Master GPT-5.1-Codex-Max with context compaction for million-token projects. Compare vs Claude Code & Cursor. Pricing, benchmarks, and best practices.
Key Takeaways
- Context Compaction Technology: GPT-5.1-Codex-Max is the first model natively trained to operate across multiple context windows through compaction, enabling coherent work over millions of tokens in a single task.
- xhigh Reasoning Effort: The new xhigh reasoning level achieves 77.9% on SWE-bench Verified with 30% fewer thinking tokens, trading latency for maximum code quality on complex problems.
- 24+ Hour Autonomous Operation: OpenAI observed the model working continuously for over 24 hours, persistently iterating through code and fixing test failures without human intervention.
GPT-5.1-Codex-Max Technical Specifications
Released November 19, 2025 by OpenAI
| Specification | Value |
|---|---|
| Context Window | Unlimited via Compaction (Millions of tokens per task) |
| Reasoning Levels | none / medium / high / xhigh (xhigh is new to Codex-Max) |
| SWE-bench Verified | 77.9% (xhigh) - n=500 evaluation |
| Terminal Bench 2.0 | 58.1% (vs Gemini 54.2%, Sonnet 42.8%) |
| API Pricing | $1.25 / $10 per 1M tokens Input / Output (Cached: $0.625) |
| Token Efficiency | 30% fewer thinking tokens vs GPT-5.1-Codex |
Key Features: Responses API Only, Native Windows Support, 24+ Hour Autonomy, Open Source CLI
OpenAI released GPT-5.1-Codex-Max on November 19, 2025, introducing the first AI model natively trained to operate across multiple context windows through a revolutionary technique called context compaction. Unlike previous iterations that focused on code completion and chat-based suggestions, Codex-Max introduces true autonomous development capabilities—planning, implementing, and testing entire features across million-token codebases with minimal human intervention. OpenAI has observed the model working continuously for over 24 hours, persistently iterating through code and fixing test failures without intervention.
For development teams and agencies, GPT-5.1-Codex-Max represents more than incremental improvement. The new xhigh reasoning effort level enables deeper analysis for complex problems, achieving 77.9% on SWE-bench Verified while using 30% fewer thinking tokens than its predecessor. Internally, 95% of OpenAI engineers use Codex weekly, shipping approximately 70% more pull requests since adoption. This guide explores how to leverage Codex-Max for autonomous coding workflows, configure reasoning effort levels, understand context compaction trade-offs, and choose the right tool when comparing with Claude Code, Cursor, Google Jules, and Devin AI.
Understanding Context Compaction: The Defining Feature
Context compaction is the breakthrough technology that sets GPT-5.1-Codex-Max apart from all other coding models. It's the first model natively trained to operate across multiple context windows, coherently working over millions of tokens in a single task. This unlocks project-scale refactors, deep debugging sessions, and multi-hour agent loops that were previously impossible.
How Context Compaction Works
- Model processes your task within its current context window
- As context approaches the limit, the model detects the approaching threshold
- Model summarizes essential state: variable definitions, architectural decisions, current bugs
- Summary carried into a fresh context window, preserving important context
- Process repeats until task completed—enabling multi-hour sessions
Compaction Trade-off: The "resolution" of memory may blur slightly over time as details are compressed. Subtle details mentioned early in long sessions can be lost. If you notice quality degradation or context loss, consider starting a fresh session rather than relying entirely on compaction for extreme-precision tasks.
The practical impact is substantial: compaction reduces overall tokens by 20-40% in long sessions, lowering costs while enabling workflows previously impossible. Unlike Gemini 3 Pro with its fixed 1M token context, GPT-5.1-Codex-Max has effectively unlimited context through iterative compaction. The feature isn't just deleting old text—it's selectively retaining the intent of previous actions, creating stability that feels less like a probabilistic generator and more like a methodical engineer reviewing their own notes.
