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GPT-5.1-Codex-Max
- Code Generation
GPT-5.1-Codex-Max is an OpenAI code-focused model, optimized for software development assistance and complex programming tasks. It is notable for its strong capabilities in code generation, understanding, and transformation across multiple languages.
About the model
What is GPT-5.1-Codex-Max?
GPT-5.1-Codex-Max is an OpenAI model specialized in coding and software-related reasoning. It is mainly used for generating and editing source code, explaining code behavior, and helping debug complex programming issues. It can also support tasks like code migration, API integration guidance, and producing developer-focused documentation or examples. It follows earlier OpenAI Codex-style and GPT-family models focused on programming assistance.
Model capabilities
5 Core Capabilities
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Interactive Chat
Engages in multi-turn conversations, follows complex instructions, and maintains context to produce coherent, helpful responses across many topics.
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Code Reasoning
Understands, generates, and explains code in multiple languages, assisting with debugging, refactoring, and algorithmic problem solving tasks.
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Visual Understanding
Interprets input images to identify objects, read diagrams, and relate visual content to textual questions or instructions.
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Text Translation
Translates between many languages while preserving meaning and tone, supporting cross-lingual reading, drafting, and information access.
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Text Extraction
Reads and extracts structured information from documents, screenshots, and other visual text sources for downstream analysis or automation.
Use cases
6 Most Valuable Use Cases
- Software Code Generation
- Code Review Assistance
- Bug Detection Support
- API Integration Drafting
- Configuration File Editing
- Log Parsing Automation
Transparent pricing
Cost Comparison
LLM API offers the lowest cost and highest performance for GPT-5.1-Codex-Max–class models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | ~120ms | ~80 tps | ~99.99% | ~$0.25 | ~$0.75 | ~256K tokens |
| OpenAI | Global | ~180ms | ~50 tps | ~99.9% | ~$0.60 | ~$1.80 | ~200K tokens |
| Azure OpenAI | US East | ~190ms | ~45 tps | ~99.9% | ~$0.65 | ~$1.90 | ~200K tokens |
| Anthropic (Claude-equivalent) | US West | ~200ms | ~40 tps | ~99.9% | ~$0.80 | ~$2.40 | ~200K tokens |
| Google (Gemini-equivalent) | Global | ~210ms | ~35 tps | ~99.9% | ~$0.70 | ~$2.10 | ~200K tokens |
Performance benchmarks
Technical Specifications
| Metric | GPT-5.1-Codex-Max | Claude 3.7 Sonnet-Code | Gemini 2.0 Code-Ultra |
|---|---|---|---|
| Avg Latency | ~180ms | ~220ms | ~250ms |
| Context Window | 256K | 200K | 128K |
| Input Price ($/1M) | $2.50 | $3.00 | $2.80 |
| Output Price ($/1M) | $10.00 | $12.00 | $11.00 |
| Max Output Tokens | 8K | 8K | 4K |
| Throughput | 60 tps | 45 tps | 40 tps |
| Uptime | 99.9% | 99.5% | 99.5% |
30-day usage via LLM API
- 128B
- Prompt tokens processed (last 30 days)
- 32M
- Completion tokens generated (last 30 days)
- 3.4M
- API requests served (last 30 days)
- 99.95%
- Avg uptime (last 30 days)
Architecture & Integration
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
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Unified AI Routing
Dynamically route each request to the best model across providers using rules and performance signals—no client changes required, just smarter traffic for every call.
One endpoint, any model. -
Cost-Aware Orchestration
Optimize for price and performance with per-call cost controls, budget guards, and automatic model downgrades when quality thresholds are safely met.
Lower spend, same output. -
Automatic Smart Fallbacks
Stay resilient with transparent failover across regions and providers, retry logic, and graceful degradation—so outages and rate limits never break your app.
No single point of failure. -
Full-Stack Observability
Trace every token across models with latency, cost, and quality metrics, plus structured logs for debugging prompts, payloads, and provider behavior.
See every call, instantly. -
Task-Level Abstractions
Call high-level tasks—chat, tools, RAG, vision—through one consistent API, while LLM.API handles provider-specific quirks, parameters, and best practices.
Program tasks, not models. -
High-Throughput Batch
Ship massive workloads with parallelized batching, rate-limit aware scheduling, and cost tracking so you can process millions of requests reliably and cheaply.
Scale to millions of calls.
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a top-tier code generation model for complex, multi-file software development tasks.
- You need automated refactoring, optimization, and documentation of large legacy codebases across languages.
- You need an assistant that can design, implement, and test APIs or microservices end-to-end.
- Your use case involves generating high-quality unit, integration, and property-based tests at scale.
- Your use case involves interactive debugging support that explains errors and proposes concrete code fixes.
- You need reliable code translation between programming languages while preserving behavior and performance characteristics.
- Your use case involves building developer tools, IDE integrations, or code review automation workflows.
Avoid if...
- You need a minimal model focused on simple chat or FAQ-style natural language responses.
- You need strictly on-device inference where large cloud-hosted models are not acceptable.
- Your workload requires ultra-low latency responses for high-frequency trading or similar scenarios.
- Your workload requires processing highly sensitive code without using external or third-party cloud services.
- You need a very small, inexpensive model for trivial code completions or snippets.
- You need guaranteed deterministic outputs suitable for formal verification or safety-critical systems.
- Your workload requires only non-coding tasks, making a specialized coding model unnecessary overhead.
FAQ
Frequently Asked Questions
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What is GPT-5.1-Codex-Max?
GPT-5.1-Codex-Max is an advanced OpenAI code-focused language model optimized for software development, debugging, and complex multi-file code reasoning via LLM.API.
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What is GPT-5.1-Codex-Max best suited for?
It excels at generating and refactoring code, explaining complex codebases, creating tests, and performing multi-step reasoning over large repositories and technical documentation.
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How is GPT-5.1-Codex-Max priced on LLM.API?
Pricing is usage-based per input and output token, with exact rates shown in your LLM.API dashboard and billing documentation.
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What is the context window of GPT-5.1-Codex-Max?
GPT-5.1-Codex-Max supports a large context window suitable for multi-file projects; check LLM.API model specs for the current exact token limit.
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How fast is GPT-5.1-Codex-Max in terms of latency?
Typical latencies are in the low-seconds range depending on prompt size and concurrency, with streaming responses available to reduce perceived delay.
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What input and output modalities does GPT-5.1-Codex-Max support?
It supports text-only inputs and outputs, making it ideal for code, logs, configuration files, and natural language instructions.
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How do I call GPT-5.1-Codex-Max through LLM.API?
Use the LLM.API endpoint with the provider set to OpenAI and the model parameter set to gpt-5.1-codex-max, passing messages and settings as usual.
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How does GPT-5.1-Codex-Max compare to general-purpose GPT-5.1 models?
Compared to general GPT-5.1 chat models, it is more accurate and opinionated for coding tasks but less optimized for open-ended conversation or creative writing.
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Does GPT-5.1-Codex-Max support tools like code execution or retrieval through LLM.API?
Yes, when configured, LLM.API can route tool calls such as code execution or retrieval-augmented generation using GPT-5.1-Codex-Max outputs.
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What are the main limitations of GPT-5.1-Codex-Max?
It can generate incorrect or insecure code, lacks real-time project environment awareness, and should not be used without human review for production-critical changes.