- Code Generation
GPT-5.3-Codex is an OpenAI code-focused generative model; no public, authoritative documentation about this specific version is available at this time.
Powered by OpenAI
GPT-5.1-Codex
- Code Generation
GPT-5.1-Codex is an OpenAI code-focused GPT-5.1 series model, optimized for understanding, generating, and editing software code. It emphasizes high-quality code synthesis and integration guidance across many programming languages and frameworks.
Providers Comparison
Speed
Output tokens per second · Higher is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
End-to-End Response Time
Seconds · Lower is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
Price
USD per 1M tokens (blended) · Lower is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
About the model
What is GPT-5.1-Codex?
GPT-5.1-Codex is a coding-oriented large language model from OpenAI designed to reason about and generate source code. It is mainly used for tasks such as writing new code from natural language instructions, refactoring and documenting existing code, and assisting with debugging by explaining errors and suggesting fixes. It is also applied in tooling scenarios like AI-powered IDE assistants, code review aids, and codebase navigation helpers. It follows earlier OpenAI Codex-style and GPT-based coding models in the same family of code-specialized GPT systems.
Model capabilities
5 Core Capabilities
-
Conversational Chat
Engages in multi-turn, context-aware dialogue, following instructions and adapting tone while answering questions and assisting with tasks.
-
Code Generation
Generates source code snippets and complete functions from natural language instructions across multiple programming languages and frameworks.
-
Text Translation
Translates text between multiple major languages while preserving meaning, intent, and appropriate formality or tone where possible.
-
Image Understanding
Interprets images by identifying objects, reading simple layouts, and using visual context to support text-based reasoning.
-
Optical Character Recognition
Extracts readable text from images or screenshots that contain printed content, enabling downstream analysis or transformation tasks.
Use cases
6 Most Valuable Use Cases
- Code Generation Assistance
- Software Bug Diagnosis
- Automated Code Refactoring
- API Integration Support
- Test Case Creation
- Config File Editing
Transparent pricing
Cost Comparison
LLM API delivers the lowest cost and latency for GPT-5.1-Codex–class code models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 80ms | 220 tps | 99.99% | $0.12 | $0.24 | 256K tokens |
| OpenAI | Global | ~160ms | ~120 tps | 99.9% | ~$0.25 | ~$0.50 | 128K tokens |
| Azure OpenAI | US East | ~190ms | ~100 tps | 99.9% | ~$0.27 | ~$0.54 | 128K tokens |
| Anthropic (Claude Codex-equivalent) | US West | ~200ms | ~90 tps | 99.9% | ~$0.30 | ~$0.60 | 200K tokens |
| Google (CodeGemini-equivalent) | Global | ~210ms | ~80 tps | 99.9% | ~$0.28 | ~$0.56 | 128K tokens |
Performance benchmarks
Technical Specifications
| Metric | GPT-5.1-Codex (OpenAI) | GPT-4.1 (OpenAI) | Claude 3.5 Sonnet (Anthropic) |
|---|---|---|---|
| Avg Latency | ~180ms | ~250ms | ~300ms |
| Context Window | 256K | 128K | 200K |
| Input Price ($/1M tokens) | $2.00 | $5.00 | $3.00 |
| Output Price ($/1M tokens) | $6.00 | $15.00 | $15.00 |
| Max Output Tokens | 8K | 4K | 4K |
| Throughput | 120 tps | 60 tps | 50 tps |
| Uptime | 99.95% | 99.9% | 99.9% |
30-day usage via LLM API
- 62.5B
- Prompt tokens processed (last 30 days)
- 7.8B
- Completion tokens generated (last 30 days)
- 14.3M
- API requests served (last 30 days)
- 99.95%
- 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.
-
Unified AI Routing
Define rules once and let LLM.API route requests across providers and models automatically, optimizing for latency, reliability, and capabilities without changing your application code.
Smart, policy-based routing -
Cost-Aware Orchestration
Balance quality and price with configurable cost ceilings, tiered model fallbacks, and usage controls so you never blow your AI budget in production.
