Google Gemini 3.1 Pro Preview Batch (google/gemini-3.1-pro-preview-batch) — Google AI Startup Credits ($350K) & 75% Cache Promo
Google DeepMind is currently subsidizing Gemini 4 Argon at 50% off standard rates ($2.00/1M input & $10.00/1M output instead of $4/$20) until November 30, 2026, alongside 95% off Context Caching ($0.10/1M) and up to $350,000 in Google for Startups Cloud/Vertex AI credits.
Google Gemini 3.1 Pro Preview Batch
API ID: google/gemini-3.1-pro-preview-batchGoogle Gemini 3.1 Pro Preview Batch is Google DeepMind's native multimodal frontier model supporting up to 2M context tokens and implicit context caching.
Prompts exceeding 200K tokens incur 2x tier pricing unless pinned via Google AI Studio Context Caching (75% off).
OpenAI GPT-6.1 Sol [gpt-6.1-sol] Power-Up & Quota Booster Studio
Came specifically for OpenAI GPT-6.1 Sol? Don't waste your 5-hour subscription quota on bloated context or sycophantic retries. Compress prompts losslessly and compile model-calibrated `CLAUDE.md` / `.cursorrules` & MCP configs.
2x price/quota cliff above 272K context & premature task completion claims without edge-case verification.
Unpruned RAG context crossing the 272,000 token threshold & redundant tool-call loops.
reasoning_effort: "medium" + max_context_guard: 265000 — Stretches your monthly usage quota by 3.6x.
Unlock OpenAI GPT-6.1 Sol Mastery Pack & 3.5x Quota Stretcher ($19 One-Time)
Get instant lifetime access to the Lossless Context Compressor, custom `CLAUDE.md` / `.cursorrules` compiler, 2026 MCP bundle, and Multi-Model Relay generator for all 400+ models.
<context_handoff model_target="gpt-6.1-sol" domain="coding" compression_ratio="72%">
<system_calibration>
CRITICAL: Verify every file mutation against the type system before declaring completion. Never assume an external API or import exists without checking package.json.
Enforce strict deterministic JSON schema outputs and zero placeholder comments.
Optimal Config: reasoning_effort: "medium" + max_context_guard: 265000
</system_calibration>
<distilled_state>
Stack: Next.js 14 SaaS Dashboard + Vector Search.
Core Proposal to Audit: Client proposed storing session tokens in localStorage (HIGH XSS RISK -> Reject & recommend HttpOnly SameSite cookies) and running vector search on every keystroke (HIGH QUOTA/LATENCY DRAIN -> Reject & recommend 350ms debounced hybrid search + semantic cache).
</distilled_state>
<execution_directive>
1. Red-team the proposed architecture in 3 bullet points.
2. Output the complete, production-ready implementation with zero "// TODO" placeholders.
</execution_directive>
</context_handoff>