DeepSeek's releases are almost all open weights, from the R1 reasoning line through the V3 and V4 model families to OCR and math specialists. ThursdAI — the weekly AI news podcast hosted by Alex Volkov — has covered 16 DeepSeek releases since Jan 2025, most recently V4 API surge pricing on Aug 20, 2026. Highlights include DeepSeek V4 Pro 0813, DeepSeek-OCR, DeepSeek-R1-0528, DeepSeek R1. 14 of them shipped with open weights. Every entry below has the episode segment where we covered it live, plus primary-source links and key numbers where we have them.
DeepSeek introduces peak/off-peak surge pricing for the V4 API
DeepSeek became the first major lab with time-of-day billing, introducing peak/off-peak surge pricing for the V4 API (live August 16). Peak output pricing runs 4.6x the off-peak rate.
DeepSeek ships its own open-source agent harness, 23K stars in days
Alongside V4 Pro, DeepSeek released its own agent harness on GitHub with a web UI. The repo hit 23K stars within days of release.
DeepSeek V4 Pro 0813 goes GA with MIT-licensed open weights
DeepSeek re-published its flagship V4 Pro weights under MIT license: a 1.6T-parameter MoE with 49B active parameters and a 1M-token context window, priced at $0.435/$0.87 per million tokens. DeepSWE jumps from 12.8 in the V4 preview to 62.7, with Terminal Bench 2.1 at 87.9, though it lands at 54 on the Artificial Analysis leaderboard — DeepSeek's answer to Kimi K3.
DeepSeek V4-Flash enters public beta, beating its bigger sibling on agent benchmarks at $0.14/$0.28
Same 284B/13B-active architecture as the preview with all gains from post-training: 82.7 on Terminal Bench 2.1 (above V4-Pro-Preview), DeepSWE up 7.3 to 54.4, CyberGym 76.7, at $0.14/$0.28 per million tokens with a 1M context. It natively speaks the Responses API protocol with one-click Codex CLI setup. The honest caveat the panel kept: API-only, no weights, no license, so the 'open source DeepSeek' habit doesn't apply yet. Wolfram places it 'Terra level,' second on his Wolfbench; Nisten reports devs delegating 90-95% of tasks to it.
DeepSeek V4: 1.6T MoE with CSA+HCA attention and 1M context
DeepSeek released the V4 paper and models (V4-Pro and V4-Flash on Hugging Face), a 1.6T-parameter MoE featuring CSA+HCA attention that fits 1M tokens of context in just 5.7GB of KV cache. It is possibly the first frontier model trained across multiple datacenters, and DeepSeek is offering API tokens at an 80% discount on already much cheaper pricing.
DeepSeek R1: the open reasoning model that crashed NVIDIA's stock
DeepSeek's open-weights reasoning model dropped January 23rd and matched OpenAI's o1 at roughly 50x cheaper pricing, with an alleged training cost of just $5.5M. It crashed NVIDIA stock 17% — a $560B single-day loss, the largest single-company monetary loss in history — and made Chinese AI a household topic. The crew named it the earthquake that shattered assumptions about who leads AI.
DeepSeek V3.1 Terminus lands amid September's relentless pace
DeepSeek resurfaced in September with V3.1 Terminus, another strong open-weights release that arrived just as the crew was barely keeping up with the weekly firehose. Nisten noted that missing a single week in this period left you completely lost.
DeepSeek V3.2 and V3.2-Speciale post gold-medal reasoning under MIT license
DeepSeek released V3.2 and the reasoning-first V3.2-Speciale, a 685B-parameter MoE under MIT license. Speciale posted gold-medal-level olympiad results and 96% on AIME (versus GPT-5 High at 94%), with V3.2 hitting 73.1% on SWE-Bench Verified. Aggressive pricing around 28 cents per 1M tokens on OpenRouter pushes open models closer to top closed-model capability.
DeepSeek Math V2: 685B open-weights model with IMO gold-level math
DeepSeek surfaced DeepSeek Math V2, a 685B-parameter Apache-2.0 model that reaches IMO gold-level math reasoning. It is the first open-weights math champion at this level, dropped quietly on HuggingFace during the week.
DeepSeek-OCR turns text into compressed vision tokens for massive contexts
DeepSeek open-sourced DeepSeek-OCR, a 3B model (~570M active parameters) that is less an OCR model and more a context-compression breakthrough: it renders text as images, compresses it up to 10x while retaining 97% decoding accuracy (60% even at 20x), and reads it back with a tiny vision decoder. The approach suggests text tokenization is far from optimal and points at vastly cheaper long-context processing; alphaXiv reportedly OCR'd all of arXiv for $1000 versus $7500 with MistralOCR, and a single H100 can process up to 200K pages.
DeepSeek V3.1 Terminus refines agents and bilingual output
DeepSeek released V3.1 Terminus, an update to V3.1 with cleaner bilingual output, stronger agentic tool use, and cheaper long-context handling. The open weights are available on Hugging Face, continuing DeepSeek's cadence of iterative open releases.
DeepSeek drops R1-0528, an updated open reasoning model with big gains
DeepSeek released R1-0528 out of nowhere, an update to their open-weights reasoning model with serious performance jumps: AIME 91, LiveCodeBench 73, and SWE-bench Verified 57.6. They also shipped an 8B distilled version based on Qwen3 that can run on a laptop, keeping it among the best open-weight models available.
DeepSeek silently drops V3-0324, 685B params under MIT license
DeepSeek silently updated their V3 base model with DeepSeek-V3-0324, a 685B parameter MoE released on Hugging Face under the MIT license. This is not R1 (their reasoning model) but the powerful base model R1 was built on, and supposedly the base for a future R2.
DeepSeek open-sources its infra stack during Open Source Week
DeepSeek ran its Open Source Week, releasing a series of production infrastructure repos (including FlashMLA, DeepEP, and DeepGEMM) that power its training and inference stack. The drops gave the open-source community a rare look at the low-level kernels and communication libraries behind DeepSeek's efficient frontier models.
DeepSeek Janus Pro: open multimodal models in 1.5B and 7B
Amid the R1 frenzy, DeepSeek also released Janus Pro, unified multimodal models at 1.5B and 7B parameters that handle both image understanding and image generation. The open release added to DeepSeek's week of dominating AI news headlines.
DeepSeek R1: MIT-licensed open source reasoning model rivals o1
DeepSeek released R1, a state-of-the-art open source reasoning model under a permissive MIT license. It matches or beats OpenAI's o1 on key reasoning benchmarks while being fully open weights, and DeepSeek also shipped a family of distilled smaller models. The show called this the hottest week open source AI has ever had.
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