What other models does OpenAI offer?

OpenAI maintains a broad lineup of models spanning cost-efficient, standard, and frontier reasoning tiers. Here are some other models OpenAI supports:

• GPT-5 Mini: GPT-5 Mini is OpenAI's cost-efficient reasoning model, priced at $0.25 per million input tokens. It supports extended thinking and is designed for high-volume workloads where price-per-token matters more than maximum intelligence ceiling

• GPT-5.2: GPT-5.2 is a reasoning model positioned above GPT-5.1 in the OpenAI lineup, with a 400k-token context window and higher benchmark performance on the Artificial Analysis Intelligence Index. It targets use cases that require stronger multi-step reasoning than GPT-5.1 can provide

• GPT-5.4: GPT-5.4 is OpenAI's current flagship reasoning model, featuring a 1.1 million token context window and the highest Intelligence Index score in the GPT-5 series. It is OpenAI's recommended choice for tasks that push the limits of reasoning and long-context understanding

• GPT-4o: GPT-4o is OpenAI's general-purpose multimodal model optimized for speed and instruction following. It handles text, image, and audio inputs and is well-suited for products where low latency matters more than extended chain-of-thought reasoning

• o3: o3 is OpenAI's dedicated reasoning model built for scientific, mathematical, and code-intensive tasks. It achieves high benchmark performance on evaluations like GPQA Diamond by spending more compute at inference time on deliberate reasoning chains

How does GPT-5.1 differ from OpenAI's other models?

GPT-5.1 sits in the middle of OpenAI's GPT-5 reasoning series, offering strong intelligence at a more moderate price point than GPT-5.2 or GPT-5.4.

• Intelligence Index ranking: GPT-5.1 scores 48 on the Artificial Analysis Intelligence Index, ranking #32 out of 150 evaluated models. This places it well above average but below GPT-5.2 (51, #19) and GPT-5.4 (57, #6)

• Pricing: Input is priced at $1.25 per million tokens with output at $10.00 per million tokens. That is meaningfully cheaper than GPT-5.2 ($1.75 input / $14.00 output) and GPT-5.4 ($2.50 input / $15.00 output), making it the most accessible entry point in the GPT-5 reasoning tier

• Speed: GPT-5.1 generates 124.4 tokens per second, which is faster than both GPT-5.2 (77.5 t/s) and GPT-5.4 (79.1 t/s). However, its time to first token is 30.37 seconds, reflecting the extended thinking overhead common to reasoning models

• Context window: GPT-5.1 supports 272k tokens, compared to 400k for GPT-5.2 and 1.1 million for GPT-5.4. For most production use cases this is sufficient, but long-document workflows may require a higher-tier model

• Capabilities: GPT-5.1 accepts text and image inputs and produces text output. It includes extended thinking/reasoning capability, which differentiates it from non-reasoning models like GPT-4o

GPT-5.1 is the right choice when you need genuine reasoning capability without the cost premium of GPT-5.2 or GPT-5.4, and where a 272k context window is sufficient for your workload.

What models should I consider using alongside GPT-5.1?

No single model is optimal for every task. Here are models worth pairing with GPT-5.1 depending on what your product needs:

• GPT-5.4:  When your application encounters tasks that require the deepest available reasoning (multi-step scientific problems, complex code generation, long legal document analysis), route those requests to GPT-5.4. Its 1.1M context window and higher Intelligence Index score (57 vs. 48) make it the better choice for ceiling-limited tasks

• GPT-5 Mini: For high-volume, lower-complexity requests such as classification, summarization, or form parsing, GPT-5 Mini's $0.25 per million input token pricing is roughly 5x cheaper than GPT-5.1. Routing simpler requests there preserves budget for harder tasks where GPT-5.1's reasoning is actually needed

• Claude Sonnet 4.5: Anthropic's Claude Sonnet models consistently perform well on instruction-following and structured output tasks. Pairing GPT-5.1 with Claude Sonnet 4.5 gives you redundancy across providers and a strong fallback if OpenAI experiences an outage

• Gemini 2.0 Flash: Google's Gemini 2.0 Flash offers very high output speed at low cost, making it a practical routing target for latency-sensitive or bursty traffic where GPT-5.1's time-to-first-token (30+ seconds) would be unacceptable to end users

• Mistral Large: For European-region deployments with data residency requirements, Mistral Large provides strong general reasoning capability from a provider with EU infrastructure, serving as a geographically appropriate complement to GPT-5.1

What are the challenges of using GPT-5.1 in my product?

Like any production LLM, GPT-5.1 comes with tradeoffs worth planning for:

• High time to first token: GPT-5.1's time to first token is 30.37 seconds, driven by its extended thinking process. This makes it a poor fit for real-time, conversational, or user-facing applications where response delay is visible. Streaming can help, but the thinking latency is inherent to the model

• Verbosity: GPT-5.1 generated 69 million output tokens during Artificial Analysis's Intelligence Index evaluation, ranking #84 of 150 models for verbosity. In practice, this means longer responses and higher output token costs than a similarly capable non-reasoning model

• Cost at scale: At $10.00 per million output tokens, output costs compound quickly at production request volumes. A product generating 10 million output tokens per month would spend $100 on GPT-5.1 output alone, before factoring in input or caching costs

• Provider dependency: Relying exclusively on OpenAI creates fragility if GPT-5.1 is deprecated (OpenAI has already flagged GPT-5.2 as a successor) or if OpenAI experiences an outage. Without a fallback, any service disruption directly impacts your users

• Outdated knowledge cutoff: GPT-5.1's training data cuts off at September 30, 2024. Applications requiring current events, recent documentation, or up-to-date pricing data will need retrieval augmentation or should route those queries to a model with a more recent cutoff

Why should I use Merge Gateway to route LLM requests with GPT-5.1 and every other model?

Using GPT-5.1 through Merge Gateway gives you access to the model itself and the infrastructure layer around it:

• One API, every provider: Access GPT-5.1 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-5.1 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-5.1?

Getting GPT-5.1 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-5.1, the model string is openai/gpt-5.1. 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-5.1 as primary with one fallback.

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