Reasoning Effort Levels: Choosing none vs medium vs high vs xhigh
GPT-5.1-Codex-Max introduces a new xhigh reasoning effort level—the highest available—while supporting the existing none, medium, and high options. The reasoning effort parameter controls how many reasoning tokens the model generates before producing a response, directly affecting cost, speed, and quality.
| Effort Level | Best For | Cost | Speed | Quality |
|---|---|---|---|---|
| none | Quick completions, simple queries | Lowest | Fastest | Basic |
| medium (Recommended) | Daily driver, most tasks, standard development | Low | Fast | Good |
| high | Complex debugging, multi-file refactoring | Medium | Moderate | High |
| xhigh (New) | Hardest problems, legacy systems, race conditions | Highest | Slowest | Highest (77.9% SWE-bench) |
Choose medium
- Standard feature implementation
- Code review and documentation
- Cost-sensitive development
- Bulk of daily tickets
Choose high
- Complex debugging sessions
- Multi-file refactoring
- Architecture changes
- When medium falls short
Choose xhigh
- Legacy data pipeline untangling
- Fragile domain layer refactoring
- Race condition debugging
- When accuracy trumps speed
Pro Tip: Using xhigh boosted Codex-Max's SWE-bench score from 76.5% to 77.9%. That's meaningful for genuinely hard problems, but overkill for routine work. Start with medium, escalate to high when needed, and reserve xhigh for tasks that would normally "eat an afternoon of senior developer time."
GPT-5.1-Codex-Max vs Claude Code vs Cursor vs Jules vs Devin: Comparison
The agentic AI coding tool landscape is rapidly converging, with each tool developing similar capabilities. Here's how GPT-5.1-Codex-Max compares with the leading alternatives based on benchmarks, features, and real-world use cases.
| Feature | GPT-5.1-Codex-Max | Claude Code | Cursor | Google Jules | Devin AI |
|---|---|---|---|---|---|
| SWE-bench Verified | 77.9% | 72.7% | Varies by model | N/A | N/A |
| Context Window | Unlimited (compaction) | 200K tokens | Varies by model | Async operation | Async operation |
| Autonomous Time | 24+ hours observed | Hours | Background mode | Async tasks | Hours |
| Windows Support | Native (first) | No | Via IDE | No | Browser only |
| Browser Access | No | No | No | Via Jules | Yes |
| Open Source Component | CLI | No | No | No | No |
| Pricing | $1.25/$10 per 1M tokens | $17/month+ | $20/month | Free beta (60/day) | $20+ |
| Industry Adoption | 96% | Growing | High | Emerging | 67% |
Choose Codex-Max When
- Long-running autonomous tasks (hours)
- Million-token codebase processing
- Native Windows development
- Need xhigh reasoning for hard problems
- Enterprise-scale API access
Choose Claude Code When
- Larger default context needed
- Terminal-centric workflow
- Less code churn preferred (30% fewer reworks)
- Sub-agent capabilities required
- More configuration options needed
Choose Cursor When
- VS Code-centric workflow
- Quick iterations preferred
- Background agent mode needed
- IDE integration is critical
- Fast setup and deployment
Choose Google Jules When
- Free tier is sufficient (60/day)
- Async operation preferred
- Google Cloud integration needed
- CLI workflow with Jules Tools
- Speed is critical (faster than Codex)
Choose Devin AI When
- Browser access needed
- Interactive IDE preferred
- End-to-end workflow automation
- SOC 2 Type II certification required
- Complex collaborative projects
The Verdict
All tools are converging. Codex-Max leads on long-running autonomy and benchmark scores. Claude Code produces less code churn. Cursor has best IDE integration. Jules is fastest. Devin has browser access. Choose based on your workflow.
What Makes GPT-5.1 Codex-Max Different
GPT-5.1-Codex-Max differs fundamentally from standard GPT-5.1 through three core architectural enhancements specifically designed for software engineering. First, the context compaction technology enables it to maintain awareness of entire monorepo codebases during generation—not through a larger window, but through intelligent summarization that preserves essential context across sessions.
