DeepSeekOpen Source

DeepSeek V4.1

Name: DeepSeek V4.1
Author: DeepSeek

The latest foundation model developed by DeepSeek. It achieves higher performance with an improved architecture and optimizations.

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Parameters

1.6

Context Window

License

MIT

Release Date

2026-06-01

Japanese Language Capability

✅High-Quality JP

Multilingual model with strong Japanese language processing capabilities.

API Pricing

API pricing for this model is not yet available

Strengths

Weaknesses

Use Cases

Deep Analysis

Arena Elo

800 (±28)

Per NIST CAISI evaluation, placing it ~8 months behind the US frontier.

SWE-Bench Verified

80.6%

For V4-Pro-Max; V4.1 expected to match or exceed.

Input Price (List)

$1.74/1M tokens

~7x cheaper than Claude Opus 4.7; has a 75% promo rate.

Context Window

1,000,000 tokens

Default across all V4 models via hybrid attention architecture.

License

MIT

Open weights for both Pro (865GB) and Flash (160GB) variants.

Codeforces Rating

3,206

Reported highest rating among evaluated models at release.

Strengths

・Exceptional cost-performance ratio, especially for coding and agentic tasks.
・Open-weight MIT license enables self-hosting, fine-tuning, and sovereign AI deployments.
・Industry-leading efficiency for million-token context via novel hybrid attention (CSA + HCA).

Weaknesses

・Lacks native multimodal (vision, audio) capabilities as of V4; expected in V4.1.
・Trails leading US models on broad world knowledge and abstract reasoning (e.g., SimpleQA, ARC-AGI-2).
・Released as a preview; V4.1 stable version with enterprise features still pending release.

Competitor Comparison

Model	Arena	SWE	GPQA	Price
Claude Opus 4.7	N/A	~64.3% (Pro)	94.2%	$5.00/$25.00
GPT-5.5	N/A	~85%	~93%	$5.00/$30.00
Kimi K2.6 Thinking	N/A	58.6% (Pro)	90.5%	N/A (API)

Overview

DeepSeek V4.1 is the anticipated mid-cycle update to the V4 foundation model series from DeepSeek-AI. Positioned as an incremental but significant upgrade, it is expected to build upon the architectural breakthroughs of V4—such as the hybrid CSA/HCA attention mechanism for efficient million-token context and the Mixture-of-Experts (MoE) design—while addressing key gaps in multimodal support and enterprise tooling. The V4 series, particularly the V4-Pro variant, has established a new standard for open-weight models by achieving frontier-competitive performance in coding and agentic benchmarks at a fraction of the cost of closed models like Claude and GPT. The key innovations of the V4 line, which V4.1 inherits, are centered on extreme efficiency. The model requires only 27% of the inference FLOPs and 10% of the KV cache of its predecessor (V3.2) at the 1M-token context length, enabling practical use of massive context windows. This efficiency translates directly into disruptive pricing, undercutting major Western competitors by an order of magnitude. V4.1 is expected to extend this foundation with multimodal adapters (vision and audio), deeper integration with the Model Context Protocol (MCP) for reliable tool use, and a refined enterprise toolchain for fine-tuning and deployment. Despite its strengths, independent evaluation (e.g., by NIST CAISI) indicates V4 still trails the leading US models by several months on broader, non-public benchmarks covering abstract reasoning and complex knowledge retrieval. Therefore, V4.1's positioning is that of a supremely cost-effective and customizable workhorse for coding, long-context analysis, and high-volume production workloads, rather than an absolute king of all knowledge domains. Its success will hinge on seamlessly filling the multimodal and enterprise gaps while maintaining its legendary price-performance edge.

