Multi-Model Routing: When to Use Gemini, Codex, or Claude
Using one AI model for everything is like using one tool for every job. Here's how I route tasks to the right model based on what each one actually does well.
Using one AI model for everything is like using one tool for every job. You can hammer in a screw, but there’s a better way.
I run three major models in production: Gemini, Codex, and Claude. Each has distinct strengths and distinct failure modes. The system that routes tasks to the right model — and sometimes to all three simultaneously — is one of the highest-value pieces of infrastructure I’ve built.
Why One Model Isn’t Enough
Every model has a shape. A profile of what it does well and what it does poorly. If you use one model for everything, you’re accepting its weaknesses across your entire operation.
That’s fine for casual use. It’s not fine when you’re running AI in production where failure has cost. A model that’s great at code generation might be mediocre at research synthesis. A model that excels at long-context analysis might struggle with precise code edits.
The single-model approach also creates a single point of failure. If your provider has an outage, your entire operation stops. Multi-model routing fixes both problems: best model for each task, plus redundancy when any single model underperforms.
Gemini: The Research Machine
Gemini’s standout capability is context. A 1-million-token context window means you can load entire codebases, complete documentation sets, or months of conversation history into a single prompt. No chunking. No RAG retrieval that might miss critical context. Just the full picture, all at once.
I route three task types to Gemini.
Deep research with large source material. When I need to analyze a 200-page specification, cross-reference it with a codebase, and produce a gap analysis, Gemini gets the job. Other models would need the content chunked and retrieved. Gemini sees the whole thing at once.
Google Search integration. Gemini has native access to Google Search, grounding responses in current web data without a separate tool chain. Competitive analysis, market research, current API docs — Gemini searches in real time as part of its reasoning. No separate scraping step.
Multimodal analysis. Gemini handles images, audio, and video natively. UI screenshots against design specs, recorded meetings for action items, data extraction from diagrams — Gemini processes the media directly instead of requiring text conversion.
Gemini’s weakness: it sometimes prioritizes breadth over precision. For tasks that require exact code changes or strict logical reasoning with no tolerance for approximation, I route elsewhere.
Codex: The Autonomous Executor
Codex operates in a sandboxed environment with a full development toolchain. It can clone repos, run tests, install dependencies, and iterate on code without human intervention. This makes it fundamentally different from models that just generate text — Codex generates text AND validates it against real execution.
I route three task types to Codex.
Test-fix loops. When a test suite is failing, Codex reads the failures, modifies the code, re-runs the tests, and iterates until green. All in a sandbox. The output is a working patch, not a suggestion.
Audit tasks. Codebase scans for security vulnerabilities, deprecated API usage, license compliance — Codex sets up the tools, runs them, interprets results, and produces a structured report. All without babysitting.
Deterministic code transformations. Migrate from library A to library B, update all endpoints to the new auth pattern, add error handling to every database call. Codex executes the transformation and validates against the test suite. It can try multiple approaches and only return the one that passes.
Codex’s weakness: it operates in isolation. No access to your production environment, your team’s context, or your architectural preferences beyond what’s in the repo. Technically correct answers that might be organizationally wrong.
Claude: The Reasoning Engine
Claude’s strength is nuanced, multi-step reasoning. When a task requires weighing trade-offs, understanding implicit context, maintaining consistency across a long chain of decisions, or producing output that needs to be persuasive to humans, Claude is my first choice.
I route three task types to Claude.
Architectural decisions. Evaluating three system design approaches against constraints like performance, maintainability, and migration cost. Claude produces structured analysis that accounts for second-order effects. It reasons about consequences, not just features.
Complex multi-step workflows. When step 3 depends on decisions from step 1, Claude maintains coherence across the full chain. Not just having a big context window — actually using it to maintain logical consistency.
Human-facing output. Documentation, proposals, post-mortems — anything people need to read. A technically perfect architecture doc that nobody reads is functionally the same as no doc at all.
Claude’s weakness: it can over-reason. For straightforward tasks, it sometimes explores alternatives that don’t need exploring, burning tokens on unnecessary analysis.
The LLM Council: Byzantine Consensus
For high-stakes decisions, I don’t route to one model. I route to all three.
Each model independently evaluates the task and produces its recommendation. Then a consensus protocol determines the final output. If all three agree, the answer ships. If two agree and one dissents, the majority answer ships but the dissent is logged and reviewed. If all three disagree, the task gets escalated to a human with all three perspectives attached.
This is Byzantine fault tolerance applied to AI. The term comes from distributed systems: how do you reach consensus when some participants might be faulty? You can’t know in advance which model will hallucinate on which task, so you treat every model as potentially faulty and require agreement.
The council catches errors that no single model catches alone. I’ve seen cases where Claude and Codex both approved a code change, but Gemini — with its broader context — flagged a conflict with a specification that the other two missed. I’ve seen Gemini and Claude agree on an approach that Codex proved was technically impossible by actually trying to execute it.
The cost is 3x the compute for a single task. The value is confidence that no single model’s blind spot sinks you.
Routing Triggers
The routing decision happens in Phase 4 of my 5-phase workflow (playbook routing). The system examines the task classification and selects the appropriate model or model combination based on concrete attributes.
Context size determines Gemini routing. If the task requires more than 128K tokens of context, Gemini gets it. Period.
Execution requirement determines Codex routing. If the task needs to run code, execute tests, or validate against a real environment, Codex gets it.
Reasoning depth determines Claude routing. If the task requires multi-step analysis, trade-off evaluation, or persuasive output, Claude gets it.
Risk level determines council routing. If the task is classified L3 or L4 (high risk, critical risk), all three models evaluate it. No exceptions.
These triggers aren’t fuzzy heuristics. They’re concrete conditions that the routing system checks programmatically. The AI doesn’t pick its own model. The infrastructure picks the model based on task properties.
The Compound Effect
Each model individually is powerful. The routing layer that puts the right task in front of the right model — that’s where the multiplication happens. A well-routed task completes faster, with higher quality, and with better error detection than the same task sent to a default model.
Multi-model routing isn’t about having the newest or most expensive model. It’s about knowing what each model does well and building the infrastructure to match tasks to capabilities automatically. The models are commodities. The routing is the asset.
I build multi-model AI infrastructure that routes tasks to the right model based on evidence, not habit. If you’re running everything through one model and wondering why quality is inconsistent, I can show you a better architecture.
Book a call: https://cal.com/davidyoussef