Deep Dive into GPT-5.1-Codex-Max

How It Compares to Previous Versions, Gemini 3, and Claude as a Serious Agentic Coding Model

1. What You’ll Learn Here and Who This Is For

Let’s first be clear about who this “GPT-5.1-Codex-Max” is actually relevant for.

People who will benefit the most:

• Those developing in-house services or their own products
  • Web engineers / backend engineers
  • Frontend engineers / full-stack engineers
• People at SIers, contract development firms, or startups
  who “face a large existing codebase every single day”
• Those already using GitHub Copilot, Claude, or Gemini,
  and now considering OpenAI’s Codex as the “next move”
• Tech leads, VPoEs, and other leaders
  who need to choose an AI development platform while watching team productivity and costs

In this article, we’ll:

1. Organize the key characteristics of GPT-5.1-Codex-Max, comparing it with earlier versions (GPT-5.1-Codex / GPT-5-Codex)
2. Explain differences in coding performance and usability compared to Google Gemini 3 and Claude (3.7 / 4 Sonnet)
3. Offer practical guidance on “Which model should I use for what?” from a real-world perspective

We’ll take it slowly and unpack things step by step.

2. What Is GPT-5.1-Codex-Max? A Quick Overview

2-1. Positioning: The “Flagship” Agent in the Codex Series

According to the official OpenAI blog, GPT-5.1-Codex-Max is described as:

“A new frontier-class agentic coding model.”

Roughly speaking:

• It is based on the latest reasoning-focused model (GPT-5.1 family)
• On top of that, it is trained for:
  • Software engineering
  • Mathematics
  • Research-style tasks
    i.e., tasks that require “agent-like, multi-step work”
• It is specifically optimized for Codex use cases (CLI / IDE extensions / cloud / code review),
  and tuned so that it can autonomously handle long-running, large-scale development tasks

2-2. The Biggest Feature: Cross-Context “Compaction”

The key word that sets GPT-5.1-Codex-Max apart is compaction.

• Traditional LLMs:
  Once you approach the context window limit (the number of tokens a model can “keep in mind” at once),
  you need to drop past conversation history or portions of the code.
• GPT-5.1-Codex-Max:
  When the session approaches that limit, it compresses the history, preserving only important information,
  thereby freeing up context and enabling the model to continue working.

According to the official description:

• It can “consistently handle tasks on the order of millions of tokens spanning multiple context windows.”
• In internal evaluations, they confirmed it can work continuously for more than 24 hours, fixing failing tests and eventually producing final outputs.

This makes it much easier to assign it:

• Refactors of huge, monolithic repositories
• Large-scale test fixes + CI pipeline adjustments
• Long-running agent loops (bug fix → test → fix again → …)

In other words, tasks that cannot realistically be done in 1–2 hours.

2-3. Benchmarks like SWE-bench Verified

A common metric for coding performance is SWE-bench Verified,
which asks models to solve real GitHub issues in real repos.

Based on public information, the rough positioning is:

• GPT-5-Codex: SWE-bench Verified 74.5% (OpenAI)
• GPT-5.1-Codex: around 73–74% (various external / unofficial mentions)
• GPT-5.1-Codex-Max:
  • SWE-bench Verified 77.9% (Diamond class, up from 73.7% for 5.1-Codex under the same conditions)
  • Under the same medium reasoning setting, 30% fewer “thinking tokens” while improving accuracy

Exact numbers vary slightly by report, but the general pattern is:

“5.1-Codex-Max is several points more accurate than the previous Codex generation,
and can do the same job with fewer tokens.”

2-4. Reasoning Modes: medium and xhigh

There’s also a slightly unusual setup for reasoning modes:

• The usual medium (standard level of internal reasoning)
• A deeper, longer-thinking xhigh (Extra High) mode

The intended usage split:

• Everyday coding: medium recommended
• High-stakes tasks like “absolutely must not miss this bug” or complex algorithm design: xhigh

3. Comparing GPT-5.1-Codex-Max with GPT-5.1-Codex / GPT-5-Codex

Now let’s focus on differences from previous versions.

