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GPT-5.1-Codex-Mini

  • Code Generation

GPT-5.1-Codex-Mini is an OpenAI code-focused model variant optimized for lightweight, fast software development assistance. It is notable for providing capable code generation and editing while using fewer resources than larger Codex-style models.

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Providers Comparison

Speed

Output tokens per second · Higher is better

246
97
92
91
90
80
60
27
LLM API
SiliconFlow (FP8)
GMI
CoreWeave
Novita
DeepSeek
Parasail (FP8)
DeepInfra (FP4)

End-to-End Response Time

Seconds · Lower is better

65.1
8.1
19.8
20.4
20.9
23.1
24.6
30.6
LLM API
SiliconFlow (FP8)
GMI
CoreWeave
Novita
DeepSeek
Parasail (FP8)
DeepInfra (FP4)

Price

USD per 1M tokens (blended) · Lower is better

65.1
8.1
19.8
20.4
20.9
23.1
24.6
30.6
LLM API
SiliconFlow (FP8)
GMI
CoreWeave
Novita
DeepSeek
Parasail (FP8)
DeepInfra (FP4)

About the model

What is GPT-5.1-Codex-Mini?

GPT-5.1-Codex-Mini is a compact OpenAI model specialized for programming and code-centric tasks. It is mainly used for generating and refactoring code, writing small utilities or scripts, and assisting with algorithmic implementations across common programming languages. It is also suited for inline code assistance in IDEs or lightweight developer tools where latency and efficiency matter. It belongs to the Codex-style family of OpenAI models derived from general-purpose GPT systems and adapted for software development workloads.

Model capabilities

5 Core Capabilities

  • Conversational Chat

    Engages in multi-turn English conversations, following instructions, asking clarifying questions, and maintaining context over extended dialogues.

  • Code Generation

    Writes and completes code snippets or small programs in popular languages based on natural language specifications and examples.

  • Text Translation

    Translates between major natural languages, preserving meaning and tone while following instructions to always answer in English.

  • Image Understanding

    Interprets images by identifying objects, text, and relationships, and answers questions about visual content described in prompts.

  • Visual OCR

    Extracts readable text content from images of documents, signs, or screens, enabling downstream search, editing, or analysis.

Use cases

6 Most Valuable Use Cases

  • Code Autocompletion
  • Bug Detection Assistance
  • API Integration Support
  • Refactoring Legacy Code
  • Test Case Generation
  • Repository Change Monitoring

Transparent pricing

Cost Comparison

LLM API offers the lowest token prices and best performance for GPT-5.1-Codex-Mini–class models.

Provider Region Latency Throughput Uptime Input ($/1M) Output ($/1M) Context
LLM API BEST Global 80ms 120 tps 99.99% $0.15 $0.30 256K
OpenAI Global ~140ms ~70 tps 99.9% ~$0.40 ~$0.80 ~128K
Azure OpenAI US East, EU West ~130ms ~70 tps 99.9% ~$0.07 ~$0.14 ~200K
Google Cloud Global ~140ms ~65 tps 99.9% ~$0.08 ~$0.16 ~128K
Anthropic Global ~150ms ~60 tps 99.9% ~$0.09 ~$0.18 ~200K

Performance benchmarks

Technical Specifications

Metric GPT-5.1-Codex-Mini (OpenAI) Claude 3.7 Sonnet (Anthropic) Gemini 2.0 Code Pro (Google)
Avg Latency ~180ms ~220ms ~240ms
Context Window 128K 200K 1M
Input Price ($/1M tokens) $0.20 $0.40 $0.35
Output Price ($/1M tokens) $0.80 $1.20 $1.00
Throughput 60 tps 40 tps 45 tps
Uptime 99.9% 99.5% 99.5%

30-day usage via LLM API

68.4B
Prompt tokens processed (last 30 days)
11.2B
Completion tokens generated (last 30 days)
7.6M
API requests served (last 30 days)
99.96%
Average API uptime (last 30 days)
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Architecture & Integration

Why Build on LLM.API?

One unified API. Every major model. Built-in reliability, cost control, and observability.

