DeepSeek launches V4-Pro, its most advanced model that rivals Kimi K3 on some benchmarks but is priced much lower, at $0.435/1M input and $0.87/1M output tokens
DeepSeek Harness (dsh) is an open-source agent harness developed by DeepSeek AI.Hugging Face:DeepSeek-V4-Pro-0813 — DeepSeek-V4-Pro-0813 is the official release of DeepSeek-V4-Pro …Blockchain.News:Silicon Data: Closed US Models Lose Token Share to Chinese Open ModelsXinmei Shen /South China Morning Post:DeepSeek's updated V4 Pro AI model struggles on benchmarks, shines in cybersecurityDeepSeek on OpenRouter:DeepSeek V4 Pro 0813 deepseek/deepseek-v4-pro-0813
The Information
Context & Ripple Effects
The quoted V4-Pro rates match the 75% API-price cut DeepSeek made permanent in May, even as the company had signaled substantial AI-service price increases earlier this month. The launch therefore tests whether DeepSeek can pair its flagship model with the low-cost position it had already established.
DeepSeek's earlier open-source V3 release supplied the foundation for a broader open-model push; related coverage now reports closed US models losing token share to Chinese open models. V4-Pro extends that competition into higher-end workloads, though the supplied coverage gives mixed signals on its benchmark performance.
First-order effects
Developers and enterprise buyers can evaluate DeepSeek's most advanced model against Kimi K3 at $0.435 per million input tokens and $0.87 per million output tokens, shifting the immediate comparison from model capability alone to capability at a stated API cost.
Kimi enters a more explicit competitive comparison on selected benchmarks, while DeepSeek gains a flagship offering that related coverage says performs strongly in cybersecurity despite weaker results on some benchmarks.
Second-order effects
AI procurement teams have greater incentive to benchmark V4-Pro on their own workloads, because its low listed token rates make small performance differences more economically consequential.
Providers competing for token volume, including Kimi, face pressure to justify any cost premium with task-specific performance, reliability, or distribution rather than broad benchmark claims.
Third-order effects
If Chinese open models continue gaining token share while offering flagship-tier models at lower API rates, model selection will increasingly be governed by cost per useful task instead of a single frontier-model ranking.
DeepSeek's open-source model releases and open agent harness point toward competition across both models and developer tooling, making ecosystem fit a larger part of model procurement.
The trend: AI model competition is moving toward a cost-per-useful-task contest in which Chinese open-model vendors combine lower token prices with increasingly capable flagship releases.
We're launching DeepSeek-V4-Pro today! 🚀 🔷 Major Agent upgrades with strong production gains! 🔷 Flexible reasoning effort for V4-Pro & V4-Flash: low for simple tasks, high for daily Agent workflows, max for complex tasks. 🔷 Native OpenAI Responses API support, optimized for Co…
🧩 DeepSeek Harness v0.1 is now available in Developer Preview! 🔹 We're opening it up to developers building agent harnesses worldwide and open-sourcing the codebase in MIT license. 🔹 Powered by the Cordis meta-framework, DeepSeek Harness is an agent harness built around one
New DeepSeek V4 0813 dropped on @huggingface , my @bot sent an alert to our slack team, and the team said “he hallucinated the link” But @bot was good! Apparently @deepseek_ai published the weights (with MIT license!) and then took them down!? [image]
new deepseek v4 pro is now open weight on hugging face (mit license) “v4” is a bit misleading, previous model was only a preview and this one has way more training behind it, feels more like a v4.5 also very excited for the deepseek harness release https://huggingface.co/... [ima…
DeepSeek has been very disappointing this year V4 came much later than expected and was smaller than expected. The price hikes also hurt. They are currently 4th or 5th place in China
DeepSeek-V4-Pro GA officially announced! Yesterday's leaked benchmarks were real: The model now supports adjustable reasoning effort: low for simple tasks, high for everyday agent work, and max for complex problems. V4-Flash gets the same control. DeepSeek also added native [imag…
reminder that deepseek made NO announcement for the new V4 Pro anywhere on social media because they are ashamed of it >scores only 1 point higher than V4 Flash [image]
DeepSeek-V4-Pro (Max) by @deepseek_ai is expected to shift the Pareto curve for Code Arena: WebDev with this upcoming open weights model. It currently sits at ~#8 overall (AutoEval) at 1607 pts. Priced at $0.435 input/ $0.87 output per MTokens, it outperforms models beyond its [i…
Really weird things going on with V4-Pro-0813 release I will withhold judgement until they admit it's rolled out and post their own evals in English. Currently, the WeChat leak does not match AA even directionally (-2.7% for Flash, -9% for Pro). I doubt this is their best. [image…
it's not a bad model, it is unfortunately definitely behind the frontier by a solid margin, but it feels a lot better than v4 pro preview to use, definitely past the opus 4.6 to opus 4.7 barrier for long tasks, much more reliable and the model has a good intuition. gpt-shaped.
IMO, they have finally stabilized the new V4 arch and are probably preparing for a larger 3T-4T V4 Max or V5 I don't think it makes a lot of sense to spend resources on V4 Pro post training
Wow. @deepseek_ai 's V4-Pro just launched via API and it looks incredible. This thing costs $0.87/M out. Opus is $25/M out. It's almost 30x cheaper than Opus! Benchmarks looks like it's somewhere between Opus 4.8 and Opus 5 quality. Absolutely incredible deflation in frontie…
DeepSeek V4 Pro 0813 is live on OpenRouter. @deepseek_ai reports large agent gains over V4 Pro Preview: DeepSWE 62.7 (+49.9), CyberGym 83.3 (+30.6), NL2Repo 61.5 (+23.0), and Terminal Bench 2.1 87.9 (+15.8) More providers coming online soon Use it now: https://openrouter.ai/...
1M context and a 384K max output turn DeepSeek V4 Pro 0813 into a scheduler puzzle. Does the provider reserve KV capacity against the full cap, or allocate blocks lazily and risk eviction when several generations refuse to stop?
Big news: DeepSeek-V4-Pro (Max) by @deepseek_ai is coming in around ~#8 overall (#2 among open models) in the Code Arena: WebDev! At 1607 pts, this places it after GPT-5.6 Sol (xHigh)(1622 pts), and makes it the second best open model after Kimi K3 (Max) (1674 pts). Note: this [i…