China’s DeepSeek has dominated much of 2025. The release of DeepSeek R1 made it clear that America’s dominance in the AI landscape is about to be challenged, hardly. The recent update to the DeepSeek V3 model, phrased as ‘DeepSeek V3-0324,’ shows that. Rolled out on March 24, 2025, DeepSeek V3-0324 brings in advanced refinements. Let’s look at them and see how well they compare to Claude 3.7 Sonnet.
What Is DeepSeek V3 0324?
DeepSeek V3 0324 is the latest iteration of DeepSeek’s V3 series, a family of LLMs designed to push the boundaries of AI performance while keeping costs low. Released on March 24, 2025, this model builds on the original DeepSeek V3, which debuted in December 2024. Unlike its predecessor, V3 0324 isn’t explicitly branded as a “reasoning” model like DeepSeek R1 (which you can try here), but it’s showing surprising strength across a range of tasks, especially coding and problem-solving.
The model is open-source, meaning anyone can download and run it locally, and it’s available for free via platforms like OpenRouter. For those using its API, pricing is a steal (NOTE: it differs based on the provider): $0.40 per million input tokens and $0.80 per million output tokens. Compare that to Claude 3.7 Sonnet’s $3 per million input tokens and $15 per million output tokens, and you’re looking at a cost difference of up to 18 times cheaper for output. But price alone doesn’t tell the whole story—let’s look under the hood.
Theoretical Foundations: How Does It Work?
DeepSeek V3 0324 is built on a Mixture-of-Experts (MoE) architecture, a design that’s becoming a go-to for efficient, high-performing LLMs. In an MoE setup, the model doesn’t activate all its parameters for every task. Instead, it uses a subset of “experts”—specialized sub-networks—tailored to the input. The original DeepSeek V3 boasts 671 billion total parameters, with 37 billion active per token, and V3 0324 likely sticks to a similar framework, though exact details remain undisclosed.
What sets V3 0324 apart from the original V3 is its enhanced post-training. Posts on X and early reviews suggest DeepSeek scaled up its supervised fine-tuning (SFT) and possibly reinforcement learning (RL) stages, optimizing the model for practical tasks like coding and structured reasoning. This aligns with trends in AI development: rather than just increasing model size, companies are refining how models learn from data to boost real-world utility.
Claude 3.7 Sonnet (try it here), released in February 2025, takes a different approach. Anthropic calls it a “hybrid reasoning model,” blending standard quick-response capabilities with an “extended thinking” mode. This mode lets Claude spend extra time reasoning step-by-step, making it ideal for complex problems. It’s built on Anthropic’s Constitutional AI framework, which prioritizes safety and interpretability, and supports a massive 200,000-token context window—far larger than V3 0324’s 128,000 tokens.
Theoretically, Claude’s hybrid design gives it flexibility, while DeepSeek’s MoE efficiency keeps it lean and fast. But theory only gets us so far—let’s see how they perform in practice.
Benchmark Breakdown: The Numbers Speak
Benchmarks are the gold standard for comparing AI models, and both DeepSeek V3 0324 and Claude 3.7 Sonnet have been put through their paces. Here’s what the data shows, based on available sources and early user reports as of March 25, 2025.
Coding Performance
- DeepSeek V3 0324: Early tests highlight its coding prowess. On Aider’s benchmark, a tool for evaluating AI coding assistants, V3 0324 scores 55%, placing it second among non-reasoning models—right behind Claude 3.7 Sonnet. Users on Reddit’s r/LocalLLLaMA report it generating “cleaner, more efficient, and production-ready code,” especially for front-end and UI development.
- Claude 3.7 Sonnet: Anthropic touts its strength in software engineering, with a top score of 81.2% on SWE-Bench Verified, a benchmark for debugging and fixing complex codebases. Its “extended thinking” mode shines here, allowing it to iterate and refine solutions. It also excels in full software development tasks, completing them 28% faster than competitors with 94% first-pass correctness.
Verdict: Claude 3.7 Sonnet leads in raw coding capability, especially for intricate projects. But V3 0324 holds its own, matching or exceeding Claude in simpler tasks and rivaling specialized reasoning models like DeepSeek R1.
Reasoning and Problem-Solving
- DeepSeek V3 0324: While not a reasoning-focused model, it’s punching above its weight. Posts on X cite a KCORES arena score of 328.3, ranking it third overall—behind only Claude 3.7 Sonnet in thinking mode and Claude 3.5 Sonnet. It’s praised for logical reasoning and complex problem-solving, often outpacing DeepSeek R1.
- Claude 3.7 Sonnet: In extended thinking mode, it scores 78.2% on graduate-level reasoning tasks and 84.8% on specialized tests like International Mathematical Olympiad geometry problems. Without extended mode, it drops to 68%, still solid but less dominant.
