DeepSeek V3.1 is the most refined release in the DeepSeek V3 family — incremental improvements in instruction following, code quality, and reasoning accuracy that compound into meaningfully better everyday performance.
DeepSeek-V3.1 is an AI model released June 2025 by DeepSeek, a leading organization in large language model research. DeepSeek-V3.1 — next iteration with improved instruction following, better coding, and stronger general capabilities. With 671B total (37B active via MoE) and a 128K tokens context window, DeepSeek-V3.1 is designed to excel across a range of demanding tasks — from software engineering and mathematical problem-solving to multilingual content generation and long-document analysis.
DeepSeek-V3.1 employs a Mixture-of-Experts (MoE) architecture with 671B total (37B active via MoE). In a MoE design, only a subset of "expert" sub-networks activates for each token during inference — DeepSeek-V3.1 has 671B total (37B active via MoE) total parameters but uses only a fraction per token. This allows the model to store significantly more learned knowledge than a comparably-priced dense model. MoE architectures have proven effective at reaching frontier-level capability while keeping serving costs manageable — a key reason DeepSeek-V3.1 can be offered at the scale that platforms like LITHOVEX AI make possible.
As of 2026-10-16, DeepSeek-V3.1 by DeepSeek demonstrates measurable performance on standard AI evaluation benchmarks. On MMLU — which tests world knowledge across 57 academic subjects from elementary to professional level — it achieves 91.7%. For software engineering tasks, DeepSeek-V3.1 scores 93.8% on HumanEval, which tests ability to write correct functions from docstrings. In mathematical reasoning, DeepSeek-V3.1 records 75.3% on AIME 2025. Across all 5 benchmarks tracked, the model shows consistent capability in its primary task domains. These scores place DeepSeek-V3.1 — at 671B total (37B active via MoE) of parameters — in a competitive position within its model class. Note that benchmark scores reflect controlled evaluation conditions; real-world performance varies by task, prompt design, and domain specificity.
The primary strengths of DeepSeek-V3.1 are: DeepSeek's most capable non-reasoning model as of June 2025; 52.3% SWE-bench Verified without chain-of-thought — exceptional software engineering; 68.2% LiveCodeBench — top-tier practical coding; MIT license maintained. These make it particularly well-suited for Enterprise software development requiring high quality at low latency, Document intelligence and knowledge base applications, API providers serving general business customers. Users working on complex, multi-step problems benefit most from DeepSeek-V3.1's ability to maintain coherence across long inputs and produce structured, verifiable outputs.
Like all current AI systems, DeepSeek-V3.1 has limitations: Requires massive GPU infrastructure; Knowledge cutoff June 2025. For production deployments, validating outputs against authoritative sources — especially for domain-specific or time-sensitive information — remains best practice. DeepSeek continues to improve DeepSeek-V3.1 through ongoing research.
DeepSeek-V3.1 is available on LITHOVEX AI with a free account. Unlike direct API access — which requires billing setup, API key management, and per-token charges — LITHOVEX AI provides a seamless browser interface with no setup required, making DeepSeek-V3.1 accessible to students, researchers, developers, and early-stage teams globally.
As of 2026-10-16, DeepSeek-V3.1 by DeepSeek demonstrates measurable performance on standard AI evaluation benchmarks. On MMLU — which tests world knowledge across 57 academic subjects from elementary to professional level — it achieves 91.7%. For software engineering tasks, DeepSeek-V3.1 scores 93.8% on HumanEval, which tests ability to write correct functions from docstrings. In mathematical reasoning, DeepSeek-V3.1 records 75.3% on AIME 2025. Across all 5 benchmarks tracked, the model shows consistent capability in its primary task domains. These scores place DeepSeek-V3.1 — at 671B total (37B active via MoE) of parameters — in a competitive position within its model class. Note that benchmark scores reflect controlled evaluation conditions; real-world performance varies by task, prompt design, and domain specificity.
Mixture-of-Experts: 671B total / 37B active, June 2025 state-of-the-art
The training pipeline for DeepSeek-V3.1 involves multiple stages: large-scale pretraining on Expanded training on V3-0324 base with new data curation to develop broad world knowledge, followed by supervised fine-tuning on instruction-response pairs, and alignment techniques such as RLHF or DPO. Each stage refines the model's helpfulness, accuracy, and safety. DeepSeek's research team — based in China — has optimized DeepSeek-V3.1 for the specific capability profile described above.
Inference efficiency: DeepSeek-V3.1 at 671B total (37B active via MoE) is designed to deliver quality outputs at practical serving speeds. Its 128K tokens context window enables processing entire documents, multi-file codebases, and extended conversation histories — particularly valuable for contract review, technical documentation analysis, and codebase-level AI assistance.
Getting started with DeepSeek-V3.1 on LITHOVEX AI takes under two minutes. Follow these steps:
For best results with DeepSeek-V3.1: provide specific, context-rich prompts; include examples when generating code; specify output format when structure matters; break complex tasks into focused sub-questions.
Production AI services requiring frontier quality with open-source licensing
Advanced software development automation
Complex business document processing and analysis
Multilingual customer service automation
Research assistant for scientific literature
DeepSeek is a Chinese AI research company founded in 2023, backed by High-Flyer — one of China's leading quantitative hedge funds. Despite its brief history, DeepSeek has made an outsized global impact with open-weight releases, particularly the DeepSeek-R1 and DeepSeek-V3 families. The company's research philosophy prioritizes efficiency and transparency: models are released under MIT licenses, and detailed technical reports accompany each release, influencing the broader AI research community. DeepSeek's engineering team demonstrated that frontier-level performance could be achieved at dramatically lower training costs than Western competitors — a revelation that reshaped industry assumptions about AI development economics. As of 2025, DeepSeek's models rank among the most-used open AI models globally.
Funding: Backed by High-Flyer Capital Management
Founded: 2023, headquartered in China.
DeepSeek-V3.1 by DeepSeek competes in a field with several strong alternatives. Below is a detailed side-by-side comparison against commonly listed alternatives, using benchmark data and capability profiles in the catalog.
Both DeepSeek-V3.1 and the models above are available free on LITHOVEX AI — test them side by side on your real tasks before committing to a subscription with any provider.
DeepSeek-V3.1 is released under MIT, which means you can download and run it on your own hardware. Below are the quickest ways to get started.
LITHOVEX AI runs DeepSeek-V3.1 on cloud infrastructure, so you can use it instantly in your browser without downloading anything, configuring drivers, or managing VRAM. Create a free account and start in under 60 seconds.
DeepSeek V3.1 is widely deployed across China, Southeast Asia, Europe, and North America as the go-to open-source frontier model for production AI services. Its MIT license, combined with frontier-tier capability, makes it one of the most consequential open-weight releases of 2025.
DeepSeek-V3.1 by DeepSeek has the following version history and notable updates:
For the complete changelog and latest release notes, visit the official DeepSeek model page.