Second, Codex-Max introduces extended execution capabilities allowing up to 24+ hours of continuous autonomous work on a single task. OpenAI observed the model working this long, persistently iterating on implementation, fixing test failures, and ultimately delivering successful results. The system checkpoints progress through compaction, allowing developers to review intermediate states and adjust direction if needed.
Third, the model incorporates enhanced planning and reasoning specifically trained on software engineering workflows. Rather than generating code line-by-line, Codex-Max first creates a detailed implementation plan, identifies dependencies and potential conflicts, generates code across multiple files in dependency order, implements tests, and performs security scanning. The model was trained on real-world software engineering tasks including PR creation, code review, frontend coding, and Q&A—making it a better collaborator in professional development environments.
GitHub Copilot Workspace Integration
GPT-5.1-Codex-Max is now available in public preview for GitHub Copilot Pro, Pro+, Business, and Enterprise users. The integration enables agentic workflows where Codex-Max can plan implementations, create branches, run builds, fix failures, and submit PRs—autonomously completing in under 8 hours what takes humans days.
| Plan | Price | Codex-Max Access | Features |
|---|---|---|---|
| Copilot Individual | $10/month | Limited | Basic completions |
| Copilot Pro | $10/month | Yes | Model selection in chat |
| Copilot Business | $19/user/month | Yes | Organization policies, audit logs |
| Copilot Enterprise | $39/user/month | Full Access | 1,000 premium requests, knowledge bases, custom models |
The integration supports collaborative workflows where developers can intervene at any stage. After Codex-Max generates an implementation plan, you can approve it as-is, request modifications, or edit specific steps before execution. The workspace interface includes real-time execution monitoring, allowing teams to track Codex-Max progress across multiple concurrent tasks.
Autonomous Coding Workflows
GPT-5.1-Codex-Max excels at autonomous workflows that previously required extensive human supervision. Legacy codebase modernization represents one of the most valuable use cases—point Codex-Max at a 15-year-old PHP application and specify migration to Laravel 11, and it will analyze the existing architecture, create a migration plan with dependency ordering, incrementally refactor code modules while maintaining backward compatibility, implement automated tests for each refactored component, and document breaking changes requiring manual review.
Feature Implementation
Product managers write natural language specifications, and Codex-Max delivers:
- Technical architecture design
- Frontend components with state management
- Backend API endpoints with migrations
- Integration and unit tests
- Developer and end-user documentation
Security Remediation
Upload security scan results, and Codex-Max systematically:
- Analyzes each vulnerability in context
- Implements fixes following OWASP best practices
- Adds security tests to prevent regression
- Documents security considerations
- Works through hundreds of findings autonomously
Productivity Impact: Internally, 95% of OpenAI engineers use Codex weekly. These engineers ship roughly 70% more pull requests since adopting Codex. For a typical mid-complexity feature, Codex-Max completes implementation in 2-4 hours while maintaining comparable code quality.
Cost Optimization: Token Efficiency and Pricing Strategies
GPT-5.1-Codex-Max achieves the same SWE-bench performance as GPT-5.1-Codex while using 30% fewer thinking tokens—translating directly to cost savings. Here's how to optimize your spending.
| Model | Input (per 1M tokens) | Output (per 1M tokens) | Cached Input |
|---|---|---|---|
| GPT-5.1-Codex-Max | $1.25 | $10.00 | $0.625 |
| GPT-5.1-Codex | $1.25 | $10.00 | $0.625 |
| GPT-5.1 | $1.25 | $5.00 | $0.625 |
Cost Optimization Strategies
Use medium Reasoning by Default: Start with medium effort. Only escalate to high/xhigh when genuinely needed. Can reduce costs 30-50% while maintaining quality for most tasks.
Leverage 30% Token Efficiency: Codex-Max uses fewer thinking tokens than its predecessor. Same performance, less compute. The savings are automatic when you upgrade.
Cache Repeated Context: Cached inputs cost $0.625 vs $1.25 per 1M tokens. Maintain session continuity and leverage compaction for long sessions to maximize caching benefits.
Right-Size Task Complexity: Use standard models for simple completions. Reserve Codex-Max for genuinely autonomous tasks. The autonomy overhead isn't worth it for sub-5-minute work.
