Claude Opus 4.6 vs GPT-5.3-Codex: Which Agentic Coding Model Offers the Best Value

Anthropic's Claude Opus 4.6 and OpenAI's GPT-5.3-Codex go head-to-head in agentic coding, but are there any differences?

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Written by Akash Dutta, Edited by Rohan Pal | Updated: 6 February 2026 19:23 IST
Highlights
  • Claude Opus 4.6 gets a context window of one million tokens
  • GPT-5.3-Codex comes with an interactive steering feature
  • OpenAI’s AI model is designed for Codex

Anthropic’s latest AI model outperforms Claude Opus 4.5 on benchmarks like Terminal-Bench 2.0

Photo Credit: Unsplash/Goran Ivos

Agentic coding models have come a long way, evolving from simple code completers into full-fledged collaborators that manage entire workflows. With the enterprise space presenting a major revenue opportunity, all the big artificial intelligence (AI) players are trying to capture this market. On Thursday, Anthropic's Claude Opus 4.6 and OpenAI's GPT-5.3-Codex arrived on the scene, offering long context retention, improved tool calling, and overall coding automation. But with overlapping strengths, the real question boils down to value. Which one delivers more bang for the buck in terms of performance, safety, and everyday utility?

GPT-5.3-Codex: Details

OpenAI's latest release unifies the coding prowess of its GPT-5.2-Codex predecessor with the broader reasoning from GPT-5.2, all wrapped in a single, streamlined package. Speed stands out as a major upgrade. The model clocks in 25 percent faster than earlier versions due to optimisations in the inference stack and co-design with Nvidia's GB200 NVL72 systems. That efficiency shines during long-running tasks, where it juggles research, tool integration and complex executions without lagging.

Interactivity sets GPT-5.3-Codex apart. It offers real-time steering, so users can jump in with questions, adjustments or debates midway through a process. The company claims that users will also get frequent progress updates and handle parallel tasks without losing context. Interestingly, the model even contributed to its own creation, assisting the Codex team in debugging training runs and diagnosing evaluations.

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On the technical side, it requires fewer tokens for similar outputs, which trims costs and latency. Expanded beyond pure code, it supports the full software lifecycle, from writing product requirement documents to monitoring deployments. In web development, it crafts intricate games like a racing simulator with dynamic maps or a diving adventure managing oxygen levels, iterating autonomously over millions of tokens.

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Safety gets a thorough treatment too. Classified as High capability under OpenAI's Preparedness Framework for cybersecurity, it comes with specialised training to spot vulnerabilities.

Claude Opus 4.6: Details

Anthropic's Claude Opus 4.6 builds on the Opus 4.5 foundation with a sharper focus on sustained performance in coding and agentic scenarios. The standout feature is the context window of one million tokens (in a beta version), a first for Opus-class models. This enables the model to handle massive codebases or extended sessions. Context compaction in beta summarises older data to maintain efficiency, while adaptive thinking dynamically ramps up reasoning based on task complexity.

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Multilingual coding and tool use see boosts, with support for agent teams in Claude Code's research preview for parallel workflows. Product integrations expand its reach: an upgraded Claude in Excel manages unstructured data and multi-step edits, while a new Claude in PowerPoint preview generates on-brand slides from templates.

Coming to safeguards, Anthropic claims that the model shows low rates of deception or over-refusals. It also gets new cybersecurity probes, which let Claude Opus 4.6 detect potential misuse. The company has also integrated support for interpretability tools for internal monitoring.

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Claude Opus 4.6 vs GPT-5.3-Codex: Differences

Before going ahead with the comparison, it should be noted that the scope of these two AI models is not the same. While Anthropic's model is a general-purpose foundational model that can perform a wide range of tasks, of which agentic coding is a part. On the other hand, OpenAI's model is made for Codex, its coding app for developers, and it specialises in agentic coding. However, a focused comparison of benchmark scores does reveal where these models stand.

Benchmarks reveal a neck-and-neck battle, with each model claiming wins in key areas. On SWE-Bench Pro, software engineering test, GPT-5.3-Codex edges ahead at 56.8 percent accuracy, slightly above Claude Opus 4.6's strong showing on the related SWE-bench Verified at 81.42 percent with optimised prompting. Terminal-Bench 2.0 sees GPT-5.3-Codex at 77.3 percent, but Claude Opus 4.6 leads overall on this command-line proficiency metric when run with its tools.

In agentic tasks, Claude Opus 4.6 outperforms on GDPval-AA, surpassing OpenAI's GPT-5.2 by about 144 Elo points, translating to a roughly 70 percent win rate, although GPT-5.3-Codex holds steady at 70.9 percent wins or ties on GDPval.

Pricing tips the scales for value. Claude Opus 4.6 starts at $5 (roughly Rs. 453) per million input tokens and $25 (roughly Rs. 2,300) for output, with premiums for extended contexts. GPT-5.3-Codex ties into paid ChatGPT plans, with application programming interface (API) access expected to arrive soon. But, currently, it has no standalone token rates.

Which AI Model Is Most Suited for Your Workflow?

Choosing between the two models depends on specific needs. For developers working on large-scale enterprise projects, Claude Opus 4.6 might offer more value if massive context windows and adaptive reasoning are a priority. For instance, Anthropic's model will do a better job at migrating multimillion-line codebases or handling multilingual tasks across different teams.

On the flip side, GPT-5.3-Codex fits workflows requiring speed and interactivity. Independent developers or those working in startups might find it more useful if they spend time iterating on web games or full lifecycle software. The faster runtime and real-time steering also offer more control while prioritising speed. Additionally, for budget-conscious users, tying into existing ChatGPT subscriptions adds convenience without extra setup.

However, determining a clear winner is not possible without extensively testing both AI models and scrutinising their capabilities in core tasks and advanced agentic performance. Once the models are widely available to developers, a clear consensus could emerge.

 

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