DeepSeek-V3/R1 vs Claude 3.7 Sonnet

• Architecture & Specs: DeepSeek-V3 is a 671B-parameter Mixture-of-Experts (MoE) transformer (37B activated per token) with a novel Multi-head Latent Attention and multi-token-prediction objective​. It’s pretrained on ~14.8 trillion tokens, then fine-tuned (SFT) and RL-tuned. DeepSeek-R1 uses the same 671B MoE base but is further refined via large-scale RL to enhance reasoning​. Both support very long context (128K tokens)​ and text modality. By contrast, Anthropic’s Claude 3.7 Sonnet is a dense transformer (parameters undisclosed, estimated at “100+B”​) built on Claude 3 architecture. Claude 3.7 introduces a hybrid “thinking” mode: a unified model that can output quick answers or engage in visible chain‑of‑thought for extended reasoning​. Claude Sonnet 3.7 supports text, vision, and audio inputs with an extremely long (200K‑token) context window​.

• Performance & Benchmarks: DeepSeek-V3 already matches top models on many tasks (e.g. 88.5% on MMLU, 89.0% on DROP​), and DeepSeek-R1 improves further via RL. For example, DeepSeek-R1 scores 90.8% on English MMLU (vs. 88.3% by Claude 3.5 Sonnet) and 97.3% on MATH-500 (vs. 96.4% by GPT-4o)​. In coding and reasoning, R1 rivals GPT-4-level models: its AlpacaEval win-rate is 87.6% vs. ~57% for GPT-4o-mini and ~52% for Claude 3.5​, and on complex code problems it outperforms OpenAI’s o1-mini​.​

• Capabilities & Features: DeepSeek-V3/R1 specialize in chain-of-thought reasoning with long contexts. R1 in particular generates visible multi-step “thought” before answering (much like Claude’s extended mode), trading speed for accuracy​. They support rich SFT instructions but no built‑in tool use or web browsing (DeepSeek’s system is closed-loop chat). Claude 3.7 supports standard and extended modes: users can toggle “think longer” or even set a token budget for reasoning​. Claude Sonnet also offers specialized safety and alignment (Anthropic’s Responsible Scaling, guardrails against harmful outputs) and multimodal input (it can analyze images, audio, PDFs, etc.)​. By contrast, DeepSeek’s published model only handles text; no public vision or tool-API is announced.

• Availability & Access: DeepSeek-V3 and R1 are open-source (MIT license) and downloadable via Hugging Face​. The chat/API is currently private to DeepSeek’s Chinese app and API, but the code/model weights are public. In contrast, Claude 3.7 is closed-source; access is via Anthropic’s hosted service (Claude.ai and API) or through partners (AWS Bedrock, Google Vertex AI). Claude’s pricing is $3/million input and $15/million output tokens​ (same as Claude 3.5). DeepSeek has no known usage fees (models are self-hosted); hardware costs are borne by the user. DeepSeek-R1 offers no commercial API yet (unless DeepSeek builds one), whereas Claude has enterprise plans. Fine-tuning: DeepSeek models (being open) can be fine-tuned or distilled by anyone; Claude weights are proprietary, so only Anthropic can fine-tune it or offer it as a service.

• Documentation & Research: DeepSeek publishes technical reports and code. An arXiv paper and HF README detail the V3 and R1 designs (MoE, MLA, RL pipeline). The Hugging Face repos include evaluation tables and instructions​. Anthropic has released a blog and a system card for Claude 3.7​【23†(pages 1–2)} describing its philosophy and safety. Notable claims: Anthropic emphasizes Claude 3.7 as their first “hybrid reasoning” model【20†L21-L29】; DeepSeek claims R1 rivals GPT-4 (“OpenAI-o1”) on math/code​. Independent analyses (e.g. industry benchmarks and leaderboards) generally corroborate that Claude 3.7 Sonnet and Google’s Gemini/DeepMind models currently lead closed-source performance, while DeepSeek-V3/R1 set new open-source marks on math and code tasks​.

Note: Datas are from developers and benchmarks; “activated” means tokens per inference.

DeepSeek-V3/R1 vs Google Gemini 2.5 Pro/Flash

• Architecture & Specs: DeepSeek’s MoE approach contrasts with Google’s Gemini, which is believed to be a large dense transformer (likely on order of trillions of params, though unpublished) with integrated “thinking” capability. Gemini 2.5 Pro debuted as Google’s top model (likely an evolution of Gemini Ultra) and supports multiple modalities (text, image, audio, video) natively. Critically, Gemini 2.5 Pro offers a 1,000,000-token context (soon 2M) out of the box​. It’s advertised as “thinking” by default (autonomous CoT reasoning), while a lower-tier Gemini 2.5 Flash presumably provides faster (non-thinking) response. DeepSeek’s context is far shorter (128K)​, and it only handles text. Unique to DeepSeek is its auxiliary-loss-free balancing and MTP training​, whereas Google emphasizes native multimodality and agentic planning.