Benchmarks & Performance

DeepSeek V4-Pro (the foundation for V4.1) demonstrates a bifurcated performance profile: it leads or matches closed-source models on coding and agentic benchmarks but trails on some knowledge and abstract reasoning tasks. The following table summarizes key benchmark scores from DeepSeek's official technical report and independent evaluations. | Benchmark | DeepSeek V4-Pro-Max | Claude Opus 4.6 Max | GPT-5.4 xHigh | Gemini-3.1-Pro High | Notes | | :--- | :--- | :--- | :--- | :--- | :--- | | **SWE-Bench Verified** | 80.6% | **80.8%** | n/r | 80.6% | Effective tie for top spot. | | **Terminal-Bench 2.0** | 67.9% | 65.4% | **75.1%** | 68.5% | GPT-5.5 leads significantly. | | **LiveCodeBench Pass@1** | **93.5%** | 88.8% | n/r | 91.7% | Highest reported score. | | **Codeforces Rating** | **3206** | n/r | 3168 | 3052 | Highest competitive programming rating. | | **GPQA Diamond** | 90.1% | 91.3% | 93.0% | **94.3%** | Trails on graduate-level science reasoning. | | **HLE (Humanity's Last Exam)** | 37.7% | **40.0%** | 39.8% | 44.4% | Trails all frontier closed models. | | **SimpleQA-Verified** | 57.9% | 46.2% | 45.3% | **75.6%** | Significant gap in factual knowledge retrieval. | | **MRCR 1M (MMR)** | 83.5% | **92.9%** | n/r | 76.3% | Strong long-context retrieval, trails Claude. | | **Toolathlon** | 51.8% | 47.2% | **54.6%** | 48.8% | Competitive on tool-use tasks. | *Note: V4.1 scores are not yet published. The above are for V4-Pro. Source: DeepSeek V4 Technical Report (HuggingFace README), NIST CAISI Evaluation.*

Detailed Comparison

**vs. Claude Opus 4.7:** * **Pricing:** DeepSeek V4-Pro's output price ($3.48/1M list) is ~7x cheaper than Claude Opus 4.7's ($25/1M). This is the most decisive advantage. * **Coding & Agentic:** V4-Pro leads on LiveCodeBench (93.5% vs 88.8%) and Codeforces (3206 vs ~3054). It ties on SWE-Bench Verified (80.6% vs ~80.8%). Claude leads significantly on Terminal-Bench 2.0 (69.4% vs 67.9%) and SWE-bench Pro (64.3% vs 55.4%). * **Reasoning & Knowledge:** Claude Opus 4.7 leads on GPQA Diamond (94.2% vs 90.1%), HLE (40.0% vs 37.7%), and has a massive lead on complex tool-use (MCP-Atlas: 77.3% vs 73.6%) and computer use (OSWorld-Verified: 78.0% vs N/A). * **Capabilities:** Claude is a fully multimodal model (up to 3.75MP images), while V4/4.1 is text-only at launch. Claude has mature agentic tooling (task budgets, `/ultrareview` in Claude Code). * **Choice:** Choose V4-1/Pro for **cost-driven, high-volume coding** where 90% of the performance at 15% of the cost wins. Choose Claude for the **absolute top-tier agentic workflows, vision tasks, and complex reasoning** where budget is secondary. **vs. GPT-5.5:** * **Pricing:** Similar story—V4-Pro is dramatically cheaper (~7x on output). * **Coding:** GPT-5.5 leads on Terminal-Bench 2.0 (82.7% vs 67.9%) but V4-Pro leads on competitive programming (Codeforces: 3206 vs 3168). * **Reasoning:** GPT-5.5 generally leads on broad reasoning and knowledge benchmarks (GPQA, HLE). * **Capabilities:** GPT-5.5 is an omnimodal model (text, vision, audio). It also has a mature ecosystem with tools like GPT-5.5 Instant. * **Choice:** GPT-5.5 is the more capable general-purpose frontier model. DeepSeek V4-1/Pro is the **rational cost-cutting alternative for specialized coding and long-context workloads**.

Community Feedback

The developer community reaction to the V4 series (precursor to V4.1) has been a mix of excitement over pricing and nuanced benchmarking: * **Cost Revolution:** Developers running high-volume API workloads have been vocal about the cost savings. Switching from Claude or GPT to DeepSeek V4-Pro is described as 'changing the economics' of AI products, with some reporting 7-10x cost reductions for coding pipelines with negligible quality loss on their specific tasks. * **Independent Validation:** The NIST CAISI evaluation is widely cited as a critical reality check. Researchers appreciate its use of non-public benchmarks and Item Response Theory, providing a more sober estimate of an ~8-month frontier gap. This has tempered the 'DeepSeek beats everyone' narrative with a more nuanced understanding: V4 is the **best *value* model**, not necessarily the absolute **best *performing* model**. * **Architectural Interest:** The hybrid attention mechanism (CSA+HCA) and the efficiency gains for 1M-token context have been studied and praised in the AI research community as a meaningful engineering advance. * **Open-Weight Ecosystem:** The MIT license has spurred immediate fine-tuning efforts and integration into frameworks like LangChain and LlamaIndex. The community is actively exploring its strengths in code and its weaknesses in knowledge for retrieval-augmented generation (RAG). * **Anticipation for V4.1:** There is significant discussion about V4.1's expected multimodal capabilities and MCP upgrades. Enterprise developers are particularly interested in the promised toolchain improvements for fine-tuning and private deployment.

