Deepseek R1 vs Deepseek V3.1

Among the leading contenders in this space are DeepSeek R1 and DeepSeek V3.1, two powerful models that have garnered significant attention for their capabilities and performance.

The evolution from DeepSeek R1 to DeepSeek V3.1 represents a significant strategic pivot by DeepSeek AI, moving from a specialized, research-driven model focused on elite reasoning to a unified, market-ready hybrid platform engineered for versatility, efficiency, and economic disruption. DeepSeek R1 established a new frontier in open-source AI, demonstrating that complex reasoning capabilities, once the domain of closed-source leaders, could be unlocked through innovative, large-scale Reinforcement Learning (RL) techniques. It was a testament to methodological prowess.

DeepSeek V3.1, in contrast, is the productization of that prowess. Built upon the same powerful Mixture-of-Experts (MoE) foundation, V3.1 integrates R1's reasoning capabilities as a faster, more efficient, and user-selectable "Thinking" mode within a single, unified architecture. This consolidation addresses the operational friction of maintaining separate models and streamlines the developer experience.

The key differentiators marking this evolution are stark. V3.1 doubles the context window to a massive 128K tokens, enabling a new class of long-form analysis tasks. It demonstrates not just comparable, but in many cases superior, performance on the very math and coding benchmarks that were R1's signature strengths. Most critically, V3.1 introduces a dramatic leap in agentic capabilities—excelling at tool use, web browsing, and code execution tasks where R1 was comparatively weak.

This entire package is delivered with a unified API and a pricing structure that strategically lowers the cost of advanced reasoning, encouraging the adoption of its hybrid paradigm. The analysis indicates that DeepSeek is leveraging a tripartite strategy—open-source accessibility, elite performance, and radical cost-efficiency—to challenge established market leaders. Furthermore, the model's deep optimizations for low-precision FP8 computation signal a deliberate, software-led approach to building a technologically sovereign AI ecosystem, mitigating reliance on foreign hardware and charting a new course for AI development. DeepSeek V3.1 is not merely an upgrade; it is the maturation of a research breakthrough into a formidable and disruptive commercial platform.

Foundational Architecture: A Mixture-of-Experts (MoE)

The architectural lineage of both DeepSeek R1 and DeepSeek V3.1 traces back to a common, highly advanced progenitor. This shared foundation is key to understanding their capabilities, as their primary differences arise not from fundamental structural divergence but from specialized post-training and refinement.

2.1 The Common Progenitor: DeepSeek-V3-Base

Both R1 and V3.1 are post-trained evolutions of the DeepSeek-V3-Base model, a formidable Transformer-based, decoder-only architecture. The defining characteristic of this base model is its sophisticated Mixture-of-Experts (MoE) framework, which is engineered to balance massive scale with computational efficiency.

Mixture-of-Experts (MoE) Framework: The V3-Base model contains a total of 671 billion parameters, a scale that places it among the largest models in the world. Some sources cite a total size of 685B parameters, which includes an additional 14B-parameter Multi-Token Prediction (MTP) module designed to accelerate inference. The critical innovation of the MoE design is its sparse activation. For any given input token, the model's routing mechanism activates only
37 billion parameters—approximately 5.5% of the total. This allows the model to possess a vast repository of knowledge and specialized "experts" while keeping the computational cost of inference comparable to a much smaller dense model, a core tenet of DeepSeek's efficiency-first philosophy.
Architectural Innovations: The V3-Base architecture inherits and refines several key technologies from previous DeepSeek models to maximize performance and efficiency:
- Multi-head Latent Attention (MLA): An advanced attention mechanism that moves beyond simple token-to-token comparisons. MLA allows the model to focus on more abstract, latent representations of meaning within its hidden states, contributing to more efficient and effective inference.
- DeepSeekMoE: The specific MoE implementation includes a pioneering auxiliary-loss-free strategy for load balancing. In many MoE models, an auxiliary loss function is required to ensure that input tokens are distributed evenly across the different experts, preventing a few experts from becoming over-utilized while others are idle. However, this auxiliary loss can sometimes degrade overall model performance. DeepSeek's approach achieves balanced expert utilization without this trade-off, preserving the model's full capabilities.

