What other models does OpenAI offer?

OpenAI's model lineup covers a wide range of price and capability tiers, from budget inference to advanced reasoning. Here are some other models OpenAI supports:

• GPT-4o mini: GPT-4o mini is OpenAI's most affordable model at $0.15 per 1M input tokens, designed for cost-sensitive, high-volume tasks like classification and extraction where advanced reasoning is not required

• GPT-4.1 mini: GPT-4.1 mini is a cost-efficient model with a 1M token context window and above-average intelligence among non-reasoning models, suited for long-context tasks at a low price point

• GPT-4.1: GPT-4.1 is a non-reasoning model with a 1M token context window, positioned as a strong general-purpose option for agentic and long-document workflows at mid-tier pricing

• o3: o3 is OpenAI's large-scale reasoning model, built for complex multi-step analytical tasks, and shares the same $2.00 per 1M input price as GPT-4.1 while offering deeper reasoning capability

• o4-mini: o4-mini is a compact reasoning model delivering 159.9 tokens per second, optimized for high-throughput reasoning workloads at a lower price than full reasoning models

• GPT-4o: GPT-4o is OpenAI's multimodal flagship from the 4o series, suited for vision tasks and general instruction following at a mid-tier price point without requiring the full GPT-5 cost tier

How does GPT-5 differ from OpenAI's other models?

GPT-5 is OpenAI's most capable model, positioned at the top of the lineup with extended thinking capabilities and the highest output cost.

• Intelligence: GPT-5 scores 45 on the Artificial Analysis Intelligence Index, ranking 40/150, making it OpenAI's highest-scoring model on this benchmark and placing it well above GPT-4.1 (26/71, as of 06/01/2026) and the o-series models

• Context window: GPT-5 supports a 400k token context window, which is smaller than GPT-4.1 and GPT-4.1 mini's 1M token window but larger than o3's 200k limit, suited for most long-document tasks

• Pricing: Input costs $1.25 per 1M tokens and output costs $10.00 per 1M tokens, making output roughly 1.25x more expensive than o3 and GPT-4.1 at the output tier

• Latency: Time to first token averages 76.88 seconds, reflecting extended reasoning computation. This is the highest latency of any OpenAI model and makes GPT-5 unsuitable for real-time or interactive applications

• Reasoning capability: GPT-5 includes extended thinking, placing it in the same architectural tier as o3 rather than as a pure non-reasoning model like GPT-4.1

GPT-5 is the right choice for tasks where accuracy and intelligence quality are the primary constraints, such as high-stakes document analysis, research synthesis, or complex agentic workflows where slower responses are acceptable.

What models should I consider using alongside GPT-5?

No single model is optimal for every task. Here are models worth pairing with GPT-5 depending on what your product needs:

• GPT-4.1 mini: Use GPT-4.1 mini for the high-volume, lower-complexity requests in the same pipeline, keeping GPT-5 reserved for tasks that genuinely require its intelligence tier and avoiding unnecessary cost on simpler queries

• o4-mini: Route to o4-mini for structured reasoning tasks that need faster responses, since o4-mini outputs at 159.9 tokens per second versus GPT-5's 90.0 tokens per second with significantly lower latency

• Claude Opus 4 (Anthropic): Claude Opus 4 competes at the top of Anthropic's lineup and provides a cross-provider alternative for flagship-tier tasks, reducing reliance on a single provider for your most critical requests

• Gemini 2.5 Pro (Google): For long-document tasks where GPT-5's 400k context is a ceiling, Gemini 2.5 Pro's extended context support provides an alternative for workloads that push beyond GPT-5's window

• o3 (OpenAI): For purely reasoning-heavy tasks without the need for GPT-5's full intelligence level, o3 can handle complex multi-step problems at the same input price point ($2.00 per 1M tokens, as of 06/01/2026) while reducing output costs

What are the challenges of using GPT-5 in my product?

Like any production LLM, GPT-5 comes with tradeoffs worth planning for:

• Provider dependency: Routing your most demanding workloads exclusively to OpenAI creates a fragile dependency. A GPT-5 availability incident or deprecation event directly impacts your highest-stakes pipelines without a fallback

• Cost at scale: At $10.00 per 1M output tokens, verbose generation tasks accumulate costs quickly. A pipeline generating 10M output tokens per month runs $100k in output costs alone without active budget controls

• High latency for interactive use: A 76.88 second time to first token makes GPT-5 incompatible with real-time chat, streaming interfaces, or any latency-sensitive user-facing feature. It is designed for batch and background workloads

• Smaller context window than peers: At 400k tokens, GPT-5's context window is smaller than GPT-4.1's 1M token limit. Applications that need to process very long documents in a single call may need to combine GPT-5 with a larger-context fallback

• Verbosity: GPT-5 generated 76M output tokens during the Intelligence Index evaluation, making it among the most verbose models evaluated. Verbose outputs drive up both cost and latency in production without additional output-length controls

Why should I use Merge Gateway to route LLM requests with GPT-5 and every other model?

Using GPT-5 through Merge Gateway gives you access to the model itself and the infrastructure layer around it:

• One API, every provider: Access GPT-5 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 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?

Getting GPT-5 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, the model string is openai/gpt-5. 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 as primary with one fallback.

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