• Performance & Benchmarks: Google reports Gemini 2.5 Pro as “#1 on LMArena” and SOTA on math/science benchmarks​. Third-party tests find it leads on big-reasoning tasks: e.g. state-of-art on advanced math (AIME 2025) and coding. The DeepSeek R1 performance (e.g. 97.3% on MATH-500​) is comparable to such top models. In codebenchmarks, DeepSeek-R1’s Codeforces percentile (~96.3) rivals scores cited for Gemini (details not published)​. On conversational and multi-turn tasks, public leaderboards place Gemini 2.5 Pro and Claude 3.7 ahead of others, with DeepSeek unranked (private)​. In summary, both DeepSeek-R1 and Gemini 2.5 Pro excel at reasoning and coding; Gemini wins in context length and multimodality, while DeepSeek is competitive on pure reasoning metrics​.

• Capabilities & Features: Gemini 2.5 Pro offers native multi-turn agentic use with tools (via Google Workspace/APIs) and handles text+vision. It “reasons through thoughts before answering” as a built‑in behavior​. DeepSeek-R1 similarly performs RL-driven reasoning, but has no announced plug‑ins or browsing. Both models support chain-of-thought by design. DeepSeek lacks known built-in ethics or guardrail details; Gemini likely inherits Google’s alignment efforts and has specialized tuning (and Google touts its use in real-world apps).

• Availability & Access: Gemini 2.5 Pro is available in Google AI Studio and the Gemini app for “Advanced” users, and coming to Vertex AI​. Pricing hasn’t been fully announced (beyond “coming soon”), but it will likely mirror previous Gemini rates. DeepSeek models are only accessible via the open-source downloads and DeepSeek’s own closed app/API (details scarce). There’s no public “Gemini Flash” tier announced beyond marketing; historically Gemini Flash refers to smaller sibling models (similar to earlier Gemini 1.x). In contrast to DeepSeek’s free weights, Gemini’s models are closed (weights not publicly downloadable) and only usable via Google’s cloud services.

• Documentation & Research: Google’s blog and documentation describe Gemini 2.5’s features (e.g. context window, reasoning), but no paper is released yet​. Benchmarks and claims appear in blog and press. DeepSeek’s documentation (GitHub/HF) explicitly covers its MoE design and RL pipeline​. Gemini’s closed nature means fewer transparency resources; DeepSeek’s open status means the community can inspect and improve the model.

DeepSeek-V3/R1 vs OpenAI o-series (GPT-o3 / GPT-4o-mini variants)

• Architecture & Specs: OpenAI’s newest models (“o-series”) are advanced GPT-4 variants. GPT-o3 (announced Apr 2025) is their largest “reasoning” model (likely MoE with ~1.8T parameters, as rumored from GPT-4) that handles multimodal input (text+image). GPT-4o-mini and mini-high are smaller, faster versions of GPT-4o (released in late 2024), focusing on cost and speed with reduced compute (OpenAI does not disclose exact sizes). These support all ChatGPT tools and vision. DeepSeek’s MoE base (671B) is much smaller. All o-models share up to ~100K context (GPT-4o), not matching DeepSeek’s 128K but larger than many GPT-3.5 models. Unique to OpenAI: the o-models can programmatically use tools (web, Python, etc.) and have “reasoning effort” settings for mini-models​. DeepSeek has no built-in external tool integration.

• Performance & Benchmarks: OpenAI claims o3 achieves new SOTA on hard benchmarks (Codeforces, custom math, MMMU) and especially on visual tasks​. In practice, o3 is expected to marginally beat Claude 3.7 and Gemini on many tasks. DeepSeek-R1 scores (~97–98% on math; ~91% on MMLU​) suggest it is in the same league as GPT-4o. Early evaluations note o3 makes ~20% fewer major errors than OpenAI’s prior “o1” (GPT-4) on hard tasks​. On coding, Symflower’s DevQualityEval reported OpenAI o1 mini (and presumably o3) as top performers, even outpacing DeepSeek-R1 in functional code generation (DeepSeek R1 “disappoints” on that benchmark)​. In informal chat leaderboards, GPT-4o variants consistently top human-preference tests along with Claude and Gemini​.

