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.
RankLLMs independent evaluations verify official benchmarks against reproducible testing suites, community logs, and provider documentation.
- •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 →)
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Lucky Yaduvanshi(luckyyaduvanshi.in →)
Founder of RankLLMs • AI Researcher & Software Engineer focusing on LLM benchmarking, DevOps, and autonomous coding agents.
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