3-1. Architecture and Training Differences

The main differences can be grouped into three points:

1. Training for Long-Horizon Tasks

   • GPT-5-Codex and GPT-5.1-Codex can do agent-style tasks, but
     they were not explicitly trained on “tasks spanning multiple context windows.”
   • GPT-5.1-Codex-Max, by contrast, is trained from the start assuming
     long-running, long-context tasks with compaction in the loop.

2. Training on Windows Environments

   • GPT-5.1-Codex-Max is the first in the Codex line
     to explicitly train on tasks involving Windows environments.
   • Since many enterprise development environments are still Windows-based,
     this is a very practical improvement.

3. Co-optimization with Codex CLI

   • It’s further trained on tasks to make its interactions inside Codex CLI—
     tool calls, conversational responses, etc.—more fluid and robust.

3-2. Benchmarks and Token Efficiency

Let’s整理 the benchmarks:

• GPT-5-Codex
  • SWE-bench Verified: 74.5% (OpenAI)
• GPT-5.1-Codex
  • Official numbers are limited, but reports generally place it in the low 70s.
• GPT-5.1-Codex-Max
  • SWE-bench Verified: 77.9% (up +4.2 pts from 73.7% for 5.1-Codex under the same Diamond setting)
  • With medium reasoning, 30% fewer thinking tokens than 5.1-Codex

In practice, this means:

• For similar levels of bug fixing or PR creation,
  “5.1-Codex-Max has a higher chance of eventually producing a working solution with fewer tokens.”
• If you can tolerate extra latency,
  xhigh mode gives you headroom to push accuracy even further.

When just “writing small snippets in chat,” the difference may be minor.
But for teams trying to run production-like development flows on Codex,
the benefits of the Max version become much clearer.

3-3. Differences You’ll Actually Feel as an Engineer

From the perspective of someone writing and maintaining code all day,
how does GPT-5.1-Codex-Max differ from 5.1-Codex?

• Large-scale refactors of monolithic web apps

  • Previously, context would often “break,” and the model would lose track of earlier parts.
  • With compaction: less “amnesia” even for long-running tasks across the repo.

• Long-running agent loops

  • Repeated cycles of test → locate failure → fix → re-test, dozens of times
  • Now: lower risk of the model losing the thread mid-way,
    and a higher “chance it will see the task through to completion.”

• Cost

  • 30% fewer tokens at medium compared to 5.1-Codex
  • For long-running tasks, this adds up at the monthly bill level

So yes, if you’re just doing small one-off code generations in chat, the difference is modest.
But the more your use case looks like “real-world, multi-hour development,”
the more GPT-5.1-Codex-Max pulls ahead.

4. Comparing with Gemini 3 and Claude (Focusing on Coding)

Next, let’s compare it to other major models, primarily via SWE-bench Verified.

4-1. Rough Score Comparison

Aggregating widely cited numbers (for coding tasks only), we get:

• GPT-5.1-Codex-Max
  • SWE-bench Verified: 77.9% (Diamond setting, up from 73.7% for 5.1-Codex)
• Gemini 3 Pro
  • SWE-bench Verified: 76.2% (per Google’s blog/docs)
• Claude Sonnet 4
  • SWE-bench: 72.7% (Anthropic)
• Claude 3.7 Sonnet
  • SWE-bench Verified: 62.3% (70.3% with custom scaffolding)

Evaluation setups (agent scaffolding, tools, etc.) are not perfectly identical,
so you should treat these as broad indications, not exact apples-to-apples comparisons.

In rough terms:

At the very top for coding you have
GPT-5.1-Codex-Max ≈ Gemini 3 Pro (with Deep Think),
slightly below that Claude Sonnet 4,
and then a half-step behind: Claude 3.7 Sonnet / GPT-5-Codex, etc.