This "common chassis" strategy is a cornerstone of DeepSeek's development model. The enormous computational expense of pre-training a model of this scale (the V3 base model required 2.664 million H800 GPU hours) is treated as a foundational investment. From this single, powerful asset, specialized models like R1 and V3.1 can be developed through comparatively less expensive post-training phases. This maximizes the return on the initial pre-training cost and enables rapid iteration and product diversification. The core difference between the models, therefore, lies less in their "hardware" (the architecture) and more in their "software" (the training they receive).

2.2 Divergence in Context Length: A Generational Leap

While sharing a common architectural core, a primary point of divergence between R1 and V3.1 is the context window—the amount of information the model can process at once.

DeepSeek R1: This model was released with a 64K token context window. At the time of its release, this was a substantial capacity, sufficient for a wide range of complex, multi-step reasoning problems.
DeepSeek V3.1: This model features a 128K token context window, doubling the capacity of its predecessor. This expansion is not merely an incremental update but a fundamental enhancement of the model's capabilities.

This leap to a 128K context window positions V3.1 as a next-generation tool aimed squarely at high-value enterprise and research use cases that were previously challenging. The ability to process the equivalent of a full-length novel or an entire research report in a single pass unlocks applications in whole-book comprehension, comprehensive legal document analysis, and large-scale codebase refactoring. This makes V3.1 not just a more efficient reasoner than R1, but a fundamentally more capable model for a different and more demanding class of problems, justifying its role as a successor.

Table 1: Architectural Specification Comparison

Table 1: Architectural Specification Comparison

Divergent Paths in Training and Specialization

The distinct identities of DeepSeek R1 as a reasoning specialist and DeepSeek V3.1 as a hybrid generalist were forged through fundamentally different training philosophies and post-training pipelines. R1's development was a research-intensive endeavor to create reasoning from first principles, while V3.1's training was a product-focused effort to scale context and package capabilities for maximum utility.

3.1 DeepSeek R1: Incentivizing Reasoning with Large-Scale Reinforcement Learning

The training of DeepSeek R1 was a landmark achievement in applying RL to elicit advanced cognitive behaviors in LLMs. The process was anchored by a groundbreaking experiment and a novel, cost-efficient algorithm.

The R1-Zero Experiment: The foundation of R1's reasoning ability was established with DeepSeek-R1-Zero. This model was a proof-of-concept trained directly via large-scale RL on the V3-Base model, crucially without any preliminary Supervised Fine-Tuning (SFT). This was the first open research to demonstrate that sophisticated reasoning patterns—such as generating long Chain-of-Thought (CoT), performing self-verification, and engaging in reflection—could emerge autonomously, incentivized purely by RL signals.
Core Algorithm - Group Relative Policy Optimization (GRPO): To make large-scale RL economically viable, DeepSeek employed GRPO. Unlike traditional RL methods like PPO that require a separate "critic" model to estimate the value of actions, GRPO is a "critic-less" algorithm. It works by sampling a group of potential outputs for a given prompt, scoring each output with a reward function, and then optimizing the policy by comparing each output's score to the average score of the group. By eliminating the need to train and maintain a critic model, GRPO drastically reduces training costs and simplifies the overall pipeline.
Rule-Based Reward System: The rewards for GRPO were provided by a deterministic, rule-based system, which circumvents the "reward hacking" problem where a neural reward model might be tricked by outputs that are superficially correct but substantively flawed. The rewards were simple and direct:
- Accuracy Reward: Correctness was verified using objective tools like mathematical equation solvers for math problems or code compilers and test cases for programming tasks.
- Format Reward: The model was rewarded for correctly structuring its reasoning process within designated <think> and </think> tags, encouraging the explicit generation of a visible CoT.
The Full Multi-Stage R1 Pipeline: While R1-Zero proved the concept, its outputs suffered from poor readability and language mixing. To create the polished, final
DeepSeek-R1 model, a more complex four-stage training pipeline was implemented :
1. Cold-Start SFT: A small, high-quality dataset of human-refined CoT examples was used to fine-tune the base model. This provided a stable starting point for RL and addressed the initial readability issues.
2. First RL Stage (Reasoning-Oriented): The core reasoning capabilities were developed by applying GRPO with the rule-based reward system to the cold-started model.
3. Second SFT Stage (Generalization): To broaden the model's skills beyond pure reasoning, a new SFT dataset was created. This was done by performing rejection sampling on the outputs of the RL-trained model to select only the highest-quality reasoning traces, which were then combined with supervised data for other tasks like writing and factual Q&A.
4. Second RL Stage (Alignment): A final RL stage was conducted to align the model with human preferences for helpfulness and harmlessness. This stage used a hybrid reward system: rule-based rewards for reasoning tasks and neural reward models for general conversational tasks.

