What other models does OpenAI offer?

OpenAI offers a tiered lineup of models covering cost-efficient inference, general-purpose tasks, and frontier reasoning. Here are some other models OpenAI supports:

• GPT-5 Mini: GPT-5 Mini is OpenAI's lowest-cost reasoning model, with input priced at $0.25 per million tokens. It includes extended thinking capability and is intended for high-volume applications where intelligence ceiling matters less than price efficiency

• GPT-5.1: GPT-5.1 is the entry-level model in the GPT-5 reasoning series, scoring 48 on the Artificial Analysis Intelligence Index. It offers faster output throughput than GPT-5.2 at a lower price point and is well-suited for reasoning tasks that don't require the maximum available capability

• GPT-5.4: GPT-5.4 is OpenAI's current flagship reasoning model, with a 1.1 million token context window and an Intelligence Index score of 57, ranked #6 out of 150 models. OpenAI itself recommends GPT-5.4 over GPT-5.2 for new deployments

• GPT-4o: GPT-4o is a multimodal general-purpose model optimized for low latency and strong instruction following. It handles text, image, and audio inputs and is a practical choice when fast response time matters more than deep reasoning chains

• o3: o3 is OpenAI's specialized reasoning model, designed for scientific, mathematical, and advanced coding tasks. It is optimized for accuracy on difficult benchmarks through compute-intensive inference-time reasoning

How does GPT-5.2 differ from OpenAI's other models?

GPT-5.2 sits above GPT-5.1 and below GPT-5.4 in OpenAI's reasoning lineup, offering higher intelligence scores than GPT-5.1 with a larger context window.

• Intelligence Index ranking: GPT-5.2 scores 51 on the Artificial Analysis Intelligence Index, ranking #19 out of 150 models. This is 3 points above GPT-5.1 (48, #32) and 6 points below GPT-5.4 (57, #6), placing it in a clear middle tier for reasoning capability

• Context window: GPT-5.2 supports a 400k-token context window, double the 272k available in GPT-5.1 and a meaningful step up for long-document workflows. GPT-5.4 extends this further to 1.1 million tokens

• Pricing: Input is priced at $1.75 per million tokens and output at $14.00 per million tokens. This is 40% more expensive on input than GPT-5.1 and 30% cheaper on input than GPT-5.4, putting GPT-5.2 in a mid-range cost position within the GPT-5 series

• Speed: GPT-5.2 generates 77.5 tokens per second, slower than GPT-5.1 (124.4 t/s) but comparable to GPT-5.4 (79.1 t/s). Its time to first token of 100.68 seconds reflects more extensive reasoning chains than GPT-5.1's 30.37 seconds

• Knowledge cutoff: GPT-5.2's training data extends to August 31, 2025, compared to September 30, 2024 for GPT-5.1. This more recent cutoff is an advantage for applications that reference developments from late 2024 or 2025

GPT-5.2 is a strong fit when you need reasoning capability meaningfully above GPT-5.1 and a 400k context window, but where GPT-5.4's pricing or latency is prohibitive.

What models should I consider using alongside GPT-5.2?

No single model is optimal for every task. Here are models worth pairing with GPT-5.2 depending on what your product needs:

• GPT-5.4: For the hardest reasoning tasks in your pipeline, such as multi-step scientific analysis or complex code architecture, GPT-5.4's higher Intelligence Index score (57 vs. 51) and 1.1M context window make it worth the additional cost. Route your ceiling tasks there while using GPT-5.2 for the rest

• GPT-5 Mini: GPT-5 Mini at $0.25 per million input tokens is roughly 7x cheaper on input than GPT-5.2. Route simpler classification, extraction, or summarization requests to GPT-5 Mini to preserve budget for tasks that actually require GPT-5.2's reasoning depth

• Claude Sonnet 4.5: Claude Sonnet 4.5 is a strong complement for structured output generation, multi-turn conversations, and instruction-following tasks. It also provides cross-provider redundancy so an OpenAI outage does not take down your entire inference layer

• Gemini 2.0 Flash: Gemini 2.0 Flash is a high-throughput, low-latency model from Google. When GPT-5.2's 100-second time-to-first-token is incompatible with your latency requirements, routing those requests to Gemini 2.0 Flash delivers fast responses without abandoning model quality entirely

• Llama 3.3 70B: For self-hosted or on-premise deployments where data cannot leave your infrastructure, Llama 3.3 70B offers strong general reasoning capability that can handle many of the same tasks as GPT-5.2, without sending data to a third-party API.

What are the challenges of using GPT-5.2 in my product?

Like any production LLM, GPT-5.2 comes with tradeoffs worth planning for:

• Very high time to first token: GPT-5.2's time to first token is 100.68 seconds. This is among the highest in the evaluated model set and makes GPT-5.2 unsuitable for any synchronous, user-facing interaction. It is best used in async pipelines where a response delay of over a minute is acceptable

• Verbosity: GPT-5.2 generated 130 million output tokens during the Artificial Analysis Intelligence Index evaluation, making it one of the more verbose models measured. This drives up output token costs and can make post-processing more complex if your downstream systems expect concise outputs

• Cost at scale: At $14.00 per million output tokens, verbose responses at scale become expensive quickly. A product generating 5 million output tokens per month would incur $70 in output costs alone from GPT-5.2, before accounting for input tokens

• Provider dependency: OpenAI has already flagged GPT-5.4 as the recommended successor to GPT-5.2. Relying on a single model from a single provider creates upgrade pressure and service fragility if OpenAI deprecates GPT-5.2 or experiences an outage

• Rate limits under bursty load: High-capability reasoning models from OpenAI are subject to per-tier rate limits that can throttle throughput during traffic spikes. Without a routing layer that can redirect overflow to equivalent models, your application may surface errors to users during peak periods.

Why should I use Merge Gateway to route LLM requests with GPT-5.2 and every other model?

Using GPT-5.2 through Merge Gateway gives you access to the model itself and the infrastructure layer around it:

• One API, every provider: Access GPT-5.2 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-5.2 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-5.2?

Getting GPT-5.2 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-5.2, the model string is openai/gpt-5.2. 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-5.2 as primary with one fallback.

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