A collection of 56 posts
DSpark is DeepSeek's open-source speculative-decoding module that makes V4-Pro and V4-Flash 51–400% faster — and it works on Qwen3 and Gemma 4 too. Here's how it works and how to use it.
Two open-weights heavyweights from China go head-to-head for the agentic-coding throne. K2.7 leads on MCP tool-use depth; V4 leads on raw per-token economics and proven independent benchmarks. We break down cost, agentic strength, self-host paths, and pick a winner per workload.
Two open-weights heavyweights from China go head-to-head for the agentic-coding throne. GLM 5.2 leads on context window; DeepSeek V4 leads on token economics. We break down cost, agentic strength, self-host paths, and pick a winner per workload.
DeepSeek made its 75% V4-Pro discount permanent on May 22, 2026. Standing rates: $0.435/M input, $0.87/M output. Here is what changed, the new cost-per-quality math vs Claude Opus 4.7 and GPT-5.5, and the migration code.
What a 4x RTX Pro 6000 Blackwell rig actually buys you for DeepSeek V4 Flash: throughput, VRAM headroom, the SM120 vLLM compile bug, and break-even vs the DeepSeek API.
A single tiered table for DeepSeek V4 Pro vs Flash VRAM at every quantization (FP8, FP4+FP8, INT4, Q2) — triangulated across official and community sources.
Install the Cline agent in Cursor and point it at DeepSeek V4 — real config JSON, Pro vs Flash guidance, the reasoning_content gotcha, and a cost worked example.
Wire DeepSeek V4 Pro or V4 Flash into Cursor as a custom model, handle the Composer reasoning_content caveat, and migrate off the deepseek-chat alias before the July 24, 2026 deadline.
Kimi K2.6 and DeepSeek V4 Pro are the two best open-weights coding models in 2026. K2.6 wins long-horizon agents and swarms; DeepSeek V4 wins on raw price.
DeepSeek V4 Flash is the under-covered story of the V4 release. 1M context, 47 on the AA Intelligence Index, $0.14 input / $0.28 output per million tokens, and it fits on a Mac Studio. Here is the full practical guide.
A deep, engineer-focused comparison of DeepSeek V4 Pro vs DeepSeek V4 Flash: benchmarks, pricing, speed, local deployment, and a decision tree for picking the right variant for your workload in 2026.
DeepSeek V4 launched the same week as GPT-5.5 and GPT-5.5 Pro. We break down the benchmarks, pricing, 1M-context engineering, coding wins, and which model your team should actually deploy.
Eight days apart, Anthropic and DeepSeek shipped the two most consequential AI releases of 2026. Here is the honest, benchmark-backed comparison engineering leaders need before they re-architect their stack.
Quick answer. For pure SWE-bench Pro top score and 1M-context agentic coding, pick Claude Opus 4.7. For longest-horizon swarm runs, pick Kimi K2.6 — open-weight and roughly 8x cheaper. For broad reasoning + Codex/CLI tooling, GPT-5.5. For commodity-priced inference at frontier-adjacent quality, DeepSeek V4 Pro. Choose per workload,
Learn how to run DeepSeek V4 Flash locally with vLLM, hardware requirements, install steps, benchmarks, pricing, and real‑world usage examples.
DeepSeek V4 vs V3.2: correct specs for V4-Pro (1.6T/49B) and V4-Flash (284B/13B), real benchmarks from HuggingFace, updated pricing, API migration deadline, and a clear recommendation.
DeepSeek V4 is out — Pro and Flash tiers, MIT license, 1M context, and pricing that undercuts the frontier by up to 11×. Here's how it stacks up against Qwen3.5, Kimi K2.5, MiniMax M2.7, GPT-5.4, and Claude Opus 4.6.
The DeepSeek API is a two-line drop-in for OpenAI. This guide covers setup, both models, streaming, thinking tokens, function calling, and everything developers need to integrate DeepSeek V3.2 into production apps.
DeepSeek V4 launched April 24, 2026 with V4-Pro (1.6T params) and V4-Flash. Here's everything developers need: specs, benchmarks, pricing, and how to migrate from deepseek-chat.
DeepSeek V4 is officially released. This article covers the real architecture (CSA+HCA, mHC, Muon), verified benchmarks for V4-Pro and V4-Flash, correct model specs, and exact API pricing to start using DeepSeek V4 today.
Quick answer. DeepSeek-OCR runs locally on CPU or modest GPU via Ollama (ollama run deepseek-ocr, requires Ollama v0.13.0+) or direct PyTorch. 16 GB RAM minimum, 32 GB recommended; no GPU is required for small documents. The MIT-licensed model compresses pages roughly 10x while keeping about 97% accuracy on
Unlock DeepSeek V3.2-Exp’s power with sub-10ms semantic search API, scalable data providers, usage patterns, performance benchmarks, and side-by-side comparison with GPT-4o, Claude 3.5, and Gemini.
The rapid evolution of large language models (LLMs) has led to fierce competition between open-source initiatives and proprietary giants. Two of the most advanced models in 2025 are DeepSeek R1-0528, an open-source model from DeepSeek AI, and OpenAI’s O3, a closed-source flagship. Both models are at the cutting edge
Last updated April 2026 — refreshed for current model/tool versions and 2025 ecosystem benchmarks. DeepSeek Prover V2 7B is the most capable open-source formal theorem-proving model at the 7B parameter scale, purpose-built for generating verified proofs in Lean 4. Released in April 2025, it remains the reference deployment target for
Running DeepSeek Prover V2 7B on Windows involves several key steps—ranging from environment preparation to downloading and executing the model. This guide walks you through everything you need to install and run DeepSeek Prover V2 7B on a Windows system effectively. What Is DeepSeek Prover V2 7B? DeepSeek Prover