Verdict: Claude’s thinking mode gives it an edge in deep reasoning, but V3 0324’s performance is impressive for a non-specialized model, especially at its price point.
General Knowledge and Language
- DeepSeek V3 0324: On the MMLU benchmark (measuring multitask language understanding), the original V3 scored 88.5%. V3 0324 likely improves on this, though exact numbers aren’t out yet. Users note it’s verbose—31.8% more so than the original V3—offering detailed, example-heavy responses.
- Claude 3.7 Sonnet: Specific MMLU scores for 3.7 aren’t public, but Claude 3.5 Sonnet hit 88.7%, and 3.7 is expected to be higher. It shines in instruction-following (93.2%) and multilingual tasks (86.1%), breaking down complex topics clearly.
Verdict: Both models are neck-and-neck in general knowledge, with Claude excelling in clarity and DeepSeek in verbosity.
Math Performance
- DeepSeek V3 0324: Early reviews suggest it’s strong in math, though it lacks the step-by-step transparency of reasoning models like R1. No specific MATH benchmark scores are available yet.
- Claude 3.7 Sonnet: Scores 89.1% on the MATH benchmark in extended mode, but struggles more (around 60%) in standard mode, per user reports.
Verdict: Claude wins in math with extended thinking, but V3 0324’s cost-to-performance ratio makes it a contender.
Comparative Analysis: Head-to-Head
Strengths
- DeepSeek V3 0324:
- Cost: At $0.40/$0.80 per million tokens, it’s a budget champion—up to 53 times cheaper than Claude in some contexts.
- Speed: Generates at ~60 tokens per second, making it snappy for real-time use.
- Open-Source: Free to run locally, a boon for privacy-conscious users or those with hardware.
- Claude 3.7 Sonnet:
- Reasoning: Extended thinking mode tackles complex problems with unmatched precision.
- Coding: Claude 3.7 Sonnet is, simply put, the best coding model for the most part.
- Versatility: Switches seamlessly between fast responses and deep analysis.
- Context: 200,000-token window dwarfs V3 0324’s 128,000, ideal for long documents.
- Polish: Excels in clear, concise communication and instruction-following.
Weaknesses
- DeepSeek V3 0324:
- Context Limit: 128,000 tokens is solid but falls short of Claude’s capacity.
- Verbosity: Over-explains at times, raising token costs for API users.
- No Multimodality: Unlike Claude, it can’t process images.
- Claude 3.7 Sonnet:
- Cost: $3/$15 per million tokens is steep for high-volume use.
- Math Struggles: Standard mode lags in mathematical reasoning.
- Closed-Source: No local hosting option, limiting flexibility.
DeepSeek V3 vs Claude 3.7 Sonnet Use Cases
- Pick DeepSeek V3 0324 if you’re a developer on a budget, need fast coding help, or want an open-source model for local deployment. It’s perfect for startups, hobbyists, or anyone scaling AI workflows cheaply.
- Pick Claude 3.7 Sonnet if you’re tackling complex reasoning, need a polished conversationalist, or work with massive contexts like legal docs or research papers. It’s built for enterprise-grade precision.
Real-World Feedback: What Users Say
On platforms like Reddit and X, users are raving about V3 0324. A Reddit user u/cpldcpu in r/LocalLLaMA said, “DeepSeek-V3-0324 has caught up to Sonnet 3.7 in my book,” citing its coding and reasoning parity at a fraction of the cost. X posts echo this, with @cline noting its integration into coding tools like Cline, calling it “competitive with Claude 3.7 Sonnet” for coding tasks.
Claude 3.7 Sonnet, meanwhile, has a loyal following among developers and businesses. Its extended thinking mode and developer tools like Claude Code (a terminal-based coding agent) make it a favorite for professional workflows. Users praise its ability to “think aloud” and deliver reliable, production-ready solutions.
The Bottom Line
DeepSeek V3 0324 is more than impressive. It matches or exceeds Claude 3.7 Sonnet in coding and holds its own in reasoning and general tasks, all while costing a tiny fraction of the price. For many users, especially those prioritizing affordability and speed, it’s not just “as good”—it’s better value.
Claude 3.7 Sonnet still reigns supreme in raw reasoning power, context handling, and polished outputs. Its hybrid design and enterprise focus make it the go-to for complex, high-stakes applications. If you need the best of the best and can afford it, Claude delivers.
So, is DeepSeek V3 0324 as good as Claude 3.7 Sonnet? Yes, in many practical scenarios—especially coding and cost-sensitive projects. But “as good” depends on your needs. For sheer capability, Claude edges out. For value and accessibility, DeepSeek steals the show. Try DeepSeek models and Claude 3.7 Sonnet with Bind AI and see which fits your world. The AI race just got a lot more exciting.