What other models does OpenAI offer?

OpenAI's model portfolio spans lightweight open-weight options, mid-tier reasoning models, and large-scale coding and research systems. Here are some other models OpenAI supports:

• GPT-4.1 Nano: A fast, non-reasoning model with a 1M-token context window priced at $0.10 per 1M input tokens, designed for high-throughput classification and extraction tasks that prioritize speed and cost over deep reasoning

• GPT-5 mini: A mid-tier reasoning model with extended thinking, a 400k-token context window, and an Intelligence Index score of 41, offering a lower-cost path to reasoning capability than GPT-5.3

• GPT-5.4 Mini: A reasoning model scoring 49 on the Intelligence Index at $0.75 per 1M input tokens, positioned between GPT-5 mini and GPT-5.3 in terms of both cost and capability

• GPT-5.4 Nano: A lower-cost reasoning model at $0.20 per 1M input tokens, scoring 44 on the Intelligence Index, suited for latency-sensitive applications that need reasoning without the cost of larger models

• GPT-OSS 20B: An Apache 2.0-licensed open-weight reasoning model with 21B total parameters, priced at $0.05 per 1M input tokens, covering use cases where self-hosting or open licensing is a hard requirement

• GPT-OSS 120B: The larger open-weight model at 117B total parameters, scoring 33 on the Intelligence Index and delivering 358.5 tokens per second, well suited for latency-sensitive open-weight deployments

How does GPT-5.3 differ from OpenAI's other models?

GPT-5.3 is OpenAI's code-focused reasoning model, sitting above mid-tier options like GPT-5 mini and GPT-5.4 Mini in both capability and cost.

• Intelligence Index (as of 6/2/2026): GPT-5.3 scores 54 out of 150 models, ranking 12th overall. GPT-5.4 Mini scores 49 and GPT-5 mini scores 41, placing GPT-5.3 meaningfully above both siblings on intelligence benchmarks

• Pricing: At $1.75 per 1M input tokens and $14.00 per 1M output tokens, GPT-5.3 is substantially more expensive than GPT-5.4 Mini ($0.75 input / $4.50 output) and GPT-5 mini ($0.25 input / $2.00 output). Teams should evaluate whether the benchmark lift justifies the cost difference for their specific workloads

• Speed: GPT-5.3 outputs 82.3 tokens per second with a time to first token of 82 seconds, reflecting the extended thinking time typical of reasoning models at this tier. GPT-5.4 Mini and GPT-5.4 Nano both deliver output faster, at 166 and 151 tokens per second respectively

• Context window: Like other GPT-5 series models, GPT-5.3 supports a 400k-token context window, the same as GPT-5 mini and GPT-5.4 Mini, so context length is not a differentiating factor within this tier

• Use case focus: GPT-5.3's architecture and positioning as "Codex" aligns it with complex code generation, multi-file reasoning, and agentic coding tasks where correctness matters more than cost

GPT-5.3 is best suited for coding-intensive applications, agent pipelines that require high accuracy on software engineering tasks, and research workflows where benchmark performance justifies the premium pricing.

What models should I consider using alongside GPT-5.3?

No single model is optimal for every task. Here are models worth pairing with GPT-5.3 depending on what your product needs:

• GPT-5.4 Nano: For routine subtasks in a pipeline that does not require GPT-5.3's coding depth, such as input classification or response formatting, GPT-5.4 Nano handles them at roughly one-ninth the input cost

• Claude Sonnet: For long-form writing, structured document generation, or tasks where tone and instruction-following precision are critical, Claude Sonnet provides strong results at a lower cost per output token than GPT-5.3

• Gemini 2.5 Pro: For multimodal tasks involving complex visual inputs or video alongside code, Gemini 2.5 Pro provides a complementary high-intelligence option with different benchmark strengths than GPT-5.3

• GPT-OSS 120B: For organizations that need to run coding workloads on their own infrastructure under an open license, GPT-OSS 120B offers Apache 2.0 licensing and 358.5 tokens per second throughput as a self-hosted fallback

• Mistral Large: For European deployments with strict data residency requirements, Mistral Large provides a capable reasoning alternative with EU-hosted inference that is not available through OpenAI by default

What are the challenges of using GPT-5.3 in my product?

Like any production LLM, GPT-5.3 comes with tradeoffs worth planning for:

• High output cost: At $14.00 per 1M output tokens, GPT-5.3 is among the most expensive models in OpenAI's lineup. A product generating even moderate output volumes will face significant per-request costs compared to using GPT-5.4 Mini ($4.50 per 1M output tokens) for less demanding tasks

• Long time to first token: With a median time to first token of 82 seconds, GPT-5.3 is unsuitable for real-time, synchronous user-facing applications. It fits best in async pipelines where latency tolerance is high

• High verbosity: GPT-5.3 generated 77M output tokens during evaluation, well above the average for comparable models. In production, this verbosity translates directly into higher output costs and longer response times if not managed with prompt-level constraints

• Provider dependency: Relying on OpenAI's proprietary infrastructure for a high-value coding pipeline creates fragility if OpenAI experiences an outage or deprecates the GPT-5.3 version. Without a configured fallback, any disruption halts the pipeline

• Cost at scale: The combination of $1.75 input and $14.00 output pricing means cost compounds rapidly at scale. Without hard budget limits and per-project attribution, spend can exceed projections quickly in agentic or iterative code generation workloads

Why should I use Merge Gateway to route LLM requests with GPT-5.3 and every other model?

Using GPT-5.3 through Merge Gateway gives you access to the model itself and the infrastructure layer around it:

• One API, every provider: Access GPT-5.3 and every other major LLM through a single endpoint and API key. Change providers by swapping the model string — no application code changes required

• Intelligent routing and automatic failover: Merge routes around OpenAI outages automatically. Routing policies based on cost, latency, or quality can reduce spend by 40–60% without touching your application code

• Cost governance: Set hard or soft project budgets so GPT-5.3 spend stays within plan. Every request is attributed to a model, project, and tag in a unified billing dashboard across all providers

• Build Your Own Router: Define what "best" means for your traffic by selecting from curated ML benchmarks or adding your own eval scores. The router scores each available model against your weights and picks the winner per request, with a plain-language explanation of every decision

• Security and compliance controls: Apply DLP rules and prompt injection protection before every request reaches OpenAI. Enforce per-project model and region policies without adding that logic to your application

How can I start using Merge Gateway to route requests with GPT-5.3?

Getting GPT-5.3 running through Merge Gateway takes a few minutes:

1. Create an account and get your API key from the dashboard.

2. Install the Merge Gateway SDK: run pip install merge-gateway-sdk (Python) or npm install merge-gateway-sdk (Node). Alternatively, if you're already using the OpenAI SDK, set base_url = "https://api-gateway.merge.dev/v1/openai" and your existing code works as-is.

3. Make your first request using the provider/model format. For GPT-5.3, the model string is openai/gpt-5.3. Swap the model string to route to any other provider without changing anything else.

4. Configure a routing policy in the dashboard to set failover behavior, cost limits, and optimization strategy. Your first policy can be as simple as naming GPT-5.3 as primary for coding tasks with GPT-5.4 Mini as a cost-optimized fallback.

Full setup instructions and SDK references are in the Merge Gateway docs.