Use Cases

1. **High-Throughput Agentic Coding Pipelines:** Ideal for organizations running thousands of autonomous coding tasks (e.g., bug fixes, test generation, documentation). A team using Claude Code can route a high volume of SWE-Bench-style tickets to DeepSeek V4-Pro at a fraction of the cost, reserving Claude Opus 4.7 only for the most complex, high-stakes refactors. **Example:** A DevOps platform that auto-generates unit tests for PRs can use V4-Pro, reducing monthly AI spend from $10,000 to ~$1,500 with a minimal acceptance-rate drop. 2. **Cost-Sensitive, Long-Context Analysis:** Perfect for legal, compliance, or research workflows that involve analyzing massive documents (contracts, research papers, codebases) within a single prompt. The 1M-token context and aggressive pricing make this feasible. **Example:** A law firm using AI to extract clauses from a 900,000-token merger agreement can use V4-Pro directly, avoiding a fragile RAG pipeline and saving thousands per analysis. 3. **Sovereign / On-Premise AI Deployment:** For organizations in regulated industries (finance, healthcare, government) or with data residency requirements that cannot use Western APIs. The MIT-licensed V4-Flash model (160GB) can be self-hosted on internal GPU clusters. **Example:** A European bank deploys V4-Flash on its private cloud for internal code review and documentation, ensuring all data never leaves its jurisdiction. 4. **Economic Routing in Model Orchestration:** Used as the 'default' model in a smart routing system. A load balancer can classify task complexity—sending simple queries to V4-Flash ($0.28/1M output), most tasks to V4-Pro ($3.48/1M output), and only the most ambiguous or creative tasks to Claude Opus 4.7 ($25/1M output). **Example:** A customer support platform handles 80% of ticket inquiries with V4-Flash, escalates 15% to V4-Pro, and 5% to Claude, optimizing cost vs. quality.

Latest News

1. **V4.1 Imminent Release (Mid-June 2026):** Multiple reports indicate V4.1, a mid-cycle upgrade, is slated for release in mid-June 2026. It will feature multimodal support (images, audio), enhanced Model Context Protocol (MCP) for reliable tool use, and an enterprise toolchain for fine-tuning and private deployment. 2. **Massive Funding Round:** DeepSeek is finalizing a ¥50 billion (~$7.3 billion) external funding round, with founder Liang Wenfeng contributing ¥20 billion. The post-money valuation is ~$51.5 billion. The funds are earmarked for model iteration, compute, and multimodal/AGI R&D. 3. **Permanent 75% Price Cut:** As of May 22, 2026, DeepSeek-V4-Pro API pricing was permanently reduced by 75% from list price. This solidifies its position as the most cost-effective frontier-class model. (List: $1.74 input / $3.48 output; Permanent: ~$0.435 input / $0.87 output per 1M tokens). 4. **Huawei Hardware Support:** Reports confirm DeepSeek's V4 models will run on Huawei's latest Ascend AI chips, diversifying its hardware stack beyond NVIDIA and strengthening China's domestic AI infrastructure.

The key innovations of the V4 line, which V4.1 inherits, are centered on extreme efficiency. The model requires only 27% of the inference FLOPs and 10% of the KV cache of its predecessor (V3.2) at the 1M-token context length, enabling practical use of massive context windows. This efficiency translates directly into disruptive pricing, undercutting major Western competitors by an order of magnitude. V4.1 is expected to extend this foundation with multimodal adapters (vision and audio), deeper integration with the Model Context Protocol (MCP) for reliable tool use, and a refined enterprise toolchain for fine-tuning and deployment.

Despite its strengths, independent evaluation (e.g., by NIST CAISI) indicates V4 still trails the leading US models by several months on broader, non-public benchmarks covering abstract reasoning and complex knowledge retrieval. Therefore, V4.1's positioning is that of a supremely cost-effective and customizable workhorse for coding, long-context analysis, and high-volume production workloads, rather than an absolute king of all knowledge domains. Its success will hinge on seamlessly filling the multimodal and enterprise gaps while maintaining its legendary price-performance edge.

Sources

Analysis generated: 2026-07-17