3.2 DeepSeek V3.1: Scaling for Context and Unifying for Utility

In contrast to R1's intricate, research-driven pipeline, the training for V3.1 was a more direct and product-oriented process focused on scaling existing capabilities and adding new, high-demand features.

Continued Pre-training for Long-Context Mastery: The most resource-intensive part of V3.1's development was a massive continuation of the pre-training phase on the V3.1-Base model. This effort was singularly focused on extending the context window to 128K. It involved training on an additional 839 billion tokens of data, sourced from newly collected long documents. This was broken into two phases: a 32K extension phase trained on 630B tokens (a 10-fold increase over the V3 model's extension) and a 128K extension phase trained on 209B tokens (a 3.3x increase).
Post-Training for Hybridity and Agency: The defining features of V3.1 were cultivated during a targeted post-training phase:
- Hybrid Thinking Mode: V3.1 was trained to support both "Thinking" and "Non-Thinking" modes within a single set of model weights. This is a crucial innovation for usability and efficiency. The mode is not selected via a separate model but is controlled entirely by the user through the chat template. Including a <think> token in the prompt prefix activates the deliberative reasoning mode, while its omission results in a faster, more direct response.
- Optimization for Tool Calling and Agents: The model underwent a specific post-training optimization process to significantly enhance its performance on tool use and agentic tasks. This was a clear strategic focus to make V3.1 a more practical and powerful tool for building complex, automated workflows.
Pioneering FP8 Training at Scale: A key enabler of efficiency for the entire V3 series, including V3.1, is the use of the UE8M0 FP8 scale data format for both model weights and activations during training. This 8-bit floating-point format reduces the memory footprint and accelerates computation on compatible hardware. DeepSeek was the first to validate the feasibility and effectiveness of FP8 training on a model of this massive scale, a critical innovation for cost-effective development and deployment.

The two training pipelines reveal a clear strategic evolution from research to product. R1's complex, multi-stage process was designed to answer a fundamental question: how can we create an elite reasoning model using RL? It was about forging the capability. V3.1's more direct pipeline—continue pre-training for a key feature (long context) and then post-train for other market-demanded features (hybridity, tool use)—suggests that the core reasoning capability is now considered a solved problem internally. The focus shifted from creating the capability to packaging and delivering it in the most efficient, versatile, and user-friendly form possible. This reflects a maturation from a research-first to a product-first mindset.

This product-centric approach is further evidenced by the unification of the API. Previously, developers had to choose between the deepseek-chat endpoint for general tasks and the more expensive deepseek-reasoner endpoint for R1's capabilities. This created complexity for developers and a higher operational burden for DeepSeek. By consolidating these functions into a single, mode-switchable model, V3.1 simplifies the user experience, streamlines the API, and reduces the total cost of ownership for both DeepSeek and its users, a hallmark of a mature and scalable product strategy.

Performance Analysis: Benchmarking the Specialist against the Hybrid

A critical question for any successor model is whether it maintains or surpasses the core strengths of its predecessor. The comprehensive benchmark results for DeepSeek V3.1 demonstrate that its hybrid architecture is not a compromise. Instead, it represents a significant evolution, successfully integrating and even improving upon R1's elite reasoning capabilities while simultaneously introducing state-of-the-art performance in areas of previous weakness.