Quality and Security Controls
GPT-5.1-Codex-Max operates in a secure sandbox by default with limited file access and disabled network functionality. OpenAI rates the model at "medium preparedness," meaning it performs best in defensive/constructive roles rather than security testing. The model refuses 100% of synthetic malicious coding prompts in benchmarks and has high resistance to prompt injection during coding sessions.
| Sandbox Mode | File Access | Network | Recommendation |
|---|---|---|---|
| read-only | Read only | Blocked | Analysis and review tasks |
| workspace-write (Recommended) | Read/write in cwd and writable_roots | Blocked by default | Most development tasks |
| danger-full-access | Full access | Available | Use with extreme caution |
Security Warning: Enabling internet access introduces prompt-injection risks from untrusted content. OpenAI recommends maintaining restricted mode. Treat Codex as an additional code reviewer, not a replacement for human review before production deployment.
Enterprise users can configure custom quality gates aligned with organizational standards. Upload your company's coding standards, internal security policies, or compliance requirements (GDPR data handling, HIPAA PHI protection, SOC 2 audit requirements), and Codex-Max incorporates these rules into its generation process. On Windows, users can choose an experimental native sandboxing implementation or use Linux sandboxing via Windows Subsystem for Linux (WSL).
When NOT to Use GPT-5.1-Codex-Max: Honest Guidance
GPT-5.1-Codex-Max is powerful but not appropriate for every situation. Being honest about limitations builds trust and helps you choose the right tool for each task.
Don't Use Codex-Max For
- Quick code completions - Overkill, use standard models
- Tasks requiring browser access - Codex lacks it, use Devin
- Sub-5-minute tasks - Autonomy overhead isn't worth it
- Extreme precision over long duration - Compaction may blur details
- Security penetration testing - "Medium preparedness" only
When Human Expertise Wins
- Architecture decisions - Business context AI lacks
- Client communication - Stakeholder management is human domain
- Security-critical final review - Human judgment required
- Novel algorithm design - Creative problem-solving
- Production deployment approval - Risk decisions need humans
Common Mistakes with GPT-5.1-Codex-Max
Based on community feedback, GitHub issues, and independent testing, here are the most common mistakes teams make when adopting GPT-5.1-Codex-Max—and how to avoid them.
Mistake #1: Using xhigh Reasoning for Everything
The Error: Defaulting to maximum reasoning effort because "higher is better."
The Impact: 3-5x higher costs, slower iteration cycles, unnecessary latency for simple tasks.
The Fix: Start with medium (the recommended daily driver). Escalate to high for complex debugging, xhigh only for genuinely hard problems that would "eat an afternoon of senior time."
Mistake #2: Ignoring Compaction Warning Signs
The Error: Not noticing when context compaction loses important details during long sessions.
The Impact: Quality degradation, repeated work, wasted tokens on confused outputs.
The Fix: Monitor for signs of context loss—repeated questions about already-discussed topics, inconsistent variable naming. Consider starting fresh for precision-critical work.
Mistake #3: Skipping Checkpoint Reviews
The Error: Trusting 7+ hour autonomous runs without reviewing intermediate results.
The Impact: Destructive changes, file deletions, lost work. Users report the model "giving up" on long tasks and destroying progress.
The Fix: Review at checkpoint intervals. Independent METR evaluation suggests 80% reliability time-horizon may be closer to 2 hours—review more frequently for critical work.
Mistake #4: Using danger-full-access Sandbox
The Error: Disabling filesystem sandboxing for convenience.
The Impact: Unintended file modifications, deletions, security vulnerabilities from network access.
The Fix: Use workspace-write mode. Explicitly allow only needed access. Enable network only when absolutely necessary and understand the prompt-injection risks.
Mistake #5: Treating It Like a Literal Genie
The Error: Giving vague or overly-specific instructions without considering how literally the model interprets them.
The Impact: The model is "extremely, painfully, doggedly persistent" in following instructions exactly—working 30 minutes to convolute solutions based on forgotten constraints.
