GPT-5.3-Codex vs Claude Opus 4.6: Which Agentic Coding Model Actually Wins?

This week, two flagship agentic coding models launched within twenty minutes of each other.

On February 5, 2026, Anthropic released Claude Opus 4.6 and OpenAI responded with GPT-5.3-Codex. For enterprise leaders, this creates both opportunity and complexity: two genuinely capable options, each with distinct strengths.

OpenAI calls GPT-5.3-Codex "the most capable agentic coding model to date." Anthropic says Opus 4.6 "excels at real-world agentic coding and system tasks."

The question isn't which model is "better"—it's which one fits your organization's workflows, risk tolerance, and operational priorities. We analyzed the benchmarks, independent assessments, and enterprise use cases to help you decide.

The Same-Day Showdown: What Each Company Claims

OpenAI's GPT-5.3-Codex

OpenAI positions Codex as the speed and efficiency champion:

• 25% faster inference than GPT-5.2-Codex

• Less than half the tokens to accomplish equivalent tasks

• First model "instrumental in creating itself" through recursive self-improvement

• First OpenAI model rated "High capability" for cybersecurity

The enterprise pitch? Teams can steer and interact with Codex while it's working without losing context—enabling real-time collaboration between human oversight and AI execution.

Anthropic's Claude Opus 4.6

Anthropic emphasizes depth and collaboration:

• 1 million token context window (beta)—roughly 750,000 words in a single session

• Agent teams: Multiple Claude instances collaborate on projects simultaneously

• Adaptive thinking: Model decides when to use extended reasoning based on task difficulty

• Context compaction: Summarizes older context to extend long-running tasks

The enterprise pitch? Agent teams that mirror how large organizations actually work—splitting complex projects across specialized units that coordinate autonomously.

The Benchmarks: Where Each Model Actually Wins

Here's where company narratives diverge from reality.

Where GPT-5.3-Codex Wins

Running terminal commands and scripts (Terminal-Bench 2.0): Codex scores 77.3% vs Opus's 65.4% on tasks like navigating file systems, running builds, and executing automation scripts. If your teams live in the command line, that 12-point gap matters.

Controlling desktop applications (OSWorld): Codex achieved 64.7% on tests that simulate clicking through interfaces, filling forms, and navigating software—the highest score ever recorded. Claude sits around 42%.

Raw speed: 25% faster than its predecessor. For high-volume automation, speed compounds.

Where Claude Opus 4.6 Wins

Fixing real bugs in real codebases (SWE-Bench Verified): Opus scored 80.8% on tests using actual GitHub issues from production repositories. When you hand it a bug report and a codebase, it finds and fixes the problem more reliably.

Knowledge work requiring reasoning (GDPval-AA): On tasks that simulate finance, legal, and consulting work—analyzing documents, synthesizing information, making recommendations—Opus outperforms GPT-5.2 significantly.

Graduate-level reasoning (GPQA Diamond, TAU-bench): On PhD-level science questions and complex multi-step problems, Opus leads.

Important caveat: OpenAI and Anthropic report on different versions of coding benchmarks, making direct comparison difficult. Take specific numbers with appropriate skepticism.

The Pattern

Codex wins when: Speed matters, tasks are well-defined, you're automating terminal or desktop workflows.

Opus wins when: Problems require deep reasoning, context spans thousands of lines, or the AI needs to push back on bad assumptions.

What Independent Assessments Reveal (Beyond the Marketing)

Real-world evaluations paint a clearer picture than benchmarks alone.

GPT-5.3-Codex: The "Execution Machine"

Independent assessments highlight Codex as the first model where organizations can:

• Assign a task, step away, and return to working software

• Achieve end-to-end automation from code changes through deployment and monitoring

• Handle the full development lifecycle with minimal human intervention

Enterprise implication: Teams with well-documented processes and clear specifications will see immediate productivity gains. Codex excels when requirements are explicit.

The tradeoff? Codex does what you specify, not what you intend. Organizations with mature documentation and process discipline will thrive. Those relying on tribal knowledge or ambiguous requirements may struggle.

Claude Opus 4.6: The "Strategic Partner"

Opus 4.6 introduces agent teams that fundamentally change how AI integrates with enterprise workflows:

• Multiple agents handle parallel workstreams—mirroring how large teams operate

• Sub-agents can be supervised interactively, maintaining human-in-the-loop oversight

• The model plans more carefully and catches its own mistakes before they reach production

Enterprise implication: Organizations tackling complex, multi-stakeholder projects benefit from Opus's collaborative approach. It's built for environments where context matters more than speed.

The tradeoff? The 1M context window is still in beta. And while agent teams are powerful, they add orchestration complexity that smaller teams may not need.