4-2. GPT-5.1-Codex-Max vs Gemini 3 Pro

Common ground:

• Both are designed as agentic coding models
• Both support long-horizon tasks
• Both integrate with existing CLI / IDE / cloud environments

Differences that stand out:

1. Platform Integration Direction

   • GPT-5.1-Codex-Max
     • Integrates tightly with Codex CLI, VS Code extensions, various IDEs, and cloud execution environments
       in the OpenAI-centric ecosystem.
   • Gemini 3 Pro
     • Deeply integrated with the Google ecosystem: Gemini CLI, Gemini Code Assist, Antigravity (AI-first dev platform), etc.
       Excellent fit with GCP, Vertex AI, and Google Workspace.

2. Multimodal and “Vibe Coding”

   • Gemini 3 is particularly strong for visual-heavy coding:
     UI from screenshots, design-driven component generation,
     understanding images and videos as part of the coding workflow.
   • GPT-5.1-Codex-Max is very capable on frontend and UI generation too,
     but the model is more explicitly focused on software engineering and long-horizon tasks.

3. Long-Horizon Agent Tuning Philosophy

   • GPT-5.1-Codex-Max:
     Focuses on spanning multiple context windows via compaction to complete tasks.
   • Gemini 3:
     Emphasizes deep reasoning within a context (e.g., Deep Think) plus strong CLI/tool integration.

Rough usage split:

• If your organization is heavily invested in Google Cloud and Google Workspace
  → it’s natural to center on Gemini 3 Pro.
• If you already use OpenAI’s stack (ChatGPT / Codex)
  → it’s natural to upgrade to GPT-5.1-Codex-Max.
• At a pure benchmark level, they’re “essentially peers,”
  so you can safely decide based on ecosystem and operations fit.

4-3. GPT-5.1-Codex-Max vs Claude (3.7 / 4)

Claude is extremely strong in:

• Natural language clarity
• Following instructions (spec adherence)
• General reasoning

On the coding side:

• Claude 3.7 Sonnet: SWE-bench Verified 62.3% (70.3% with custom scaffolding)
• Claude Sonnet 4: SWE-bench 72.7%

Sonnet 4 is quite strong, but as a specialized agentic coding model,
it still lags slightly behind GPT-5.1-Codex-Max and Gemini 3 Pro.

However, Claude shines when you need:

• To feed it long specs, meeting notes, and design docs,
  and have it summarize or cleanly organize requirements
• To draft architecture docs and PR descriptions in very natural Japanese/English
• To generate polite, empathetic, and clear code review comments

In other words, Claude is superb for “coding-adjacent communication and documentation work.”

So a powerful pattern is:

• Agentic coding: GPT-5.1-Codex-Max or Gemini 3
• Specification and design docs / review comments: Claude Sonnet 4

i.e., many teams will increasingly use them in a complementary fashion.

5. Practical Usage Scenarios

Let’s walk through concrete patterns of how you might actually use these models.

5-1. Large-Scale Refactor of an Existing Monolithic Web Service

• A large monolithic Rails / Laravel / Spring app
• Test coverage is “okay,” but DB schemas and service classes are spaghetti-fied

Recommended setup:

• Main driver for code changes: GPT-5.1-Codex-Max (via Codex CLI + IDE extensions)
  • Load the repo, then let it gradually:
    • Reorganize packages,
    • Extract modules,
    • Factor out common logic,
      over multiple days if needed.
• Architecture review and refactor strategy discussions: Claude Sonnet 4
  • “Here’s how I’m thinking of splitting this. Any architectural risks?”
  • “Turn this rough diagram into a proper document,” etc.

Long-horizon tasks with compaction are exactly where GPT-5.1-Codex-Max shines.

5-2. Greenfield UI-Centric Product (Mobile/Web) from Zero

• New service where code is still small but UI/UX is critical
• You want to rapidly generate UI components in sync with Figma/design systems

Recommended setup:

• UI prototyping & vibe coding: Gemini 3 Pro (Code Assist / Stitch / Antigravity)
• Backend design & implementation / CI setup: GPT-5.1-Codex-Max or GPT-5.1-Codex
• Specs and requirements documents: Claude Sonnet 4

Gemini 3’s “vibe coding” (generating UI from mixed text + visuals)
is very powerful for UI-first products.