The Fix: Be precise but reasonable. Review system prompts for outdated constraints. Unlike Claude which might recognize "obvious typos," Codex-Max will follow instructions to the letter.
Real-World Agency Applications
Development agencies can leverage GPT-5.1-Codex-Max to dramatically improve project economics and delivery timelines while maintaining code quality. Client project scaffolding represents the most immediate value—instead of spending 8-12 hours setting up a new project with authentication, database migrations, CI/CD pipelines, and deployment configurations, Codex-Max completes the entire setup in 45-90 minutes based on a simple specification of tech stack and requirements.
For agencies managing multiple client projects simultaneously, Codex-Max enables parallel development workflows previously impossible with limited developer resources. A 5-person agency can effectively manage 12-15 active projects by delegating routine implementation tasks to Codex-Max—database schema updates, CRUD endpoint generation, form validation implementation, API integration code—while developers focus on architecture decisions, complex business logic, and client communication.
Technical debt remediation workflows provide ongoing value for agencies maintaining legacy client projects. Instead of accumulating expensive technical debt that eventually requires costly rewrites, agencies can use Codex-Max for continuous improvement during maintenance phases—updating deprecated dependencies, refactoring code to modern patterns, improving test coverage, and enhancing security posture. A typical maintenance contract might allocate 20% of hours to technical debt work; Codex-Max can accomplish 3-4x more improvements in the same time budget.
API Access and Custom Integration
GPT-5.1-Codex-Max is available through the Responses API only—not the Chat Completions API. The model identifier is "gpt-5.1-codex-max" and supports function calling, structured outputs, compaction, web_search tool, and the new reasoning effort parameters (none, medium, high, xhigh). API access was recently expanded beyond the Codex CLI and IDE extension to third-party tools including Cursor, GitHub Copilot, Linear, and others.
Open Source Reference: The best reference implementation is the fully open-source codex-cli agent, available on GitHub at github.com/openai/codex. Users can clone the repo and use Codex to ask questions about how things are implemented.
Custom integration patterns include automated code review agents that analyze pull requests and suggest improvements, documentation generation pipelines that extract API specifications from code and generate up-to-date documentation, testing assistants that generate comprehensive test suites based on code coverage analysis, and deployment automation that analyzes applications and generates infrastructure-as-code configurations for AWS, Google Cloud, or Azure.
Conclusion
GPT-5.1-Codex-Max represents a fundamental evolution in AI-assisted software development. The combination of context compaction for unlimited token processing, xhigh reasoning effort for maximum quality on hard problems, and 24+ hour autonomous operation enables workflows previously requiring full-time developer attention. The 30% token efficiency improvement delivers automatic cost savings, while native Windows support expands the model's reach.
However, it's not appropriate for every task. Quick completions, browser-requiring workflows, and extreme-precision long-duration tasks may be better served by alternatives. Understanding the compaction trade-offs, configuring appropriate sandbox modes, and reviewing at checkpoints are essential for successful adoption. Choose Codex-Max for long-running autonomous tasks across million-token codebases; consider Claude Code for less code churn, Cursor for IDE integration, Jules for free-tier async work, or Devin for browser access.
Frequently Asked Questions
What is GPT-5.1-Codex-Max and how is it different from standard GPT-5.1?
GPT-5.1-Codex-Max is OpenAI's specialized frontier agentic coding model released November 19, 2025. It differs from standard GPT-5.1 through three key innovations: (1) Context compaction that enables working over millions of tokens by summarizing and retaining essential information when approaching context limits, (2) Extended reasoning effort levels including the new 'xhigh' setting that achieves 77.9% on SWE-bench Verified, and (3) Native Windows environment support—the first OpenAI model trained to operate in Windows. Unlike GPT-5.1 which is a general-purpose model, Codex-Max is only recommended for agentic coding tasks.
What is context compaction and how does it work in GPT-5.1-Codex-Max?