Enterprise Case Studies: Real Results in Production

Beyond benchmarks and reviews, here's what organizations are actually reporting.

Claude Opus 4.6 in Production

Rakuten deployed Opus 4.6's agent teams to manage engineering operations across six repositories. Results: autonomously closed 13 issues and assigned 12 issues to the right team members in a single day—effectively coordinating a ~50-person organization.

Sourcegraph reported: "It breaks complex tasks into independent subtasks, runs tools and subagents in parallel, and identifies blockers with real precision."

JetBrains noted: "Claude Opus 4.6 reasons through complex problems at a level we haven't seen before. It considers edge cases that other models miss."

Vals AI benchmarks show Opus 4.6 achieving state-of-the-art on Finance Agent (60.7% on SEC filing research) and TaxEval (76.0%)—critical for regulated industries.

GPT-5.3-Codex in Production

OpenAI Frontier customers—including HP, Intuit, Oracle, and Uber—are deploying Codex for enterprise agent workflows.

Box reported 10% productivity improvements in early testing.

Independent reviewers describe Codex as "the first coding model I can start, walk away from, and come back to working software." The key enabler: judgment under ambiguity combined with built-in validation and testing.

Cybersecurity: The Elephant in the Room

OpenAI's own system card rates GPT-5.3-Codex as "High capability" for cybersecurity—the first model to receive that rating. CEO Sam Altman noted they lack "definitive evidence" the model can fully automate cyberattacks, but they're "taking a precautionary approach."

This matters for enterprises. If you're building security-critical applications, both models require careful guardrails. But Codex's enhanced capability to identify (and potentially exploit) vulnerabilities means stricter access controls are warranted.

Pricing Reality Check

Claude Opus 4.6

• API: $5/$25 per million tokens (input/output)

• 1M context mode: $10/$37.50 per million tokens

• Available now via API and all major cloud platforms

GPT-5.3-Codex

• API pricing: Not yet released (coming "in the weeks following launch")

• Current access: Paid ChatGPT plans ($20/month for Plus, higher for Business/Enterprise)

• Previous Codex: $1.25/$10 per million tokens

For API-heavy workflows, Opus 4.6's known pricing lets you budget now. Codex users are waiting on official numbers.

Which Model Wins for Your Organization?

Here's the agentic take—based on benchmarks, independent assessments, and enterprise deployment patterns.

GPT-5.3-Codex: Best for Execution-Focused Organizations

Ideal organizational profile:

• High-velocity environments: Startups, product teams shipping weekly, organizations prioritizing speed-to-market

• Infrastructure-heavy operations: DevOps teams, platform engineering, cloud automation at scale

• Process-mature organizations: Companies with strong documentation, clear specifications, and well-defined workflows

• Cost-conscious scaling: Codex's efficiency (half the tokens, 25% faster) compounds at enterprise volumes

Use cases where Codex excels:

• Automated infrastructure provisioning and monitoring

• Rapid prototyping and iteration cycles

• Terminal-heavy automation workflows

• Organizations with dedicated technical specification practices

Claude Opus 4.6: Best for Complexity-Driven Organizations

Ideal organizational profile:

• Large enterprise environments: Organizations managing massive codebases, legacy systems, and cross-functional dependencies

• Regulated industries: Financial services, healthcare, and sectors requiring deep reasoning and audit trails

• Security-first organizations: Teams where AI must identify risks and push back on problematic approaches

• Knowledge-intensive operations: Consulting, R&D, and environments where context and nuance drive decisions

Use cases where Opus excels:

• Enterprise-wide code audits and security reviews

• Complex system migrations and modernization

• Multi-stakeholder projects requiring parallel workstreams

• Organizations where AI needs to challenge assumptions, not just execute orders

The Enterprise Hybrid Strategy

Forward-thinking organizations are deploying both:

• Codex for execution: Defined tasks, automation pipelines, infrastructure operations

• Opus for strategy: Architecture decisions, security reviews, complex problem-solving

This isn't hedging—it's optimizing. The models serve different organizational muscles.

The Bottom Line for Enterprise AI Strategy

Neither model wins across the board—and that's the point. The market has matured beyond "which AI is best" to "which AI fits our operational model."

GPT-5.3-Codex is built for execution at scale. Organizations with mature processes, clear specifications, and speed-to-market priorities will see immediate ROI. It does exactly what you specify, making it ideal for automation-heavy workflows.

Claude Opus 4.6 is built for complexity and collaboration. Organizations managing large codebases, navigating regulatory requirements, or tackling multi-stakeholder initiatives will benefit from its deeper reasoning and agent team capabilities.

The strategic question isn't "Codex or Opus?" It's "Where does each model create the most value across our organization?"

For most enterprises, the answer is both—deployed strategically based on use case requirements.