DeepSeek

DeepSeek has released V4-Flash-Vision-Exp, an experimental multimodal extension of V4-Flash that adds image understanding while preserving text reasoning capabilities. The company claims the model approaches or beats Anthropic's Opus 4.8 on its internal multimodal agent benchmarks.

DeepSeek has released V4 Flash Vision Exp, an experimental vision-enabled variant of DeepSeek V4 Flash 0731 that adds image understanding while matching the base model's text performance. The sparse mixture-of-experts model uses 13B active parameters out of 284B total and supports a 1M token context window.

DeepSeek will raise API output token pricing roughly fourfold starting August 16, introducing peak and off-peak rates for its V4 Pro and V4 Flash models. Despite the increase, DeepSeek remains cheaper than competitors like OpenAI's GPT-5.6 Sol and Moonshot's Kimi K3.

DeepSeek released build V4-Pro-0813 with major agent benchmark gains, open-sourced its Deepseek Harness agent framework under MIT license, and announced API price increases of up to 52% effective August 16.

DeepSeek has released DeepSeek-V4-Pro-0813, a 1.7-trillion-parameter model that supersedes the DeepSeek-V4-Pro preview. The model adds a DSpark speculative decoding module and posts measurable gains on agentic and coding benchmarks, according to DeepSeek's technical report.

DeepSeek has shipped the general availability release of DeepSeek V4 Pro, codenamed 0813, featuring a 1,049,000-token context window. The mixture-of-experts model is priced at $0.43 per million input tokens and $0.87 per million output tokens, and is live now on OpenRouter.

DeepSeek has published a new routing endpoint, deepseek-v4-flash-latest, that always points to the newest model in its V4 Flash family. The endpoint offers a 1,049K token context window and pricing of $0.09/M input and $0.18/M output tokens via OpenRouter.

DeepSeek released V4-Flash 0731, a post-training-only update to its API and open-weights model that lifted Terminal-Bench scores by 25.8 points without changing model architecture or parameter count. The update arrived alongside disclosed sandbox-escape incidents at OpenAI and Anthropic that renewed debate over eval infrastructure and open-weight safety.

DeepSeek has shipped the full release of DeepSeek-V4-Flash-0731, a 284B-parameter model that according to DeepSeek outperforms its own larger V4-Pro (Preview) on agentic and coding benchmarks. Unsloth has published quantized GGUF versions, with lossless 8-bit weights requiring 162GB of storage.

DeepSeek has updated its budget model V4 Flash to version '0731,' pushing its Artificial Analysis Intelligence Index score to 50 — just one point behind OpenAI's GPT-5.6 Luna — while costing an estimated 60 percent less per task. The MIT-licensed model keeps its 284B-parameter architecture but shows major gains in agentic benchmarks and token efficiency.

DeepSeek has released DeepSeek-V4-Flash-0731, a 304-billion-parameter model that supersedes its earlier preview version with what the company describes as substantially enhanced agentic capabilities. According to DeepSeek's technical report, the model outperforms the larger DeepSeek-V4-Pro (Preview) on several coding and agent benchmarks despite a far smaller activated parameter count.

DeepSeek has released V4 Flash 0731, a sparse mixture-of-experts model with 13B active parameters out of 284B total and a 1049K token context window. The model targets coding, reasoning, and agent workflows, priced at $0.14 per million input tokens and $0.28 per million output tokens via OpenRouter.

Unsloth has released optimized GGUF quantizations of DeepSeek-V4-Flash, a 284B parameter Mixture-of-Experts model that activates 13B parameters and supports 1 million token context windows. The Q8 quantization (UD-Q8_K_XL) runs at 162GB with claimed lossless precision, only 7GB larger than the Q4 variant.

Cline, the autonomous AI coding assistant, released v4.0.2 with support for reasoning effort controls on DeepSeek thinking models, including the new 'xhigh' setting. The update also improves the ClinePass provider experience with clearer reasoning controls and model selection.

DeepSeek has released two new MoE models: DeepSeek-V4-Pro with 1.6T parameters (49B activated) and DeepSeek-V4-Flash with 284B parameters (13B activated). Both models support a one million token context window and use a hybrid attention architecture that requires only 27% of single-token inference FLOPs and 10% of KV cache compared to DeepSeek-V3.2.

DeepSeek has released two Mixture-of-Experts models: V4-Pro with 1.6 trillion parameters (49B activated) and V4-Flash with 284B parameters (13B activated), both supporting 1 million token context windows. V4-Pro requires only 27% of inference FLOPs and 10% of KV cache compared to V3.2 at 1M token context, trained on over 32 trillion tokens.

Chunjiang Intelligence has released DeepSeek-V4-Fable, an autonomous agent model designed for offensive security research and CTF challenges. The model, distilled from Claude-5-Fable and built on DeepSeek-V4-Flash, was trained on 80,000 verified CTF trajectories and achieves a 58.7% solve rate across held-out security challenges.

DeepSeek has permanently reduced pricing for its V4 Pro model by 75%, bringing input token costs down to $0.003625 per million tokens from $0.0145. The move makes permanent a promotional discount that was set to expire May 31, 2026.

DeepSeek released V4 in two versions: a 284 billion parameter Flash model and a 1.6 trillion parameter Pro model with 49 billion active parameters. According to DeepSeek, the models use 9.5x-13.7x less memory than V3 through compressed attention mechanisms and FP4/FP8 mixed precision, while supporting a 1 million token context window.

Chinese AI lab DeepSeek has released preview versions of DeepSeek V4 Flash and V4 Pro, mixture-of-experts models with 1 million token context windows. The V4 Pro has 1.6 trillion total parameters (49 billion active), making it the largest open-weight model available, while both models significantly undercut frontier model pricing.