AI Tech Inspire

How to Orchestrate Multiple LLMs Without Losing Your Mind

Everyone wants the best of GPT, Claude, local models, and the latest entrants like DeepSeek—without rebuilding the same stack in five different apps. At AI Tech Inspire, we spotted a recurring ask from the community: a single, sane workflow for juggling multiple LLMs, ideally with the convenience of OAuth logins and the power of standardized tools like MCP.

Quick facts from the community ask

  • Uses Claude and ChatGPT interchangeably; each excels at different tasks.
  • Prefers Claude’s integrations (e.g., Google and O365 extensions) that work as needed.
  • Finds ChatGPT’s deep research more accurate, with a larger context window and strong utility.
  • Built automations in Claude; discovered those instructions are portable across other LLMs—including local models like Qwen 3.6—at zero per-token cost.
  • Wants a tool that combines ChatGPT and Claude via OAuth (not API) to retain provider features, especially those inside Claude Desktop.
  • Seeks a central way to configure all MCP tools once and swap the underlying model without reinstalling MCPs across every client.
  • Open to trying DeepSeek; not a hard requirement.
  • Not a full-time developer but comfortable with occasional coding.

Why a multi-LLM workflow matters

Developers and engineers increasingly treat models like interchangeable power tools. Some excel at reasoning, some at web search, some at structured outputs, and some at offline processing. A pragmatic stack lets you route tasks to the most capable or cost-effective model.

  • Complementary strengths: Claude’s integrations (like O365) can be workflow gold. Many find ChatGPT’s “deep research” helpful and its larger context windows convenient.
  • Cost control: Local models (e.g., Qwen) handle bulk or repetitive tasks cheaply. Cloud models shine when accuracy, breadth, or proprietary tools are needed.
  • Portability of instructions: Prompts and automations are often model-agnostic, which makes reusing them across providers surprisingly effective.

Key takeaway: Treat your prompts and tools as portable assets. Treat models as pluggable engines you can swap in and out.

OAuth vs API: the reality check

Many readers ask for OAuth-based aggregation of ChatGPT and Claude to preserve first-party features. Today, that’s a tough constraint:

  • Third-party clients generally rely on API keys, not OAuth, to access model endpoints.
  • First-party features (e.g., Claude’s O365 integrations) live inside the vendor’s official apps. OAuth sign-in to a third-party tool won’t typically unlock those features.
  • Apps that let you chat with multiple models via a single UI often act as resellers or intermediate APIs; they don’t grant your personal provider-specific extensions.

Implication: keep using Claude Desktop (or ChatGPT’s web UI) for the features that only exist there. For everything else, use an API-driven router or local runtime. It’s a hybrid world.

A practical architecture you can adopt this week

Here’s a tiered, low-friction design that aligns with how engineers already work:

  • Tier 1 — First-party UIs for vendor-only superpowers: Use Claude Desktop when you need O365/Google extensions or MCP inside Claude. Use ChatGPT in the browser for its browsing and assistant features.
  • Tier 2 — Routing layer for choice and cost control: Stand up a central proxy that exposes a single OpenAI-compatible endpoint and routes to different providers. Popular approaches include an API aggregator (e.g., OpenRouter) or a self-hosted proxy (e.g., LiteLLM’s proxy).
  • Tier 3 — Local models for bulk and privacy: Run Ollama and pull models like Qwen locally. Use local models for pre-processing, summarization, and quick offline tasks.

In practice, you’ll toggle between the vendor UIs and your router-backed tools. The key is centralizing prompts, MCP servers, and evaluation—so you don’t duplicate setup across apps.

MCP centralization without the chaos

Anthropic’s Model Context Protocol (MCP) is a promising way to make tools reusable across clients. Today, Claude Desktop is the flagship MCP client. There are also emerging clients for coding workflows (e.g., agent-style tools in editors) that can consume MCP servers.

  • Goal: Install MCP servers once; reuse them across clients.
  • Reality: Each client may still need a config step pointing to the same set of MCP servers. Store configs in a single directory (e.g., a Git repo) and symlink or import from each client to avoid drift.
  • Tool-agnostic design: Write prompts/instructions that avoid client-specific features. Where possible, return structured outputs (e.g., JSON) to keep logic portable.

As more MCP-compatible clients emerge, this approach minimizes rework—and makes swapping models feel like flipping a switch.