The performance of V3.1 is best understood by examining its two distinct operational modes: "Thinking" mode, which engages in longer, deliberative Chain-of-Thought similar to R1, and "Non-Thinking" mode, which provides faster, more direct responses.

4.1 General Reasoning and Knowledge (MMLU, GPQA)

On broad benchmarks measuring general knowledge and problem-solving, V3.1-Thinking performs on par with the highly specialized R1.

MMLU-Redux/Pro: V3.1-Thinking achieves scores of 93.7 and 84.8, respectively, which are directly comparable to R1-0528's scores of 93.4 and 85.0. This indicates a successful transfer of the vast knowledge base and general reasoning ability into the new hybrid framework.
GPQA-Diamond: On this benchmark for graduate-level questions, V3.1-Thinking scores 80.1, a slight regression from R1-0528's 81.0. This minor dip may represent a small trade-off in handling extremely niche, difficult reasoning problems in exchange for the model's greatly expanded versatility and agentic skills.

4.2 Mathematical and Logical Prowess (AIME, HMMT)

In the domain of complex mathematical reasoning, which was the hallmark of R1's capabilities, V3.1-Thinking demonstrates a clear and significant improvement.

AIME 2024/2025: On the American Invitational Mathematics Examination benchmarks, V3.1-Thinking scores 93.1 and 88.4, respectively. This consistently outperforms R1-0528, which scored 91.4 and 87.5.
HMMT 2025: A similar pattern is evident on the Harvard-MIT Mathematics Tournament benchmark, where V3.1-Thinking's score of 84.2 is substantially higher than R1-0528's 79.4.
These results are particularly noteworthy, as they show that the hybrid model not only preserved but actively enhanced the specialized reasoning strength of its predecessor.

4.3 Coding and Software Engineering Acumen (LiveCodeBench, SWE-Bench, Aider)

V3.1 exhibits superior performance across a range of coding tasks, from competitive programming to practical software engineering.

LiveCodeBench & Aider-Polyglot: In tasks related to competitive programming and code assistance, V3.1-Thinking (74.8 and 76.3) again edges out R1-0528 (73.3 and 71.6).
SWE-Bench Verified (Agent mode): This benchmark, which tests the ability to autonomously resolve real-world GitHub issues, reveals a massive leap in capability. The V3.1-NonThinking mode achieves a score of 66.0, dramatically surpassing R1-0528's 44.6. This highlights the success of V3.1's targeted post-training for agentic coding tasks and the effectiveness of its faster, non-deliberative mode for such execution-oriented problems.

4.4 Agentic Capabilities (BrowseComp, Terminal-bench)

The most significant performance gains for V3.1 are seen in agentic tasks that require interaction with external tools and environments.

BrowseComp (Web Navigation): V3.1-Thinking's score of 30.0 represents a more than 3x improvement over R1-0528's 8.9, indicating vastly superior capabilities as a web-browsing agent.
Terminal-bench (Command Line Operation): V3.1-NonThinking achieves a score of 31.3, a more than 5x improvement over R1-0528's 5.7. This cements V3.1's status as a far more capable and practical model for building autonomous agents that can operate in digital environments.

The benchmark data overwhelmingly refutes the notion that creating a hybrid model necessitated a compromise in elite performance. V3.1-Thinking not only matches but often exceeds R1's performance in R1's own areas of strength, such as mathematics and complex coding. Concurrently, V3.1 establishes new state-of-the-art capabilities in agentic domains where R1 was comparatively weak. This indicates that V3.1 is not a simple merger of previous models but a true evolutionary step, where the reasoning engine has been refined and the agentic framework has been built out to a new level of maturity.

Furthermore, the performance split between the "Thinking" and "Non-Thinking" modes appears to be a deliberate and sophisticated design choice. The "Thinking" mode, with its longer CoT, excels at problems requiring deep, multi-step deliberation, such as solving math competition problems. The faster, more direct "Non-Thinking" mode excels at agentic tasks that involve executing a sequence of tool calls or commands, such as fixing a software bug or running terminal commands. This bifurcation allows developers to select the optimal operational mode for the task at hand—deep deliberation for problem-solving and rapid execution for tool-based agency—offering a level of flexibility and optimization that a single-mode model cannot.