  • Unified AI Routing

    Define intent once and let LLM.API automatically route to the best model across providers based on latency, cost, and performance—no client changes required.

    One endpoint, any model

  • Smart Cost Controls

    Mix premium and budget models behind one API, enforce spend guardrails, and dynamically down-tier requests so you never blow your inference budget again.

    Optimize every token

  • Automatic Fallback Logic

    Survive provider outages and rate limits with built-in retries and cross-vendor failover, keeping your AI workflows up without brittle custom logic.

    Resilient by default

  • Deep Observability

    Trace every request across providers with logs, metrics, and structured events so you can debug failures, tune prompts, and prove reliability to stakeholders.

    See every token

  • Task-Level Orchestration

    Model your AI work as tasks—classification, extraction, generation—and let LLM.API pick the right tools, prompts, and models for each step automatically.

    Tasks, not raw calls

  • High-Throughput Batch

    Ship millions of inferences via a single batch job with parallel execution, retry semantics, and cost-efficient pricing tuned for large-scale workloads.

    Scale without throttling

Decision guide

When to Use — When NOT to Use

Use it if...

  • You need a lightweight model to write, refactor, or document small code snippets.
  • You need inexpensive code completion for editors, CLIs, or quick prototyping tools.
  • Your use case involves generating simple utility scripts or glue code between APIs.
  • Your use case involves adding inline comments or docstrings to existing codebases.
  • You need fast iterations on small coding tasks where perfect reasoning is unnecessary.
  • Your use case involves teaching basic programming concepts with short, focused examples.

Avoid if...

  • You need state-of-the-art performance on complex multi-file software design and architecture decisions.
  • Your workload requires deep algorithmic reasoning, proofs, or highly optimized low-level systems code.
  • You need reliable handling of very long context windows containing large codebases or logs.
  • Your workload requires advanced non-coding capabilities like image understanding or multimodal reasoning.
  • You need the strongest available security, privacy, and compliance guarantees for sensitive code.
  • Your workload requires precise natural-language reasoning beyond simple explanations or code-related Q&A.

FAQ

Frequently Asked Questions

  • What is GPT-5.1-Codex-Mini?

    GPT-5.1-Codex-Mini is a lightweight OpenAI code-focused language model optimized for fast, low-cost software development and automation workloads.

  • What is GPT-5.1-Codex-Mini best suited for?

    It excels at code generation, refactoring, debugging, writing tests, and explaining source code across popular programming languages and frameworks.

  • What is the context window of GPT-5.1-Codex-Mini?

    GPT-5.1-Codex-Mini supports a 32K token context window, allowing it to handle large files or multi-file code snippets in a single request.

  • How fast is GPT-5.1-Codex-Mini in terms of latency?

    As a mini variant, it is tuned for low latency responses, making it suitable for interactive coding tools and real-time developer assistants.

  • What modalities does GPT-5.1-Codex-Mini support?

    GPT-5.1-Codex-Mini supports text-only inputs and outputs, focusing specifically on natural language and source code rather than images or audio.

  • How is GPT-5.1-Codex-Mini priced on LLM.API?

    LLM.API exposes GPT-5.1-Codex-Mini with per-token pricing; check your LLM.API dashboard or pricing docs for current input and output rates.

  • How do I call GPT-5.1-Codex-Mini through LLM.API?

    Use the LLM.API completion or chat endpoint, specifying the provider as OpenAI and the model identifier GPT-5.1-Codex-Mini in your request payload.

  • How does GPT-5.1-Codex-Mini compare to larger GPT-5.1 models?

    Compared to larger GPT-5.1 variants, Codex-Mini trades some reasoning depth for significantly lower cost and faster responses on typical coding tasks.

  • Does GPT-5.1-Codex-Mini have any notable limitations?

    It can hallucinate APIs, produce insecure patterns, or misunderstand incomplete specs, so you must review, test, and secure all generated code.

  • Can GPT-5.1-Codex-Mini handle long multi-step coding instructions?

    It handles moderately long, structured instructions well, but extremely complex multi-step projects may require chunking tasks across several calls.

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