Concrete tools to try

  • Routing/API layer:
    • LiteLLM Proxy: A self-hosted, OpenAI-compatible proxy that can route to multiple providers and models. Configure in YAML; your apps just point to one endpoint.
    • OpenRouter: An aggregator that exposes many models behind one API. Great for experimenting with new models like DeepSeek, without wrangling multiple keys.
  • Local runtime:
    • Ollama: Simple local serving for models like Qwen. Ideal for zero-token pre-processing or offline experiments.
  • First-party UIs:
    • Claude Desktop: Keep using it for O365/Google extensions and MCP tools.
    • ChatGPT Web: Use it for high-context “deep research” and convenience features of GPT-powered assistants.
  • Prompt/eval management:
    • Store prompts and instructions in Git or a note system. Consider lightweight testing frameworks to A/B models and measure quality.

Sample litellm proxy config to route different tasks:

# config.yaml
model_list:
  - model_name: research-gpt
    litellm_params:
      model: openai/gpt-4o
      api_key: $OPENAI_API_KEY
  - model_name: summarize-local
    litellm_params:
      model: ollama/qwen2.5:7b
      api_base: http://localhost:11434
  - model_name: claude-high-context
    litellm_params:
      model: anthropic/claude-3-7-sonnet
      api_key: $ANTHROPIC_API_KEY
router_settings:
  - route: "#deep_research"
    to: research-gpt
  - route: "#summarize"
    to: summarize-local
  - route: "#long_context"
    to: claude-high-context

Point your IDE or scripts at the proxy once, then select models by name. Use task tags (#deep_research) in your workflow to steer routing.

A day-in-the-life multi-LLM flow

  • Inbox triage with integrations: In Claude Desktop, invoke the O365/Google extensions for calendar and doc summaries. Keep this in the first-party app to retain permissions and formatting fidelity.
  • Summarize locally for zero cost: Batch long PDFs through Ollama running Qwen. Generate JSON summaries (title, abstract, key points) for downstream use.
  • Deep research when accuracy matters: For tricky, cross-domain topics, send the prompts (and extracted JSON from local summaries) to your router’s research-gpt model—often a GPT-4 class model via your proxy.
  • Switch models with one shortcut: Map Ctrl + Shift + M to bring up a model switcher in your IDE or chat client (many UIs support quick-input selectors). Choose the preconfigured route like #long_context for sizeable inputs.
  • Reuse MCP tools across clients: Keep your MCP server configs in a shared folder. Point Claude Desktop and any editor agent to the same tool list, so file access, web fetch, and repo actions behave consistently.

Tip: Ask every model to return {"thoughts":[],"actions":[],"result":...}. Even if you don’t use the internals, structured replies make evaluation and chaining simpler.

Migrating existing Claude automations

Good news: if your automations are instruction-based (not UI clicks), they likely port to other models:

  • Encapsulate them as parameterized prompt templates ({{goal}}, {{constraints}}, {{format}}).
  • Normalize outputs to JSON schemas so any model can plug in.
  • Avoid provider-only features unless you stay in that UI. If needed, create two modes: first-party mode (uses Claude’s integrations) and router mode (uses API-accessible tools).

This dual-mode design preserves convenience without locking your logic to a single vendor.

Trying new models (e.g., DeepSeek) without the pain

When new models drop, the question is always: “How do I slot this into my stack?” With a routing layer, the answer is: add a model entry, test with a small subset of tasks, and promote if quality/cost check out. For open models, use Hugging Face references to find quantized variants you can run locally.

  • Start with low-stakes tasks (summaries, formatting) before critical reasoning.
  • Compare outputs side-by-side for a week with the same prompts.
  • Log time-to-first-token and total cost to decide when to route by default.

Privacy, cost, and the boring but vital stuff

  • Privacy: Keep sensitive data on local models or first-party UIs governed by your org’s policies.
  • Cost: Route long-context tasks to the model that gives the best accuracy per token, not just the biggest context window.
  • Observability: Even if you’re not coding-heavy, basic logs help. Capture prompt+model+latency+rating to identify when a route underperforms.

Bottom line

You don’t need a unicorn app that OAuths into every provider and magically exposes all their first-party features. The realistic, durable pattern is a hybrid:

  • Use first-party UIs where integrations live (Claude Desktop for O365/Google; ChatGPT for its research and assistant features).
  • Adopt a routing layer to unify APIs and swap models in one place.
  • Lean on local models for cost-effective bulk work and privacy.
  • Centralize MCP servers and prompt templates to keep everything portable across clients.

Follow this playbook and you’ll spend less time installing, more time shipping—and you’ll be ready for whatever model lands next.

Recommended Resources

As an Amazon Associate, I earn from qualifying purchases.