• Capabilities & Features: OpenAI o3 and o4-mini integrate full tool use: the blog emphasizes they learn when/ how to employ ChatGPT’s web search, Python, browser, and vision tools​. O3 is multimodal by design; o4-mini currently is text-only (no vision support)​. They support custom developer and system messages (like DeepSeek), plus structured output formatting. All “o” models are highly optimized for safety with OpenAI’s RLHF and guardrails. DeepSeek’s alignment details are unspecified (likely basic filters). The main difference: OpenAI models provide built-in chains for image reasoning and agentic actions, which DeepSeek lacks.

• Availability & Access: The o-series are available via OpenAI’s API and ChatGPT interface. GPT-o3 and o4-mini launched in April 2025 for ChatGPT Plus/Enterprise (o3) and are gradually enabled via API (tiered access)​. Pricing: OpenAI charges more for o3 (premium tier) and less for mini variants (they replace o1-mini with o3-mini at similar cost)​. No model weights are released. Fine-tuning is not offered to customers (OpenAI’s APIs are inference-only). DeepSeek models (open-source) can be self-hosted or fine-tuned locally by users under MIT license​, whereas GPT-o weights are proprietary.

• Documentation & Research: OpenAI’s blog posts outline high-level changes​ and developer documentation lists features. Benchmarks (Codeforces, MMMU) are referenced but detailed scores are not published beyond these summaries. DeepSeek’s documentation (papers and readmes) provides exact architecture details and benchmark tables​. OpenAI claims in external repots (and third-party sites) show GPT-o3 dominating benchmarks (e.g. according to Virtualization Review, o3 is a “heavy hitter”)​. Both companies claim strong performance; OpenAI often provides limited details beyond marketing, whereas DeepSeek shares full eval results for transparency.

*†Rumored/estimated, as OpenAI does not disclose.

DeepSeek-V3/R1 vs Meta LLaMA 4 (Scout/Maverick)

• Architecture & Specs: Meta’s Llama 4 is a new MoE-based family. The public models are Scout (17B active, 109B total, 16 experts, 10 million token context) and Maverick (17B active, 400B total, 128 experts, 1M context)​. Both are multimodal (text+image) and multilingual (12 languages)​. Notably, Scout is designed to run on a single GPU despite 10M-token context, thanks to MoE sparsity​. DeepSeek-V3/R1 share the MoE approach but are much larger (671B) with smaller context (128K)​. Llama4’s “Behemoth” (2T params, 288B active) is forthcoming​. All models support text/image inputs.

• Performance & Benchmarks: Meta claimed Llama 4 “bested” GPT-4o on LMArena scores, though this used a special “experimental” chat variant​. In open benchmarks, Scout (despite being only 109B) reportedly matches or beats other small models, while Maverick is competitive with GPT-4o and Claude on reasoning/code​. For example, on math benchmarks, DeepSeek-R1’s ~97–98% MATH accuracy​ is similar to scores suggested by Meta for Maverick. In code, DeepSeek-R1’s Codeforces is 96.3 percentile​.

• Capabilities & Features: Llama 4’s standout is ultra-long context: Scout can handle 10M tokens and Maverick 1M​, far beyond any competitor. They use MoE sparsity (only 16 or 128 experts active) to maintain efficiency. DeepSeek models have shorter context (128K) and no native vision. Llama4 supports text and image (multimodal). Meta emphasizes speed and streaming (speculative decoding) for fast inference​, similar in spirit to DeepSeek’s MLA. Llama4 is mainly a base model (no explicit chain-of-thought RL training announced), unlike DeepSeek-R1’s CoT focus.

• Availability & Access: Meta provides Llama 4 via its licensed channels (weights for research under Llama license). Scout and Maverick are reportedly released to the community (GitHub and Llama.com)​, though with usage restrictions for large companies. DeepSeek is open-source MIT. Both DeepSeek and Llama4 permit local fine-tuning (subject to licenses). Commercial access: DeepSeek has none (aside from self-hosting), while Meta has partnerships (e.g. Meta.ai chatbot). Llama 4 Scout/Maverick appear in Meta’s APIs (e.g. Facebook, Instagram features), offering supervised fine-tuning via its ecosystem, but no official API platform akin to Claude/Gemini yet.

• Documentation & Research: Meta published a blog on Llama 4 (with performance claims) and a system card highlighting context and architectures​. The Wikipedia summary and Llama repository list the specs. DeepSeek docs provide model architectures and full benchmark tables​. Llama4’s release generated media scrutiny (e.g. VentureBeat) over benchmark practices, but the core specs (MoE, param counts) are clear​. Both Meta and DeepSeek emphasize open research: Meta calls Llama4 “open-source” (though partly restrictive) and DeepSeek openly publishes all technical details.

