The Archive

DeepSeek releases V4 Pro (1.6T-A49B) and Flash (284B-A13B) models optimized for Huawei Ascend chips, no longer leading benchmarks.

DeepSeek V4 Flash matches Gemini 3 Flash performance at 20% of the cost, pressuring pricing economics.

DeepSeek just launched its fourth generation of flagship models with DeepSeek-V4-Pro and DeepSeek-V4-Flash, both targeted at enabling highly efficient... DeepSeek just launched its fourth generation of flagship models with DeepSeek-V4-Pro and DeepSeek-V4-Flash, both targeted at enabling highly efficient million-token context inference. DeepSeek-V4-Pro is the largest model in the family, with 1.6T total parameters and 49B active parameters. DeepSeek-V4-Flash is a smaller 284B-parameter model with 13B active parameters, designed for higher-speed… Source

DeepSeek V4 Pro costs 15x more to run than V3.2 on Artificial Analysis benchmarks, exceeding Gemini 3.1 Pro pricing.

Unverified claim of Deepseek V4 AGI confirmation on social media, lacks evidence or official announcement.

DeepSeek v4 Flash outperforms Claude Haiku on tool-calling benchmarks in production chat system evals.

DeepSeek v4 Flash demonstrates strong tool-use accuracy and multi-tool calling in code editing tasks, but trades speed for reasoning depth.

Reddit discussion of DeepSeek V4 capabilities; original Chinese content translated, lacks substantive technical details.

DeepSeek V4 Pro shows weaker-than-expected performance on LMSYS Arena user preference voting, a crowdsourced benchmark distinct from capability measurement.

Technical deep-dive on DeepSeek V4 architecture: hybrid sparse attention, manifold-constrained connections, and FP4 quantization innovations vs. V3.

Reddit discussion thread about Deepseek v4; lacks substantive detail or official announcement.

DeepSeek releases V4-Pro (1.6T params, 49B active) and V4-Flash (284B/13B) with 1M context, largest open-weights models, MIT licensed.

DeepSeek v4 Flash offers competitive pricing for its model size on the official API.

DeepSeek V4 benchmark results released; comparative performance data on frontier model capability.

DeepSeek V4 model released; limited details provided in source.

DeepSeek releases V4 model weights on HuggingFace, expanding open-weights frontier LLM competition.

DeepSeek V4 Flash and standard V4 weights released on Hugging Face, expanding availability for local deployment.

DeepSeek V4 Flash and Pro models now available via API, enabling developers to run open-weights inference at scale.

Moonshot releases Kimi K2.6, an open-weight model claiming performance parity with Claude Opus 4.6.