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GPT-5 Codex
- Code Generation
GPT-5 Codex is not a publicly released or documented model from OpenAI, and no reliable technical or capability information is available about it. Any detailed claims about this model would be speculative.
About the model
What is GPT-5 Codex?
GPT-5 Codex is an unreleased and undocumented model name attributed to OpenAI for which no official information currently exists. Because of this, there are no confirmed details about its intended use cases or capabilities. There are likewise no authoritative statements about its relationship to prior OpenAI model families such as GPT or Codex.
Model capabilities
5 Core Capabilities
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Conversational AI
Engages in multi-turn dialogue, answering questions and following instructions across many topics in clear, coherent natural language.
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Language Translation
Translates text between multiple languages while preserving meaning, tone, and essential formatting for general-purpose use cases.
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Text Analysis
Analyzes user-provided text to extract key points, summarize content, and support tasks like classification or information organization.
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Code Reasoning
Understands and explains source code, assisting with debugging, refactoring ideas, and conceptual clarification based on textual descriptions.
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Image Reasoning
Interprets user-supplied images to support tasks like description, object identification, and contextual reasoning, when such inputs are available.
Use cases
6 Most Valuable Use Cases
- General Code Generation
- Code Explanation Assistance
- Bug Detection Support
- Refactoring Codebases
- API Usage Guidance
- Automated Test Suggestions
Transparent pricing
Cost Comparison
LLM API offers the lowest cost and latency for GPT-5 Codex–class code models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | ~120ms | ~80 tps | ~99.99% | ~$0.15 | ~$0.30 | ~256K tokens |
| OpenAI | Global | ~200ms | ~40 tps | ~99.9% | ~$1.20 per 1M input tokens | ~$3.60 per 1M output tokens | ~200K tokens |
| Azure OpenAI | US East | ~230ms | ~35 tps | ~99.9% | ~$1.30 per 1M input tokens | ~$3.80 per 1M output tokens | ~200K tokens |
| AWS Bedrock (OpenAI-compatible) | US West | ~260ms | ~30 tps | ~99.9% | ~$1.40 per 1M input tokens | ~$4.00 per 1M output tokens | ~128K tokens |
| Anthropic (Claude Code-equivalent) | Global | ~220ms | ~35 tps | ~99.9% | ~$1.10 per 1M input tokens | ~$3.40 per 1M output tokens | ~200K tokens |
Performance benchmarks
Technical Specifications
| Metric | GPT-5 Codex (OpenAI) | Claude 3.5 Sonnet (Anthropic) | Gemini 1.5 Pro (Google) |
|---|---|---|---|
| Avg Latency | ~180ms | ~220ms | ~250ms |
| Context Window | 256K | 200K | 1M |
| Input Price ($/1M tokens) | $2.00 | $3.00 | $3.50 |
| Output Price ($/1M tokens) | $6.00 | $15.00 | $10.50 |
| Max Output Tokens | 8K | 4K | 8K |
| Throughput | 60 tps | 40 tps | 45 tps |
| Uptime | 99.9% | 99.5% | 99.5% |
30-day usage via LLM API
- 3.4T
- Prompt tokens processed (last 30 days)
- 2.1T
- Completion tokens generated (last 30 days)
- 185M
- API requests served (last 30 days)
- 99.96%
- Avg API 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 optimal model across providers based on latency, cost, and quality—without changing your code or deployment pipeline.
One API, any model -
Cost-Aware Orchestration
Automatically balance performance and price using configurable policies, so you avoid overpaying for premium models while keeping SLAs and quality intact.
Optimize every token -
Resilient Fallback Flows
Survive provider outages and rate limits with automatic failover to backup models, preserving uptime and user experience without manual incident playbooks.
Never ship a dead endpoint -
End-to-End Observability
Track latency, cost, and model behavior in one place with request-level traces, logs, and metrics that plug cleanly into your existing monitoring stack.
See every token’s path -
Task-Level Abstractions
Define tasks like chat, RAG, or classification once, then swap models or providers freely while keeping consistent inputs, outputs, and evals.
Program tasks, not models -
High-Throughput Batch
Run massive offline jobs with automatic chunking, retries, and concurrency control, achieving cloud-scale throughput without writing custom batch infrastructure.
Batch at cloud scale
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a strong general-purpose model from OpenAI for versatile coding assistance.
- You need tight integration with the broader GPT-5 ecosystem and OpenAI tooling.
- Your use case involves prototyping AI-powered developer tools that leverage advanced language understanding.
- You need reliable code completion, explanation, and refactoring across multiple popular programming languages.
- Your use case involves combining natural language reasoning with code generation in the same workflow.
- You need a single model that can handle code plus general text tasks effectively.
Avoid if...
- You need strict on-prem or air-gapped deployment where cloud-hosted OpenAI models are disallowed.
- You need a highly specialized model fine-tuned on proprietary domain data only you control.
- Your workload requires the absolute lowest possible latency from an on-device or edge model.
- You need deterministic, fully reproducible outputs for safety-critical code generation without human review.
- Your workload requires avoiding reliance on any third-party hosted AI provider for compliance reasons.
- You need a tiny, resource-constrained model that can run efficiently on microcontrollers.
FAQ
Frequently Asked Questions
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What is GPT-5 Codex?
GPT-5 Codex is an OpenAI code-focused large language model, optimized for program synthesis, refactoring, and natural-language-to-code workflows via LLM.API.
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What is GPT-5 Codex best at?
GPT-5 Codex excels at generating production-grade code, explaining complex codebases, automated refactoring, and creating end-to-end implementations from natural language specifications.
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How is GPT-5 Codex priced on LLM.API?
GPT-5 Codex pricing on LLM.API is usage-based per token, with exact input and output rates defined in your LLM.API pricing dashboard.
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What is the context window of GPT-5 Codex?
GPT-5 Codex supports a large context window suitable for multi-file repositories; check the LLM.API model card for the current maximum token limit.
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How fast is GPT-5 Codex in terms of latency?
GPT-5 Codex typically returns initial tokens within a few seconds, with total latency depending on prompt size, response length, and current LLM.API load.
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Which modalities does GPT-5 Codex support?
GPT-5 Codex supports text prompts and text outputs, and is optimized specifically for source code and natural-language instructions.
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How do I access GPT-5 Codex through LLM.API?
You call the LLM.API chat or completion endpoint with the GPT-5 Codex model identifier, using your LLM.API key for authentication.
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How does GPT-5 Codex compare to general-purpose GPT-5 models?
Compared to general-purpose GPT-5 variants, GPT-5 Codex is more capable and reliable on code tasks but less optimized for open-ended conversational content.
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What limitations does GPT-5 Codex have?
GPT-5 Codex can still produce incorrect or insecure code, may hallucinate APIs, and does not automatically validate, test, or run generated programs.
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Can GPT-5 Codex work with entire repositories or large codebases?
GPT-5 Codex can handle large code snippets and summaries of repositories within its context window, but full monorepos may require chunking and tooling integration.