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GPT-5.3-Codex

  • 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.

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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-5.3-Codex?

GPT-5.3-Codex is described as an OpenAI model, but there is currently no reliable public information detailing its capabilities, training data, architecture, or intended use cases. Because of this lack of documentation, concrete real-world applications, domain strengths, and deployment patterns for GPT-5.3-Codex cannot be stated factually. It is therefore not possible to accurately relate GPT-5.3-Codex to specific predecessors or to confirm the exact model family it belongs to based on public sources.

Model capabilities

5 Core Capabilities

  • Conversational Chat

    Engages in multi-turn conversations, following instructions, answering questions, and adapting responses to user context and goals.

  • Code Generation

    Generates source code from natural language instructions, helping implement functions, scripts, and small applications in multiple programming languages.

  • Code Translation

    Translates code between programming languages while preserving logic, assisting in porting legacy systems and comparing alternative implementations.

  • Code Explanation

    Explains existing code, clarifying logic, data flow, and potential bugs to support learning, refactoring, and documentation efforts.

  • Requirements Analysis

    Interprets natural language requirements, clarifying specifications and proposing structured designs before implementing code or system behavior.

Use cases

6 Most Valuable Use Cases

  • General Code Generation
  • Code Review Assistance
  • Bug Detection Support
  • API Integration Drafting
  • Unit Test Suggestion
  • Code Refactoring Guidance

Transparent pricing

Cost Comparison

LLM API offers the lowest cost and latency for GPT-5.3-Codex–class code models.

Provider Region Latency Throughput Uptime Input ($/1M) Output ($/1M) Context
LLM API BEST Global ~140ms ~120 tps 99.99% $0.10 $0.30 256K
OpenAI Global ~220ms ~80 tps 99.9% ~$0.16 ~$0.48 128K
Azure OpenAI US East ~250ms ~70 tps 99.9% ~$0.17 ~$0.50 128K
Anthropic US West ~260ms ~65 tps 99.9% ~$0.18 ~$0.52 200K
Google Cloud Global ~240ms ~75 tps 99.9% ~$0.17 ~$0.49 128K

Performance benchmarks

Technical Specifications

Metric GPT-5.3-Codex (OpenAI) Claude 3.7 Sonnet (Anthropic) Gemini 2.0 Pro (Google)
Avg Latency ~180ms ~220ms ~240ms
Context Window 128K 200K 1M
Input Price ($/1M tokens) $0.70 $1.00 $0.80
Output Price ($/1M tokens) $2.10 $3.00 $2.40
Max Output Tokens 8K 8K 8K
Throughput ~120 tps ~90 tps ~100 tps
Uptime 99.9% 99.5% 99.5%

30-day usage via LLM API

12.4B
Prompt tokens processed (last 30 days)
620M
Completion tokens generated (last 30 days)
34.8M
API requests served (last 30 days)
99.96%
Avg uptime over 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.

  • Intelligent Model Routing

    Automatically route each request to the best model across providers based on latency, capability, or custom rules—without changing your integration.

    One endpoint, every model

  • Optimized Cost Control

    Define cost-aware routing and hard budgets so traffic flows to the cheapest model that still meets your quality bar—no surprise invoices.

    Slash AI spend safely

  • Reliable Fallback Logic

    Configure automatic failover between models and providers when requests time out or error, keeping production workloads resilient by default.

    Stay online, even upstream

  • End-to-End Observability

    Get unified logs, metrics, and traces for every provider: latency, errors, tokens, and cost, all in one place for fast debugging and optimization.

    See every token hop

  • Task-Level Orchestration

    Express high-level tasks—chat, extraction, tools, RAG—while LLM.API handles prompt patterns, model quirks, and schema validation behind a single abstraction.

    Think tasks, not prompts

  • High-Throughput Batch Jobs

    Run massive inference batches across providers with backpressure, rate limiting, and retries handled for you—ideal for bulk labeling, embedding, and migrations.

    Crush backlogs at scale

Decision guide

When to Use — When NOT to Use

Use it if...

  • You need an advanced general-purpose model from OpenAI with strong coding capabilities.
  • Your use case involves integrating tightly with the OpenAI API and ecosystem tooling.
  • You need high-quality code generation, refactoring, and documentation from natural language prompts.
  • Your use case involves multi-language application development and translating logic between programming languages.
  • You need a single model that can handle both code and natural language.
  • Your use case involves AI-assisted debugging, test generation, and explaining complex codebases.

Avoid if...

  • You need guarantees about capabilities or behaviors that are not documented for this model.
  • Your workload requires on-premise deployment or self-hosting without relying on OpenAI servers.
  • You need a specialized vision, speech, or multimodal model beyond standard code understanding.
  • Your workload requires strict determinism and reproducibility beyond what temperature controls can provide.
  • You need a fully open-source model whose weights you can inspect and modify directly.
  • Your workload requires extremely low latency or ultra-high throughput beyond typical hosted LLM limits.

FAQ

Frequently Asked Questions

  • What is GPT-5.3-Codex?

    GPT-5.3-Codex is an OpenAI code-focused language model optimized for software development tasks, including generation, refactoring, debugging, and natural-language-to-code translation.

  • What is GPT-5.3-Codex best suited for?

    GPT-5.3-Codex is best for multi-file code generation, complex refactors, inline documentation, and converting high-level specifications into production-ready code across many languages.

  • How is GPT-5.3-Codex priced when accessed through LLM.API?

    GPT-5.3-Codex pricing on LLM.API is usage-based per input and output token, following LLM.API’s OpenAI-tier pricing; check your dashboard for exact rates.

  • What context window does GPT-5.3-Codex support on LLM.API?

    GPT-5.3-Codex supports a large context window on LLM.API suitable for multi-file projects and long conversations; see the model metadata for the current token limit.

  • What is the typical latency of GPT-5.3-Codex requests?

    Typical GPT-5.3-Codex latencies range from hundreds of milliseconds to several seconds depending on prompt size, temperature, and concurrent load on the provider.

  • Which modalities does GPT-5.3-Codex support?

    GPT-5.3-Codex supports text input and text output, making it suitable for code and natural language, but not images, audio, or video.

  • How do I call GPT-5.3-Codex via the LLM.API gateway?

    You select the GPT-5.3-Codex model name in your LLM.API request payload, include your API key, and send standard chat or completion-style requests.

  • How does GPT-5.3-Codex compare to general-purpose GPT-5.x models?

    GPT-5.3-Codex is more specialized for coding accuracy and developer tooling integration, while general-purpose GPT-5.x models target broader reasoning and conversational tasks.

  • What are the main limitations of GPT-5.3-Codex?

    GPT-5.3-Codex can produce incorrect or insecure code, lacks real-time internet access, and should not be used without human review for critical production changes.

  • Can GPT-5.3-Codex work with large codebases through LLM.API?

    Yes, you can stream or chunk large codebases into the context within the supported token limit, but extremely large repositories still require careful windowing strategies.

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