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companyxAI36 published changescompanyDeepSeek12 published changescompanyAnthropic7 published changescompanyCursor6 published changescompanyOpenAI3 published changescompanyGoogle2 published changescompanyAlibaba0 published changescompanyMoonshot AI0 published changescompanyZhipu AI0 published changesproductClaude58 published changesproductClaude Code36 published changesproductGrok11 published changesproductDeepSeek10 published changesproductCursor6 published changesproductCodex4 published changesproductChatGPT0 published changesproductGemini0 published changesproductKimi0 published changes

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Deprecation Importance 92/100 Confidence 100/100

Codex MCP server command deprecated

OpenAI deprecated the Codex MCP server command. Integrations that launch it should move to the Codex app server.

Action requiredReplace Codex MCP server launch commands with the Codex app server before the old path is removed.
CodexproductOpenAIcompany
1 official source 0 linked discussions 0 reader signals Open Codex hub
API change Importance 90/100 Confidence 100/100

DeepSeek V4-Flash API update

DeepSeek moved V4-Flash into public beta and documented stronger agent performance plus Responses API compatibility for Codex workflows.

Action requiredVerify model IDs and Responses API settings in Codex or other agent clients before switching production traffic.
DeepSeek V4-FlashmodelDeepSeekcompanyDeepSeek modelsmodel familyCodexproduct
1 official source 0 linked discussions 0 reader signals Open DeepSeek V4-Flash hub
Deprecation Importance 88/100 Confidence 100/100

DeepSeek-V4-Flash Update ​

DeepSeek-V4-Flash Update ​ The official release of the DeepSeek-V4-Flash API is now in public beta. The API calling method remains unchanged — simply set the model name to deepseek-v4-flash to use the latest version. Significantly enhanced agent capabilities, with benchmark results far exceeding V4-Pro-Preview: Terminal Bench 2.1: 82.7 NL2Repo: 54.2 Cybergym: 76.7 DeepSWE: 54.4 Toolathlon verified: 70.3 Agent Last Exam: 25.2 Automation Bench (Public): 25.1 DSBench-FullStack: 68.7 DSBench-Hard: 59.6 Note 1: For the Code Agent tasks in the public benchmark sets, the official DeepSeek-V4-Flash was tested using the DeepSeek Harness minimal mode (to be released soon) as the framework, with the max effort level, topp=0.95, and temperature=1.0 Note 2: DSBench-FullStack is an internal full-stack development test set, and DSBench-Hard is an internal Coding Agent hard-problem test set The official V4-Flash natively supports the Responses API format and is specifically adapted for Codex. For the specific configuration, please refer to the documentation . DeepSeek-V4-Flash-0731 keeps the same model architecture and size as DeepSeek-V4-Flash-Preview, and was only re-post-trained. Note: This upd

Action requiredReview affected integrations and migrate before the documented retirement boundary.
DeepSeekcompanyAgentscapabilityCodexproductCodingcapabilityDeepSeekproductDeepSeek V4-FlashmodelReasoningcapability
3 official sources 0 linked discussions 0 reader signals Open DeepSeek hub
API change Importance 78/100 Confidence 100/100

DeepSeek-V4-Pro Update ​

DeepSeek-V4-Pro Update ​ The GA release of DeepSeek-V4-Pro has been rolled out on the APP, Web, and API. The API calling method remains unchanged — simply set the model name to deepseek-v4-pro to use the latest version. Significantly enhanced Agent capabilities The GA version of DeepSeek V4 Pro greatly enhances agent capabilities, with particularly significant performance improvements in production environments. HLE (wo / w tools): 42.7/60.0 Terminal Bench 2.1: 87.9 NL2Repo: 61.5 Cybergym: 83.3 DeepSWE: 62.7 Toolathlon-Verified: 74.1 Agents' Last Exam: 25.7 AutomationBench (Public): 31.8 DSBench-FullStack: 71.1 DSBench-Hard: 67.2 Native support for the Responses API The DeepSeek API now natively supports the OpenAI Responses API format and is specifically adapted for Codex. Users can refer to the official documentation and complete the Codex configuration with a one-click configuration script. More flexible thinking effort control The thinking modes of V4-Pro and V4-Flash now support three thinking effort levels: low / high / max. In real-world usage, users can flexibly choose based on task complexity: use low for simple tasks, high for daily Agent tasks, and max for more complex s

DeepSeekcompanyAgentscapabilityCodexproductDeepSeekproductOpenAIcompany
1 official source 0 linked discussions 0 reader signals Open DeepSeek hub

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Official and first-party repository changes may publish automatically after deterministic validation. Independent sources remain draft until review. Community links are attached as discussion evidence and cannot create an indexable fact by themselves.

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