Table 2: Comprehensive Performance Benchmark Results

Note: Higher scores are better for all metrics. Best scores for each benchmark are in bold.

Economic and Operational Analysis: Cost, Efficiency, and Inference

Beyond raw performance, the practical viability of a large language model is determined by its cost and operational efficiency. DeepSeek has made radical cost-effectiveness a central pillar of its strategy, and the evolution from R1 to V3.1 reflects a maturation of this approach, focusing on simplifying the pricing structure, enhancing inference speed, and leveraging architectural innovations to drive down costs.

5.1 API Pricing Structure and Evolution

The release of V3.1 prompted a significant and strategic overhaul of DeepSeek's API pricing, moving from a bifurcated model to a unified structure.

DeepSeek R1 (Legacy Pricing): R1 was accessible via the deepseek-reasoner API endpoint. Its pricing was set at $0.55 per 1 million input tokens (for a cache miss) and $2.19 per 1 million output tokens. This was significantly more expensive than the standard
deepseek-chat endpoint, creating a cost barrier for tasks requiring advanced reasoning.
DeepSeek V3.1 (Unified Pricing): With the launch of V3.1, DeepSeek announced a new, consolidated pricing model scheduled to take effect in September 2025. Under this new structure, both the
deepseek-chat (Non-Thinking mode) and deepseek-reasoner (Thinking mode) endpoints, which now point to the same V3.1 model, will converge on a single price: approximately $0.56 per 1 million input tokens and $1.68 per 1 million output tokens.

This pricing change is strategically significant. While it represents a price increase for basic chat, it constitutes a 23% price reduction for the output tokens of the high-end reasoning mode. This is not merely a simplification but a deliberate economic incentive. By removing the cost penalty associated with invoking the "Thinking" mode, DeepSeek encourages developers to build more sophisticated, hybrid applications that can dynamically switch between fast and deliberative modes without complex cost calculations. This move is designed to accelerate the adoption of the advanced agentic and reasoning paradigms that V3.1 enables.

5.2 Inference Efficiency and Speed

DeepSeek V3.1's cost-effectiveness is underpinned by a suite of architectural and software optimizations designed for high-speed, low-cost inference.

Architectural Efficiency: The core driver of efficiency remains the MoE architecture, which keeps computational overhead low by activating only a small fraction (~5.5%) of the model's total parameters for any given token.
Hardware and Software Optimizations:
- FP8 Precision: The training and deployment of V3.1 using the UE8M0 FP8 data format is a critical efficiency measure. This low-precision format significantly reduces the model's memory footprint and accelerates mathematical computations on compatible hardware, such as NVIDIA's H200 GPUs and, crucially, upcoming domestic Chinese semiconductors.
- Multi-Token Prediction (MTP): The V3 architecture's MTP module is designed to work in concert with speculative decoding techniques. By predicting multiple tokens ahead simultaneously, it can substantially reduce the wall-clock time required to generate long sequences of text, lowering latency for the end-user.
Comparative Speed: A key advantage of V3.1 is its improved speed. The company explicitly claims that V3.1's "Thinking" mode achieves comparable or superior answer quality to R1 while responding more quickly and using fewer tokens to reach a conclusion. This suggests significant algorithmic refinements that allow the model to reason more efficiently, a critical factor for real-time applications.

5.3 Training Cost and Overall Economic Impact

DeepSeek's entire development philosophy is built on achieving elite performance at a fraction of the cost of its competitors.

Low Training Cost: While specific figures for R1 and V3.1 are not fully disclosed, the V3 base model was trained for a remarkably efficient 2.788 million H800 GPU hours. Other estimates placed R1's training cost at just $6 million, compared to $100-200 million for competing models. This efficiency in R&D translates directly into lower operational costs and more aggressive API pricing.
Market Disruption: The result is a pricing model that is radically disruptive. At its launch, R1 was cited as being up to 27 times cheaper than OpenAI's o1 model , and the V3 series has been benchmarked as 18 to 36 times cheaper than GPT-4o for equivalent tasks. This combination of top-tier performance and rock-bottom pricing exerts immense pressure on the business models of established closed-source providers and is a cornerstone of DeepSeek's competitive strategy.

