a Glance
| Specification | DeepSeek V4 Flash 0731 Specification |
|---|---|
| Release Date | July 31, 2026 |
| Architecture | Sparse Mixture-of-Experts (MoE) |
| Technical Research Paper | arXiv:2606.19348 |
| Total Backbone Parameters | 284 Billion |
| Active Parameters per Token | 13 Billion (4.6% activation sparsity) |
| Context Window | 1,048,576 Tokens (1M Standard) |
| Maximum API Output | 384,000 Tokens |
| Input Price (Cache Miss) | $0.14 per 1M tokens |
| Input Price (Cache Hit) | $0.0028 per 1M tokens (98% discount) |
| Output Price | $0.28 per 1M tokens |
| Execution Modes | Dual: Thinking (Reasoning) + Non-Thinking (Speed) |
| Licensing | Permissive MIT License (Hugging Face) |
Architectural Innovations: Ultra-High Context Efficiency
DeepSeek’s technical report details several foundational engineering upgrades powering the V4 model family:
- Hybrid Attention Architecture (CSA + HCA + DSA): Combines Compressed Sparse Attention (CSA) and Heavily Compressed Attention (HCA) alongside DeepSeek Sparse Attention (DSA). This reduces KV-cache memory demands across 1,000,000 tokens by 90% compared to traditional Multi-Head Attention.
- Manifold-Constrained Hyper-Connections (mHC): Replaces standard residual connections with manifold-constrained paths, improving gradient stability across deep MoE layers.
- Inference Compute Savings: In 1M-token context environments, DeepSeek V4 requires only 27% of the single-token inference FLOPs of previous-generation architectures.
Benchmark Comparison: Calculated Deltas from Preview to 0731
The defining achievement of the 0731 build is its specialized agentic post-training curriculum:
| Benchmark Evaluation | V4 Flash Preview | V4 Flash 0731 | Absolute Delta | Relative Improvement |
|---|---|---|---|---|
| DeepSWE v1.1 | 7.3% | 54.4% | +47.1 pts | +645.2% improvement |
| Terminal-Bench 2.1 | 61.8% | 82.7% | +20.9 pts | +33.8% improvement |
| CyberGym Security | 38.7% | 76.7% | +38.0 pts | +98.2% improvement |
| Toolathlon-Verified | 49.7% | 70.3% | +20.6 pts | +41.4% improvement |
| DSBench-FullStack | 37.0% | 68.7% | +31.7 pts | +85.7% improvement |
| NL2Repo Repository Synthesis | 39.4% | 54.2% | +14.8 pts | +37.6% improvement |
| AutomationBench Public | 10.8% | 25.1% | +14.3 pts | +132.4% improvement |
Methodological Note: Scores represent vendor-verified evaluations. Learn more about evaluation standards in our SWE-bench Verified Explainer.
Key Benchmark Takeaways:
- Transformative SWE Gains: Jumping from 7.3% to 54.4% on DeepSWE indicates that post-training reinforcement learning on execution environments can elevate a lightweight model into a viable autonomous coding engine.
- Terminal Execution Parity: At 82.7% on Terminal-Bench 2.1, Flash-0731 operates within striking distance of Claude Opus 4.8 (85.0%) at roughly 1/50th of the operational cost.
Prompt Caching & Monthly Workload Economics
With cached input billed at $0.0028 per million tokens, developers maintaining persistent context in tools like DeepSeek Harness or Claude Code realize dramatic savings:
| Monthly Developer Workload | Cache-Miss Cost | Cache-Hit Cost (90% Hit Rate) | Total Monthly Expense |
|---|---|---|---|
| 10M Input + 2M Output | $1.40 | $0.20 | $0.76 / mo |
| 100M Input + 20M Output | $14.00 | $1.96 | $7.56 / mo |
| 1B Input + 200M Output | $140.00 | $19.60 | $75.60 / mo |
Final Verdict
DeepSeek V4 Flash 0731 is the gold standard for high-efficiency agentic inference in 2026.
By packaging 82.7% Terminal-Bench capability, 1M context length, open MIT weights, and $0.14 / $0.28 token pricing, DeepSeek enables developers to run high-volume autonomous software engineering pipelines without unsustainable cloud bills.
Explore comparative rankings in our AI Model Leaderboard and review our DeepSeek V4 Flash vs GPT-5.6 Luna Comparison.
Benchmark and pricing data is aggregated from OpenRouter, Artificial Analysis, and models.dev, then scored with the published RankLLMs Index. These are the primary sources behind the numbers in this article.
- •DeepSeek-V4 Technical Research Paper (arXiv:2606.19348)(Primary Source →)
- •DeepSeek API Official Pricing & Documentation(Primary Source →)
- •Hugging Face DeepSeek V4 Model Collection(Primary Source →)
Lucky Yaduvanshi(luckyyaduvanshi.in →)
Founder of RankLLMs • AI Researcher & Software Engineer focusing on LLM benchmarking, DevOps, and autonomous coding agents.
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