DeepSeek V4-Pro — Benchmarks, Capabilities, and Use Cases
DeepSeek V4-Pro is a open-weight AI model from DeepSeek (49B/1.6T MoE), classified as Frontier tier with a 1M tokens context window. It is available directly in...
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DeepSeek V4-Flash is an advanced AI model suited for demanding production workloads, developed by DeepSeek and released in April 2026. As an open-weight model, its trained weights are publicly available — you can self-host it, fine-tune it on proprietary data, or run it on-premise without API dependencies. The 1M-token context window is large enough to hold entire codebases, lengthy technical documents, or extended agentic sessions without truncation.
On the FlowHunt AI Leaderboard it is one of 24 tracked models, evaluated across 5 benchmarks. Standout scores: 86.2% on MMLU-Pro (#3 of 8); 91.6% on LiveCodeBench (#3 of 5); 34.8% on HLE (#5 of 6).
DeepSeek V4-Flash was released alongside V4-Pro on April 24, 2026 as the lightweight, low-latency variant of the fourth-generation V series. Using 13B active parameters from a 284B total MoE pool, V4-Flash delivers 91.6% on LiveCodeBench — only 1.9 percentage points behind V4-Pro — at significantly lower per-token inference cost. This near-parity between Flash and Pro on competitive programming reflects DeepSeek’s signature design philosophy: achieve near-flagship performance from smaller active parameter counts by routing tokens through specialised expert networks. V4-Flash targets the high-throughput segment where developers need near-frontier coding capability with API response times measured in milliseconds.
Note: Near-Pro quality at fraction of cost. ~2,500 concurrent requests.
- Released
- April 2026
- Parameters
- 13B/284B MoE
- Context
- 1M tokens
- Weights
- Open
- Tier
- Advanced
- Provider
- DeepSeek
DeepSeek V4-Flash Benchmark Rankings
Rank among all 24 models tracked on this leaderboard. SR = self-reported · 3P / AA / C = independently verified.
| Benchmark | Score | Rank | Source | What it measures |
|---|---|---|---|---|
| SWE-bench Verified | 79.0% | 9 of 13 | SR | Real GitHub issue resolution (500 verified issues) |
| GPQA Diamond | 88.1% | 8 of 14 | SR | Graduate-level Google-proof science Q&A |
| MMLU-Pro | 86.2% | 3 of 8 | SR | Hard knowledge reasoning, 12K questions |
| LiveCodeBench | 91.6% | 3 of 5 | SR | Competitive programming, continuously updated |
| HLE | 34.8% | 5 of 6 | SR | Humanity's Last Exam (50+ STEM disciplines) |
Detailed Benchmark Scores
SWE-V
SR
79.0%
Real GitHub issue resolution (500 verified issues)
GPQA ◇
SR
88.1%
Graduate-level Google-proof science Q&A
LiveCode
SR
91.6%
Competitive programming, continuously updated
HLE
SR
34.8%
Humanity's Last Exam (50+ STEM disciplines)
DeepSeek V4-Flash vs. Advanced Peers
Head-to-head benchmark comparison with other Advanced-tier models. Higher is better for all metrics.
| Benchmark | DeepSeek V4-Flash | Claude Sonnet 4.6 | GPT-4.1 |
|---|---|---|---|
| SWE-V | 79.0% | 79.6% | 54.6% |
| GPQA ◇ | 88.1% | — | — |
| MMLU-P | 86.2% | — | — |
| LiveCode | 91.6% | — | — |
| HLE | 34.8% | — | — |
About DeepSeek
DeepSeek is a Chinese AI research lab founded in 2023 as a subsidiary of High-Flyer, one of China's largest quantitative hedge funds. The lab became widely known in early 2025 when the release of DeepSeek V3 and R1 triggered the largest single-day drop in NVIDIA's market capitalisation to that date, as investors reassessed the capital intensity of frontier model development in light of DeepSeek's reported lower training costs. DeepSeek is unusual among frontier labs in its commitment to releasing open-weight models alongside its proprietary research, and in operating an active social-media presence that critiques closed-model pricing. Its V-series models (V3 through V4-Pro) are widely used as self-hostable coding and reasoning models in the West, despite ongoing regulatory and security scrutiny of Chinese-origin AI models in several jurisdictions.
Use Cases
What to Use DeepSeek V4-Flash For
- Software Engineering
DeepSeek V4-Flash scores 79% on SWE-bench Verified — ranked #9 of 13 models tracked here. It resolves real GitHub issues, writes production-quality code across multiple languages, and handles multi-step agentic coding workflows — a strong choice for AI-assisted software development teams.
- Scientific & Technical Reasoning
DeepSeek V4-Flash scores 88% on GPQA Diamond (#8 of 14 models), around or above the human PhD-expert baseline. It handles demanding scientific reasoning, research literature summarisation, and knowledge-intensive tasks across STEM disciplines.
- Self-Hosted & Private Deployments
DeepSeek V4-Flash is open-weight (13B/284B MoE parameters) — its trained weights are publicly available for download and self-deployment. Run it on your own GPU hardware or private cloud to ensure data never leaves your environment, eliminate per-token API costs at scale, or fine-tune the model on proprietary datasets.
- Long-Document & Codebase Analysis
With a 1M-token context window, DeepSeek V4-Flash can ingest entire software repositories, long legal contracts, book-length research reports, or extensive conversation histories in a single prompt — enabling deep document Q&A, cross-file codebase reasoning, and comprehensive summarisation without chunking heuristics or RAG pipelines.
- Competitive Programming
DeepSeek V4-Flash scores 91% on LiveCodeBench (#3 of 5 models), a contamination-resistant benchmark continuously refreshed with new competitive programming problems. At this level it handles advanced data structures, graph algorithms, and interview-level challenges with high reliability.
Strengths & Limitations
Strengths
- Strong software engineering — 79% SWE-bench Verified
- Advanced scientific Q&A — 88% GPQA Diamond
- Competitive programming — 91% LiveCodeBench
- Self-hosted & data-private deployments (open-weight)
- Very long documents and large codebases (1M context)
Browse the Full AI Model Leaderboard
Compare all 24 models across 11 benchmarks — sortable, sourced, and updated June 2026.
Frequently asked questions
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