Context compaction is the defining feature of GPT-5.1-Codex-Max. When the session approaches its context window limit, the model automatically summarizes the essential state—variable definitions, architectural decisions, current bugs—and carries that summary into a fresh context window. This process repeats until the task is completed, enabling the model to work over millions of tokens coherently. However, there's a trade-off: the 'resolution' of memory may blur slightly over time as details are compressed, so consider restarting fresh for tasks requiring extreme precision over very long durations.
How do I choose between reasoning effort levels (none, medium, high, xhigh)?
OpenAI recommends 'medium' as your daily driver—it balances intelligence and speed for most coding tasks. Use 'high' for complex debugging, multi-file refactoring, or architecture changes. Reserve 'xhigh' for your hardest problems: untangling legacy data pipelines, refactoring fragile domain layers, or chasing race conditions. The new xhigh level gives the model a very large internal thinking budget and is responsible for the top SWE-bench scores, but trades latency for reliability. Using xhigh boosted Codex-Max from 76.5% to 77.9% on SWE-bench—worthwhile for complex tasks but overkill for routine work.
How does GPT-5.1-Codex-Max compare to Claude Code?
Both are leading agentic coding tools with different strengths. Claude Code achieves 72.7% on SWE-bench Verified in some independent tests vs Codex-Max's official 77.9% at xhigh reasoning. Claude Code has a larger default context window and produces less code churn (30% fewer reworks), while Codex-Max offers unlimited context through compaction and native Windows support. Claude Code has more features (sub-agents, custom hooks), but Codex-Max's CLI is open source. Choose Claude Code for terminal-centric workflows with less iteration; choose Codex-Max for long-running autonomous tasks and million-token codebases.
How does GPT-5.1-Codex-Max compare to Cursor?
Cursor leads on setup speed, Docker deployment, and VS Code integration, while Codex-Max excels at long-running autonomous tasks. Cursor's background agent mode provides remote sandboxes for independent AI work, similar to Codex's parallel agents. Codex-Max is available inside Cursor as a model option. Choose Cursor for quick iterations and IDE-centric development; choose Codex-Max for tasks requiring hours of autonomous work across million-token codebases. Pricing is similar at around $20/month for both.
How does GPT-5.1 Codex-Max integrate with GitHub Copilot?
GPT-5.1-Codex-Max is available in public preview for GitHub Copilot Pro, Pro+, Business, and Enterprise users as of December 2025. Users can select the model in the Copilot Chat model picker from Visual Studio Code in ask, edit, and agent modes. The integration enables agentic workflows where Codex-Max can plan implementations, create branches, run builds, fix failures, and submit PRs—often completing in under 8 hours what takes humans days. Enterprise tier includes 1,000 premium requests per user.
What are the main use cases for GPT-5.1 Codex-Max in development teams?
GPT-5.1-Codex-Max excels at long-running autonomous tasks: (1) Legacy codebase modernization—analyzing million-token codebases and implementing migration strategies; (2) Feature implementation from specifications—converting product requirements into full stack implementations; (3) Comprehensive test generation for untested codebases; (4) Security remediation—scanning vulnerabilities and implementing OWASP-compliant fixes; (5) Multi-hour refactoring sessions where context compaction maintains coherence across the entire project. Internally, 95% of OpenAI engineers use Codex weekly, shipping 70% more PRs since adoption.
How long can GPT-5.1 Codex-Max work autonomously on a single task?
OpenAI has observed GPT-5.1-Codex-Max working continuously for over 24 hours on coding projects, persistently iterating through implementation, fixing test failures, and ultimately delivering successful results without human intervention. The model includes checkpointing approximately every 30 minutes through context compaction, allowing developers to review intermediate states. However, independent METR evaluation found 'generally low reliability' on very long tasks, suggesting an 80% success time-horizon may be closer to 2 hours in practice. Review at checkpoints for critical work.
What are the security and quality controls for GPT-5.1 Codex-Max generated code?
