DeepSeek Chat V3 (deepseek/deepseek-chat-v3) — Automatic Disk KV Cache (0.014/1M) & Off-Peak Promo
DeepSeek-V4 Pro (1.6T MoE) automatically caches repeated 64-token prompt prefixes on NVMe storage at $0.022/1M off-peak input tokens (vs $0.66 off-peak base) with zero code changes required, plus off-peak UTC scheduling discounts.
DeepSeek Chat V3
API ID: deepseek/deepseek-chat-v3DeepSeek Chat V3 (Oct 17, 2026 Pareto Update) delivers 77.0% on DeepSWE v1.1, 60.5% on Automation-Bench, native Search/Fetch API ($0.75/1K calls), and 90% auto disk-KV cache savings.
Automatic disk-KV caching cuts repeated prompt input costs by 90% ($0.014/1M) with zero manual configuration. Search/Fetch API billed separately at $0.75/1K calls.
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. Distill prompt context semantically 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 3.5x.
Unlock OpenAI GPT-6.1 Sol Mastery Pack & 3.5x Quota Stretcher ($19 One-Time)
Get instant lifetime access to the Semantic Context Distiller, 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>