Codex Bridge In My Terminal, GPT-5.5 And Opus 4.7 In One Loop

Ask three frontier models the same hard architecture question and you will get three different answers. Pick the one you like best and you have not validated a decision, you have laundered your own bias through a model that happened to agree with you. It is the mistake every operator running real client work makes exactly once: the cheapest confident answer is free, and the bill arrives in production two weeks later.

The lesson, the hard way: one flagship is one read. Two flagships, adversarial, surface the disagreement. The disagreement is the interesting part. The signal an Indian operator is paying for in 2026 is the gap between Opus 4.7 and GPT-5.5 on a hard question, not either one's confidence alone.

This is the field write-up from two days of wiring a Claude Code to Codex CLI bridge on my own machine. The setup is now live in my empire. The rules below are the ones I had to learn the hard way before the system stopped tripping over itself.

Why Both, Not Either

GPT-5.5 launched with the highest score on the Artificial Analysis intelligence index. Opus 4.7 holds SWE-bench Pro and ships the 1M context window. The two models disagree about 22 percent of the time on hard architecture questions in my last week of measurement. The disagreements split into four buckets. One, GPT-5.5 is right and Opus is missing a detail. Two, Opus is right and GPT-5.5 is hallucinating tooling. Three, both are right in different operating regimes. Four, neither is right and the answer is a third option I find only because the disagreement forced me to think harder.

The fourth bucket is the one no single-model loyalty would have surfaced. Worth the wiring on its own.

Pricing on both is flat at the consumer tier. Claude Code Max 20x is $200 a month at the time of writing. ChatGPT Pro Lite is $100 a month. Both meter in sessions or messages, not in raw dollar usage. Run them at flat rate and worry about the message cap, not the per-token bill.

The Bridge, In One Diagram

One bash umbrella, gpt, accepts verbs. Verbs route to per-task scripts in scripts/codex/. The empire's verbs as of week one:

• gpt code for autonomous multi-agent coding tasks. Runs Codex CLI with agents.max_threads=6 and reasoning effort xhigh.
• gpt research for multi-source web research with the unique web.finance tool Codex ships and Claude does not have a peer to.
• gpt image for image generation. Claude Code does not have an in-CLI image-gen path. Codex does.
• gpt ask for one-shot prompts with no tool use.
• gpt audit for pool-burn analytics. Reads the bridge log at ~/.claude/state/codex-bridge/log.jsonl and reports percent of weekly cap consumed.

Each verb logs the call envelope, the model used, the wall-clock duration, and the result. The audit verb sweeps the log weekly and flags any session where Claude was asked to do a Codex task and silently answered from its own knowledge. The No-Fallback rule below catches this, and it is the single most important guardrail.

The total bridge footprint: 412 lines of bash and Python across five files. Zero external dependencies beyond Codex CLI and Claude Code, both already installed.

The No-Fallback Rule

When the user types "use codex" or its Hinglish equivalents in the empire, Claude must call the bridge. Not answer from its own knowledge. Not start writing the code itself and call Codex later. The very next tool call after the request lands in the bridge or the rule is broken.

The reason this rule needs to exist at all: Claude is naturally helpful. If you ask Claude to use a different model and Claude has the answer cached or pattern-matched, the helpful instinct is to answer immediately and save the user a round trip. Five times out of ten this is the right call. Four times out of ten it produces a subtly wrong answer that looks confident. The fifth time it produces an answer the user could have produced themselves and the whole point of asking the other model was to get a second read.

I lost a deploy because of this exact failure mode in week two of running the bridge. Asked Codex for a Cloudflare config, Claude answered from training data with a 2024 pattern that broke under the 2026 zone-level API change. Cost a Sunday afternoon. The rule and the audit tool both came out of that incident.

Codify the rule in your state files. Wire the audit to flag violations weekly. Read the audit. The first time you find a flagged session and remember what you actually wanted, you will trust the bridge more, not less.

Per-Task Routing, The Working Table

The routing table from my last 30 days, distilled to the rules I now follow without thinking:

GPT-5.5 by default for: architecture validation on greenfield builds, adversarial debate when I am already biased toward an answer, math and quant verification, deep web research that needs multi-source synthesis, second opinion on a code review I just wrote myself, alternate-training-corpus signal when I want a non-Anthropic read.

Opus 4.7 by default for: long agentic coding fanout, 1M-context monorepo refactors, voice-critical empire content (Aditya-byline articles, BazaarBaazi commentary, customer-facing copy), vault and settings ops, anything Claude-Code-native (tool use, hooks, MCP plumbing), WhatsApp compliance verticals where the agency rule library is tuned to Claude's behaviour.

Toss-up between them, decided by current cap-burn: anything where both could plausibly handle the task and you would just like the cheaper answer this week. Audit the pool burn from gpt audit --pool, pick the under-burned model, move on.

The mid-tier and grunt routing is its own piece. Roughly: gpt-5.4-mini for mass-dispatch fanout on Codex, Sonnet 4.6 or Cerebras Qwen for cheap classification on Claude, MiMo on OpenRouter for extraction work where structured outputs matter more than reasoning depth.

What The Two Days Cost Me

Saturday morning: read the Codex CLI docs end to end, set up ~/.codex/config.toml with model=gpt-5.5 and model_reasoning_effort=xhigh so every Codex call defaults to the flagship without per-call flags.

Saturday afternoon: wrote gpt, gpt-code, gpt-research, gpt-image, gpt-audit, gpt-ask. Total 320 lines of bash. Each verb is a thin wrapper around codex exec with the right config and logging.

Saturday evening: wrote the bridge log schema and the audit script. 92 lines of Python. The audit script computes weekly burn against the empirical 350-message cap, flags fallback violations, prints a 12-line summary.

Sunday: ran the bridge against three real workloads. Architecture review of an empire agent, image gen for a tutorial OG card, web research on Indian fintech compliance changes. All three completed. The image gen took twelve seconds on Codex against the eight minutes the Claude-native path would have needed via the gpt-image-1 API plus manual download. The web research surfaced two sources Claude had not found because Codex's web.finance tool indexes a few feeds the generic web search does not. The architecture review surfaced one disagreement with Opus 4.7 worth a fifteen-minute conversation to resolve.

Sunday evening: wrote the No-Fallback rule into state. Wired the audit cron. Tested the cap-burn meter against a manual count.

Forty-eight hours, working bridge. Live and audited.

The Indian Operator Read

If you pay for both Max 20x and ChatGPT Pro Lite already, wire this bridge. The wiring is two evenings. The signal is permanent.

If you pay for one and not the other, the question is which workload you have more of. Heavy agentic coding work means Claude Code Max 20x first. Heavy research and architectural validation work means ChatGPT Pro Lite first. Run with one for thirty days, measure where you actually wished for a second read, then add the other.

If you pay for neither, you are operating in 2024. The math on a single Indian solo founder running real client work with no flagship CLI access in 2026 is harder than the math on $200 a month. Pick one.

The disagreement between two flagships is the operator product. The bridge is how you ship it.

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