Multi-Model LLM Routing in 2026: GPT-5.5, Claude Opus 4.7, and DeepSeek V4

April 2026 compressed what would normally be a year of model releases into two weeks. Claude Opus 4.7 launched on April 16, GPT-5.5 on April 23, and DeepSeek V4 appeared the day after. The question has shifted from "which model is best?" to "how do you route tasks across them?"

What each model is actually good at

Claude Opus 4.7 leads on 6 of 10 benchmarks against GPT-5.5, with the widest margins on tasks requiring sustained reasoning across large codebases. For complex refactors, security review, or multi-file changes where context continuity matters, it's the strongest option currently available.

GPT-5.5 has a one-million-token context window — the largest of the three. This makes it the obvious choice for tasks that require ingesting an entire codebase or a large set of documents in a single pass. At $5/M input and $30/M output tokens, it's not the cheapest option, but the context ceiling is genuinely useful for batch documentation or cross-repository analysis.

DeepSeek V4 Flash at $0.14/M input tokens is the most aggressive pricing in the frontier model tier. V4-Pro at $1.74/M input is still significantly cheaper than its Western competitors. The practical caveat: DeepSeek's latency profile is higher in non-Asia regions, which matters for user-facing applications with strict response time requirements.

A practical routing design

The two axes that matter for routing: task complexity and cost tolerance.

type TaskType =
  | "lint-and-style"
  | "logic-check"
  | "bulk-doc-generation"
  | "architecture-review"
  | "security-audit";
 
const modelRouter: Record<TaskType, string> = {
  "lint-and-style":      "deepseek-v4-flash",
  "logic-check":         "deepseek-v4-pro",
  "bulk-doc-generation": "gpt-5.5",
  "architecture-review": "claude-opus-4-7",
  "security-audit":      "claude-opus-4-7",
};
 
async function route(taskType: TaskType, prompt: string): Promise<string> {
  const model = modelRouter[taskType];
  return callLLM(model, prompt);
}

This is a simplified version, but the pattern holds: define task categories, map them to models, and keep the routing logic in one place so you can swap models as new releases come out.

Escalation for quality control

Not every task fits neatly into a category. A common pattern is to start with the cheapest model and escalate if the output quality score falls below a threshold:

async function completionWithEscalation(
  prompt: string,
  startModel = "deepseek-v4-flash"
): Promise<string> {
  const models = [
    "deepseek-v4-flash",
    "deepseek-v4-pro",
    "claude-opus-4-7",
  ];
 
  let currentModelIndex = models.indexOf(startModel);
 
  while (currentModelIndex < models.length) {
    const model = models[currentModelIndex];
    const output = await callLLM(model, prompt);
    const score = await evaluateQuality(output);
 
    if (score >= 0.75) return output;
    currentModelIndex++;
  }
 
  throw new Error("All models failed quality threshold");
}

The evaluateQuality function is domain-specific. For code generation, you might run syntax checks and test execution. For prose, an LLM-as-judge pattern works well with a lightweight model doing the evaluation.

Cost comparison at scale

For a team running 1,000 code generation requests per day (average 2,000 input / 1,000 output tokens):

The routing approach doesn't just cut cost — it applies the right compute to each task, which often produces better aggregate quality than over-applying an expensive model to everything.

What to watch going forward

DeepSeek V4's pricing is aggressive enough that it will pressure other providers to respond. Building your routing layer as a proper abstraction — not hardcoded API calls scattered through the codebase — means you can incorporate new models as they ship without touching application logic.

The competitive pressure from DeepSeek V4 is also worth tracking in terms of open-weight releases. Google shipped Gemma 4 under Apache 2.0 in the same week, suggesting the gap between proprietary and open-weight frontier models is narrowing faster than expected. Self-hosted inference may become cost-competitive for high-volume, latency-tolerant workloads within the next release cycle.

For teams building AI-powered features today, the most durable investment is the abstraction layer between your application and the model APIs — not the choice of any particular model.