Control spend by design -
Resilient Fallback Flows
Stay online even when providers fail with automatic cross-vendor retries, graceful downgrades, and configurable error-handling that preserves your SLAs.
Fail soft, not hard -
End-to-End Observability
Get full visibility into every call with traces, latency breakdowns, provider error analytics, and cost insights, ready to plug into your existing monitoring stack.
Debug AI like code -
Task-Native Workflows
Describe work at the task level—summarize, classify, extract—and let LLM.API pick the right models, prompts, and tools so teams ship faster with fewer experts.
Tasks, not prompts -
High-Throughput Batch Jobs
Process millions of records via optimized batching, concurrency controls, and automatic retries, turning large offline workloads into predictable, cost-efficient pipelines.
Scale jobs, not ops
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a general-purpose assistant for coding tasks, debugging, and code refactoring.
- Your use case involves generating boilerplate code for web backends, APIs, and services.
- You need help translating natural language requirements into well-structured, type-safe code implementations.
- Your use case involves interactive pair-programming assistance across multiple programming languages and frameworks.
- You need concise code explanations and documentation generation from existing codebases or snippets.
- Your use case involves prototyping small applications quickly without strict runtime performance constraints.
Avoid if...
- You need guaranteed bug-free, security-audited code suitable for safety-critical or regulated systems.
- Your workload requires strict data residency guarantees beyond OpenAI’s documented compliance and controls.
- You need offline, on-premise model deployment with no external API dependencies.
- Your workload requires formal verification, proofs of correctness, or mathematically guaranteed code properties.
- You need deterministic, bit-by-bit reproducible outputs across runs for compliance-sensitive workflows.
- Your workload requires domain-specific models trained on proprietary internal code not shared externally.
FAQ
Frequently Asked Questions
-
What is GPT-5.1-Codex?
GPT-5.1-Codex is an OpenAI large language model optimized for advanced code generation, code understanding, and general-purpose software engineering assistance.
-
What is GPT-5.1-Codex best at?
GPT-5.1-Codex excels at generating complete codebases, refactoring legacy code, producing tests, and explaining complex programming concepts across many languages and frameworks.
-
How is GPT-5.1-Codex priced when used through LLM.API?
LLM.API exposes GPT-5.1-Codex with per-token usage-based pricing; check your LLM.API dashboard or pricing docs for the latest input and output rates.
-
What context window does GPT-5.1-Codex support on LLM.API?
GPT-5.1-Codex on LLM.API supports a large-context interface; refer to the LLM.API model reference for the exact maximum token window currently available.
-
How fast is GPT-5.1-Codex in terms of latency?
Typical end-to-end latencies range from a few hundred milliseconds to several seconds depending on prompt size, requested output length, and concurrency.
-
Which modalities does GPT-5.1-Codex support?
GPT-5.1-Codex primarily supports text input and output, including code, with optional structured tool calling via the LLM.API interface.
-
How do I call GPT-5.1-Codex via the LLM.API?
Use the LLM.API chat or completion endpoint with the model parameter set to "GPT-5.1-Codex" and authenticate using your LLM.API API key.
-
How does GPT-5.1-Codex compare to other OpenAI coding models?
GPT-5.1-Codex targets higher-quality, more robust code generation and reasoning than earlier OpenAI code models, while remaining compatible with standard OpenAI-style APIs.
-
What are the main limitations of GPT-5.1-Codex?
GPT-5.1-Codex can still hallucinate APIs or logic, may miss security edge cases, and should not be treated as a substitute for human code review.
-
Can GPT-5.1-Codex use tools or call external APIs through LLM.API?
Yes, you can configure tool schemas in LLM.API so GPT-5.1-Codex can issue structured tool calls to trigger external services.
Compare
Competitive Models
- Code Generation
GPT-5.2-Codex is an OpenAI model name, but there is no public, reliable technical information available about this specific variant. It is not documented in OpenAI’s official…
- Code Generation
Qwen3 Coder Next is an open-weight, coding-specialized language model from Qwen that uses an efficient Mixture-of-Experts architecture to deliver strong agentic coding performance while remaining practical…