Guides, comparisons, and insights on LLM routing and AI API cost optimization.
Our smart-routing product had a blind spot: 80%+ of traffic was bypassing the router because client defaults hardcoded Opus. After 12 hours of fixes, real data: per-request cost down 36%, Opus share of spend from 80% to 45%, honest savings from 60% to 91%, ~$90K/month saved. Here's the story.
我们的智能路由产品发现了一个尴尬的盲区:大部分流量根本没在路由。修完之后 12 小时真实数据:每请求成本降 36%,Opus 花费占比从 80% 降到 45%,真实 savings 从 60% 升到 91%。这是过程和数据。
中文圈 LLM 中转站的本质是 OpenAI / Anthropic API 转售,大多数把所有请求都发给 Opus 或 GPT-5。我们用 5000 次真实路由的生产数据告诉你:为什么 80% 的编码 token 应该用 V4-Flash / Sonnet 而不是 Opus,以及任务感知路由和中转站的本质区别在哪里。
GitHub Copilot just raised prices 6x. Claude Code heavy users burn $50-200/day. Here's why AI coding bills spiral — and the 5 concrete steps to cut them by 60-90% without losing productivity.
OpenRouter is great for model access, but it won't cut your coding bill. Here are 7 alternatives that actually reduce what you spend on Claude Code, Cursor, Aider, and Copilot — with real pricing and tradeoffs.
Chinese LLMs ship with a 'thinking mode' that breaks the OpenAI/Anthropic API contract on turn 2. Real production errors from Claude Code, Cursor, and Aider — plus the one-line fix per provider, a cross-provider audit checklist, and why this trap will keep biting API gateways through 2026.
Most LLM routers pick by cost or capability. Phase-aware routing detects which phase of coding you're in (plan / implement / debug / test / refactor / docs / small-edit) and routes each call independently. After 30 days in production: 78% cost savings, 7 different models touched per Claude Code session, no measurable quality regression. The detection algorithm, the model assignments, the pitfalls, and the data.
Four major coding models dropped in one week (April 20–23, 2026): GPT-5.5, GPT-5.4, DeepSeek V4 Pro/Flash, and Kimi K2.6. One table, one decision tree, one verdict per task. Skip the marketing — here's what actually changed for coding agents and which model to pick for which job.
DeepSeek V4 ships in two tiers. V4 Pro at $1.74/$3.48 scores 81% on SWE-bench Verified — near-Opus territory. V4 Flash at $0.14/$0.28 is 12× cheaper, still 1M context, still strong on implementation. Here's the decision matrix for coding agents, plus why pinning just one wastes money.
OpenAI's GPT-5.5 tops the Artificial Analysis Intelligence Index at 60, beating Opus 4.7 on Terminal-Bench 2.0 (82.7% vs 75.1%) — but Opus still wins real-world SWE-Bench Pro (64.3% vs 58.6%). Here's how to pick per-task, and why paying for both via phase-aware routing is cheaper than committing to one.
Moonshot's Kimi K2.6 (April 2026) scores 58.6% on SWE-Bench Pro — tied with GPT-5.5 — at $0.60/$4.00 per 1M. It's open-weight, 256K context, purpose-built for long-horizon agentic coding (300 sub-agents, 4000 steps). Full review, benchmarks, and when it's the right pick over Claude Opus, GPT-5.5, and DeepSeek V4.
Cut Claude Code's bill 50–80% by routing every call through a phase-aware proxy. Includes the AUTH_TOKEN-not-API_KEY trap, the experimental-betas fix for v2.1.x, and a complete settings.json template that just works.
Aider's architect + editor split is brilliant — but both modes default to Opus. Here's how to combine Aider's --architect flag with phase-aware routing for 80%+ cost reduction without touching your workflow.
OpenRouter and CodeRouter sound similar — both are 'routers'. But they solve different problems. OpenRouter gives you multi-model access; CodeRouter reduces your coding agent bill by picking the cheapest capable model per request automatically.
Cursor Pro burns tokens fast when you hit fast-request limits. Here's how phase-aware API routing cuts your real monthly coding spend without switching away from the Cursor IDE.
Head-to-head: DeepSeek V3 at $0.28/$0.42 vs. Claude Sonnet 4.6 at $3/$15 per 1M. On coding tasks, when does the 15× cost difference show up in output quality — and when doesn't it?
GitHub Copilot's $10/month is cheap but locks you into their model choices. For power users who hit Copilot's rate limits, phase-aware routing via a Custom Model endpoint delivers more context + cheaper per-token + model diversity.
Most LLM routers pick one model and stick with it. Phase-aware routing detects which *phase* of coding you're in — planning, implementing, debugging, testing — and picks the cheapest capable model per phase. Here's how it works in <10ms.