5-3. “One-Stop Shop” AI Coding for a Small Team

• Startups or small dev shops where 1–2 people handle full-stack
• You don’t want overly complex agent setups at first;
  you want to start with chat + a bit of auto-fixing and PR creation

Recommended setup:

• First choose either:
  • ChatGPT (with GPT-5.1 + Codex integrated), or
  • Gemini 3 Pro (Gemini Advanced / Code Assist)
    as your main entry point
• Gradually add:
  • Automated PR creation
  • Automated code review
  • IDE integrations for autocompletion and test fixing

Whether you go “all in on Codex” or “all in on Gemini” can simply be decided by:

• Your existing cloud stack (GCP or not)
• Team preferences
• Pricing and quotas

6. Rough Overview of Pricing and Delivery Models

6-1. GPT-5.1-Codex-Max

• Availability:
  • Available within Codex for users of ChatGPT Plus / Pro / Business / Edu / Enterprise,
    with Max as the default model.
  • API access via Codex CLI is “coming soon.”
• Pricing:
  • Included under each ChatGPT plan with per-usage details depending on the plan.

Exact per-token prices update frequently in OpenAI’s docs,
so for production deployments, always check the latest official pricing.

6-2. Gemini 3 Pro

• Exposed through Google AI Studio, Vertex AI, Gemini Advanced, etc.
• Gemini Code Assist and Gemini CLI come with fairly generous free tiers for individuals.

6-3. Claude Sonnet 4

• Available via Claude Pro, Claude for Work, and API
• Follows the familiar input vs output token metered pricing plus monthly fees

7. Which Model Should You Choose? A Quick Flow

Here’s a simple way to think about “Which model should we center on?”

7-1. Key Questions

1. What cloud ecosystem are you on?

   • Mostly GCP + Google Workspace → consider Gemini 3 Pro as your primary model
   • Already using ChatGPT Enterprise / Business → make GPT-5.1-Codex-Max your core
   • Undecided / small-scale → use free tiers of both and test them

2. What’s the main use case?

   • Long-horizon refactors and debugging on large repos
     → GPT-5.1-Codex-Max (compaction + xhigh are strong differentiators)
   • Ground-up UI/UX-heavy web or mobile apps
     → Gemini 3 Pro (vibe coding + Stitch + Code Assist)
   • Need to generate large volumes of specs, design docs, and legal-ish text
     → Add Claude Sonnet 4 as your “document specialist”

3. How mature is your team?

   • Comfortable building rich agent workflows
     → Aim for SWE-bench-level tasks; build full workflows on GPT-5.1-Codex-Max or Gemini 3 Pro
   • Want to start with chat + simple automatic PR flows
     → There’s little difference between the two at this stage—
     choose based on pricing, UI, and organizational preference.

8. Summary: GPT-5.1-Codex-Max as a Step Toward “Real” Production-Level Coding Agents

To wrap up the main points:

• GPT-5.1-Codex-Max is a
  long-horizon, long-context agentic coding model

  • With compaction, it can span multiple context windows and
    handle million-token-level tasks running for 24+ hours.

• On benchmarks like SWE-bench Verified,
  it outperforms GPT-5.1-Codex while using 30% fewer thinking tokens,
  meaning it is evolving toward being “stronger and cheaper.”

• Gemini 3 Pro offers comparable coding performance plus
  stronger UI/multimodal capabilities and deep Google ecosystem integration.

• Claude Sonnet 4, while also strong at coding, really shines in
  spec organization, documentation, and review comments—
  the “surrounding work” around coding.

So the high-level mental model:

“If you want to build robust agentic development flows end to end”
→ GPT-5.1-Codex-Max or Gemini 3 Pro

“If you also care about very high-quality human-facing prose”
→ Add Claude Sonnet 4 on top, and run a three-model stack

Thinking of it this way should help you make sense of the current option space
and choose whichever combination best fits your team and your product.