DeepSeek-V3/R1 vs xAI Grok 3 (and Grok 3 mini)

• Architecture & Specs: xAI’s Grok 3 (Feb 2025) is described as a massively scaled transformer (media reports suggest a base ~340B parameters with 128 experts​, though xAI itself reveals little). Grok 3 is trained on Twitter-centric data using Elon’s Colossus supercomputer (10× the compute of prior models)​. Like DeepSeek-R1, Grok 3 emphasizes reinforcement-learned reasoning (“Think” mode). A smaller Grok 3 mini parallels OpenAI’s mini models: it’s a lean variant optimized for STEM tasks​. Neither Grok model supports vision or tools (unlike DeepSeek and others); both are text-only with prompt-based “think” toggles. Context windows are unknown but likely on par with GPT-4 (tens of thousands). Unique: xAI’s branding targets “agentic” use, but currently Grok is only on X (Twitter’s backend), not in a public toolkit.

• Performance & Benchmarks: xAI claims Grok 3 achieves state-of-the-art math and coding scores. In its beta report, Grok 3 achieved 93.3% on AIME-2025 and 84.6% on GPQA-Diamond​, comparable to Claude 3.7. Grok 3 mini scored 95.8% on AIME-2024​, indicating excellent math capabilities. It also reports an Elo of 1402 in Chatbot Arena​, placing it competitive with GPT-4o and above Gemini. By comparison, DeepSeek-R1’s AIME scores (not publicly given) would likely be similar given its 97.3% MATH accuracy​. Independent benchmarks are scarce, but press suggests Grok 3 generally outperforms DeepSeek-R1 on knowledge-based tasks (perhaps reflecting differences in pretraining data). On coding, no direct comparison is public, but DeepSeek’s R1 already rivals GPT-4, so Grok 3 (with RL and huge compute) is expected to be competitive.

• Capabilities & Features: Both DeepSeek-R1 and Grok 3 spend extra “thinking” compute. Grok’s “Think” mode is explicitly exposed to users (on X, one clicks a button to let it chain-of-thought​). Grok mini achieves this at lower cost. Like DeepSeek, Grok 3 reportedly backtracks on errors and refines solutions via RL​. Neither model offers vision or external tools in current form. Grok is unique in being tightly integrated with social media (trained heavily on Twitter data), which may bias its knowledge and style. DeepSeek has a more general web-scale pretraining (14.8T tokens from mixed sources​). Safety: xAI has minimal published safety work (aside from standard filters); DeepSeek’s safety is undeclared.

• Availability & Access: Grok 3 was rolled out as a beta on X.com (formerly Twitter) for a limited preview​. There’s no public API or weight release yet – it’s only usable via Elon’s platform. DeepSeek models are fully open-source. Pricing: Grok’s future pricing (if any) is unknown; currently access is free on X. Fine-tuning: not available for Grok (closed system), whereas DeepSeek can be fine-tuned by anyone.

• Documentation & Research: xAI’s announcement is the main source on Grok 3​. No technical paper has been released; all architecture details are speculative (e.g. tweets about 340B params​). DeepSeek publishes full papers and code​. Independent analysis (e.g. LifeArchitect) is the only source of Grok internals and is not peer-reviewed​. In short, Grok 3 remains opaque (we know it’s huge and RL-trained), while DeepSeek provides transparent specs and benchmarks.

† Parameter estimates from independent analysis​; xAI has not published official specs.

Summary: DeepSeek-V3/R1 holds its own as an open-source reasoning powerhouse (especially in math/code) but is generally outclassed by the latest closed-source frontier models. Claude 3.7 Sonnet, Gemini 2.5 Pro, and OpenAI’s o3 each offer larger models, richer modalities (vision/tools), and comparable or better benchmark scores​. Meta’s Llama 4 boasts revolutionary context lengths (up to 10 million tokens) and strong multimodal performance​. xAI’s Grok 3 achieves leading math/coding scores via massive scale and RL. DeepSeek’s advantage lies in its availability and innovative training (pure-RL reasoning), making it strategically attractive for research and customization. In capability terms, DeepSeek‑V3/R1’s long-context and chain-of‑thought designs push open-source performance, but without the multi-modal and agentic features of the closed models. In deployment, DeepSeek can be fully self-hosted and fine-tuned freely, whereas competitors mainly require cloud APIs or restrictive licenses. All comparisons above use cited evaluations or official claims​, with gaps noted where data is unpublished.