Google prepares Gemini 3.8 Flash to catch up in AI coding
Google DeepMind is preparing the launch of the AI model Gemini 3.8 Flash to challenge OpenAI and Anthropic in the field of programming.

What happened?
Google DeepMind is preparing the launch of its new AI model Gemini 3.8 Flash, internally known by the codename "Skimaki". According to reports from The Wall Street Journal on 1 September 2026, the model could be launched as early as Wednesday, 2 September 2026. The primary objective of the model is to drastically improve Google's capacity for programming and code generation.
Key facts
| Modellnamn | Gemini 3.8 Flash |
|---|---|
| Internt kodnamn | Skimaki |
| Möjligt lanseringsdatum | 2 september 2026 |
| Rapporteringsdatum | 1 september 2026 |
Why it matters
Google has for some time lagged behind competitors such as OpenAI and Anthropic regarding specialized AI for software development. By focusing on coding performance in the faster Flash series, Google is attempting to secure its position in the rapidly growing market for enterprise solutions and developer tools.
Who is affected?
The model is primarily aimed at software developers, IT departments, and enterprise customers building applications within the Google ecosystem. It also concerns developers who currently use competing tools from OpenAI and Anthropic for code generation.
Impact on the EU
Gemini 3.8 Flash is expected to be available globally via Google's existing cloud infrastructure and developer tools, meaning European developers will also gain access to the model in connection with the rollout. County administrative boards and companies in the EU that use Google Cloud are subject to current data protection regulations.
What else you should know
The report from The Wall Street Journal is based on internal information and tests from Google's developer platform Jetski. Google DeepMind has not officially commented on the launch date or the internal performance figures ahead of the expected release.
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- Assess technical risk: model choice, vendor lock-in, data flow and running cost.
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