GPT-5.1-Codex-Max operates in a secure sandbox by default with limited file access and disabled network functionality. Three sandbox modes are available: read-only, workspace-write (recommended), and danger-full-access. Network sandboxing is disabled by default—enabling internet access introduces prompt-injection risks. The model refuses 100% of synthetic malicious coding prompts in benchmarks and has high resistance to prompt injection. OpenAI rates it at 'medium preparedness,' meaning it performs best in defensive/constructive roles. Treat Codex as an additional reviewer, not a replacement for human review.
Can GPT-5.1-Codex-Max accidentally delete files?
Yes, users have reported instances where GPT-5.1-Codex-Max made destructive decisions, including file deletion. GitHub issues document cases where the model 'gave up' on long tasks and destroyed work. The model is described as a 'literal genie'—extremely persistent in following instructions exactly as written, which can lead to unexpected behavior. To prevent this: (1) Use workspace-write sandbox mode, not danger-full-access; (2) Review at checkpoint intervals; (3) Never trust multi-hour autonomous runs without review; (4) Configure explicit file protection rules where possible.
How do I access GPT-5.1-Codex-Max via API?
GPT-5.1-Codex-Max is only available through the Responses API—not the Chat Completions API. The model identifier is 'gpt-5.1-codex-max'. It supports function calling, structured outputs, compaction, web_search tool, and reasoning effort parameters (none, medium, high, xhigh). Pricing is $1.25 per 1M input tokens and $10 per 1M output tokens, with cached inputs at $0.625 per 1M. API access was recently expanded beyond Codex CLI/IDE to third-party tools including Cursor, Linear, and others.
What is the difference between GPT-5.1-Codex and GPT-5.1-Codex-Max?
GPT-5.1-Codex-Max is the upgraded version with three key improvements: (1) Support for 'xhigh' reasoning effort—the highest reasoning level available, achieving better benchmark scores; (2) Native context compaction training for working over millions of tokens; (3) 30% fewer thinking tokens for equivalent performance, translating to cost savings. GPT-5.1-Codex-Max has replaced GPT-5.1-Codex as the default in Codex surfaces. Both use the same pricing ($1.25/$10 per 1M tokens).
What Windows features does GPT-5.1-Codex-Max support?
GPT-5.1-Codex-Max is the first OpenAI model trained to operate in Windows environments. It includes robust Windows PowerShell support with understanding of syntax differences from Bash, can navigate Windows file systems and permission structures, and can execute PowerShell scripts safely in a sandbox to test its own code. On Windows, users can choose an experimental native sandboxing implementation or use Linux sandboxing via Windows Subsystem for Linux (WSL).
How much does GPT-5.1 Codex-Max cost and what are the pricing tiers?
API pricing: $1.25 per 1M input tokens, $10 per 1M output tokens, $0.625 per 1M cached input tokens. GPT-5.1-Codex-Max is available through ChatGPT Plus ($20/month), Pro ($200/month), Business, Edu, and Enterprise plans. GitHub Copilot access: Business at $19/user/month, Enterprise at $39/user/month (includes 1,000 premium requests, knowledge bases, custom models). The model's 30% token efficiency improvement translates to real cost savings compared to GPT-5.1-Codex.
How does Google Jules compare to GPT-5.1-Codex-Max?
Google Jules operates as an asynchronous coding assistant with a free beta tier (60 daily tasks, 5 concurrent tasks). It clones codebases into secure Google Cloud VMs and launched Jules Tools CLI in October 2025. Jules is significantly faster for specific tasks and provides structured architectural refactors with professional code reviews. However, Codex-Max has broader industry adoption, longer autonomous runtime (24+ hours vs async tasks), context compaction for million-token projects, and is available in more tools. Choose Jules for free tier access and quick async tasks; choose Codex-Max for long-running autonomous work.
What are the limitations of context compaction?
While context compaction enables working over millions of tokens, there are trade-offs: (1) Memory 'resolution' may blur over time—subtle details can be lost in summarization; (2) Intermittent quality dips are possible during the compaction process itself; (3) The model may lose important nuanced context that was mentioned early in a long session. Community reviews note these dips likely stem from aggressive summarization. For tasks requiring extreme precision over very long durations, consider starting fresh sessions rather than relying entirely on compaction.
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