This relentless focus on efficiency, particularly the optimization for FP8 and support for domestic hardware, also has a clear geopolitical dimension. In the face of US restrictions on advanced GPU exports to China, the ability to extract maximum performance from available or domestically produced hardware is paramount. DeepSeek's software-led approach, co-designing models for specific hardware capabilities like FP8 support on upcoming Chinese chips, represents a viable strategy for building a technologically sovereign and globally competitive AI ecosystem. Efficiency is thus not just a commercial advantage but a strategic necessity.

Table 3: API Pricing and Efficiency Metrics

Synthesis and Strategic Implications

The transition from DeepSeek R1 to V3.1 is more than a technical iteration; it is a clear articulation of DeepSeek AI's strategy and a bellwether for broader trends in the artificial intelligence industry. The analysis of their architecture, training, performance, and economics reveals a multi-layered approach aimed at technological leadership, market disruption, and strategic independence.

6.1 The Triumph of the Hybrid Model

DeepSeek V3.1's success validates the move towards unified, hybrid architectures. By demonstrating that a single model can not only match but improve upon a specialized predecessor's core strengths while simultaneously introducing new state-of-the-art capabilities, DeepSeek has provided a powerful case study against the need for separate, task-specific models. This approach offers superior flexibility to developers and significantly reduces the operational and financial overhead for providers. This trend towards integrated, mode-switching models that can fluidly move between rapid response, deep reasoning, and agentic execution is likely to become a dominant paradigm in the next generation of LLMs.

6.2 DeepSeek's Competitive Moat: Open Source, Elite Performance, and Radical Low Cost

DeepSeek is executing a classic disruptive strategy with precision. Its competitive moat is built on three pillars that are difficult for incumbents to replicate simultaneously:

Open Source Accessibility: By releasing model weights under a permissive MIT license, DeepSeek fosters a vibrant ecosystem of developers and researchers who can innovate on, deploy, and fine-tune its models freely, driving rapid adoption and community engagement.
Elite Performance: The rigorous benchmark results show that DeepSeek's open models are not second-tier alternatives but are directly competitive with—and in some cases superior to—the world's leading proprietary models from labs like OpenAI and Anthropic.
Radical Low Cost: The aggressive pricing strategy, made possible by deep innovations in training and inference efficiency, fundamentally alters the economics of deploying high-end AI. This creates immense pressure on the high-margin business models of closed-source competitors and democratizes access to frontier-level capabilities.

6.3 The Software-Led Path to Technological Independence

The development trajectory of DeepSeek V3.1, with its explicit optimization for FP8 data formats and upcoming domestic Chinese hardware, marks a pivotal moment in the global AI landscape. It represents a sophisticated, software-led strategy to achieve technological sovereignty. Rather than being solely dependent on access to foreign-made, top-of-the-line GPUs, this approach demonstrates that advanced software, algorithmic innovations, and hardware co-design can create a highly competitive AI stack. This charts a viable path for a powerful AI ecosystem that is resilient to geopolitical pressures and supply chain disruptions, challenging the long-held assumption that AI leadership is inextricably linked to a single hardware provider.

6.4 Future Trajectory: What to Expect from V4/R2

The rapid evolution from R1 to V3.1 provides a clear signal of DeepSeek's future direction. As the community anticipates the next generation of models, likely to be designated R2 or V4, development will almost certainly continue along three primary vectors :

Algorithmic and Systems Efficiency: Continued refinement of the MoE architecture, low-precision computation, and training methodologies to extract even more performance from a given amount of compute.
Deepening Agentic Capabilities: Moving further into the "agent era" by enhancing the model's ability to perform complex, multi-step tasks, interact with a wider array of tools, and exhibit more autonomous behavior.
Tighter Hardware Co-design: As the domestic Chinese semiconductor ecosystem matures, future DeepSeek models will be even more deeply integrated and optimized for this hardware, solidifying the software-led strategy and reinforcing their path toward a fully independent, high-performance AI infrastructure.