Gemini
3 articles
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GoogleGemini Optimized ADK Overnight. Your Database Store Missed It.
Google let Gemini 4 Argon run unattended for 11 hours on its 205,000-line Agent Development Kit; two of six proposed optimizations shipped in ADK 2.12.0 (commit 1754b3e, 'Argon generated these changes') and maintainers cut four. Benchmarked against 2.11.0 on an M2 Pro, ADK's pre-model overhead at turn 150 fell from 51 to 25 ms with the in-memory session store and stayed at about 45 ms with the SQLite-backed DatabaseSessionService; before the change the in-memory store was the slower of the two. The LlmAgent import fell from 474 to 357 ms but a fresh process still reached its first model request in 579 and 584 ms, so local evals got faster and the gap to production got wider.
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GoogleGemini 4 Argon Is Cheap Per Token, Not Per Task
Google made Gemini 4 Argon generally available on 13 October at an introductory $2 per million input tokens and $10 per million output, a fifth of GPT-6 Astra and half of Claude Opus 5.5, with the price doubling to $4 and $20 on 1 December. Artificial Analysis scored it level with Astra on its Intelligence Index but found it costs 60% of Astra per task, not 20%, because Argon averages 62,000 output tokens per task against Astra's 27,000; at the standard price that becomes $3.98 per task against Astra's $3.26. The migration guide also makes max_output_tokens a hard cap on thinking plus output and tells you to raise it to at least 32,768, silently ignores temperature, top_p and top_k, and drops explicit caching entirely.
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GeminiGemini 3.7 Flash Is Deprecated and Quietly Swapped Out
On 8 October Google deprecated gemini-3.7-flash, a stable model released on 13 August, and gemini-3.5-flash, with no shutdown date for either. Requests to both IDs now return 200 and are silently served by 3.8 Flash and 3.6 Flash; the model list and metadata endpoint show no change, and the only signal is the modelVersion field in the response. Vertex AI gives the same model a fixed retirement date instead. Log modelVersion and re-run your evals.