Claude Opus 4.6 vs GPT-5.3 Codex

Описание: I sit down with Morgan Linton, Cofounder/CTO of Bold Metrics, to break down the same-day release of Claude Opus 4.6 and GPT-5.3 Codex. We walk through exactly how to set up Opus 4.6 in Claude Code, explore the philosophical split between autonomous agent teams and interactive pair-programming, and then put both models to the test by having each one build a Polymarket competitor from scratch, live and unscripted. By the end, you'll know how to configure each model, when to reach for one over the other, and what happened when we let them race head-to-head.

Timestamps
00:00 – Intro
03:26 – Setting Up Opus 4.6 in Claude Code
05:16 – Enabling Agent Teams
08:32 – The Philosophical Divergence between Codex and Opus
11:11 – Core Feature Comparison (Context Window, Benchmarks, Agentic Behavior)
15:27 – Live Demo Setup: Polymarket Build Prompt Design
18:26 – Race Begins
21:02 – Best Model for Vibe Coders
22:12 – Codex Finishes in Under 4 Minutes
26:38 – Opus Agents Still Running, Token Usage Climbing
31:41 – Testing and Reviewing the Codex Build
40:25 – Opus Build Completes, First Look at Results
42:47 – Opus Final Build Reveal
44:22 – Side-by-Side Comparison: Opus Takes This Round
45:40 – Final Takeaways and Recommendations

Key Points

Opus 4.6 and GPT-5.3 Codex dropped within 18 minutes of each other and represent two fundamentally different engineering philosophies — autonomous agents vs. interactive collaboration.
To use Opus 4.6 properly, you must update Claude Code to version 2.1.32+, set the model in settings.json, and explicitly enable the experimental Agent Teams feature.
Opus 4.6's standout feature is multi-agent orchestration: you can spin up parallel agents for research, architecture, UX, and testing — all working simultaneously.
GPT-5.3 Codex's standout feature is mid-task steering: you can interrupt, redirect, and course-correct the model while it's actively building.
In the live head-to-head, Codex finished a Polymarket competitor in under 4 minutes; Opus took significantly longer but produced a more polished UI, richer feature set, and 96 tests vs. Codex's 10.
Agent teams multiply token usage substantially — a single Opus build can consume 150,000–250,000 tokens across all agents.

Numbered Section Summaries

1) Setup and Configuration for Opus 4.6
I have Morgan walk through the exact steps every developer needs: update via NPM, verify version 2.1.32+, set the model to Opus in settings.json, and — critically — enable the experimental Agent Teams flag by setting `claude_code_experimental_agent_teams` equal to one. He also covers adaptive thinking (API-only), effort levels (max is Opus 4.6 exclusive), and TMUX split panes for visualizing multiple agents in action.

2) Feature-by-Feature Comparison
Opus 4.6 offers a 1-million-token context window, strong architectural comprehension, and less tendency to write reckless code. GPT-5.3 Codex has a roughly 200,000-token context window, wins on SWE-Bench Pro and coding benchmarks, and excels at progressive execution and rapid iteration. Morgan frames it well: Claude is your senior staff engineer asking "should we do this?" while GPT-5.3 is your founding engineer asking "how fast can I ship this?"

3) Live Build: Prompt Design and Fair Setup
Morgan creates separate prompts tailored to each model's strengths. For Opus, he instructs it to build an agent team with four specialists (architecture, prediction market domain, UX, testing). For Codex, he gives a parallel prompt asking it to think deeply about the same four areas. He starts both at essentially the same moment to keep the comparison honest.

4) Final Verdict and Recommendations
Morgan is clear: this is about methodology, not a winner-takes-all contest. Opus 4.6 excels when you want to delegate whole chunks of work to autonomous agents and review comprehensive results. Codex 5.3 excels when you want a fast, interactive collaborator you can steer in real time. Many teams will end up using both. Morgan encourages engineering leaders to give their teams access to both models and let them experiment.

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Morgan Linton
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Bold Metrics: https://boldmetrics.com/
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