What other models does OpenAI offer?

OpenAI's model lineup spans multiple price and capability tiers, from ultra-low-cost inference to advanced reasoning. Here are some other models OpenAI supports:

• GPT-4o mini: GPT-4o mini is OpenAI's lowest-cost option at $0.15 per 1M input tokens, optimized for simple, high-volume tasks where budget is the primary concern and a 128k token context window is sufficient

• GPT-4o: GPT-4o is OpenAI's multimodal flagship from the 4o series, designed for strong instruction following and vision tasks at a mid-tier price, positioned between GPT-4.1 mini and the full GPT-4.1 model

• GPT-4.1: GPT-4.1 is the full-scale counterpart to GPT-4.1 mini, sharing the same 1M token context window but offering above-average intelligence at $2.00 per 1M input tokens for workloads that require higher quality

• o3: o3 is OpenAI's large-scale reasoning model, suited for complex multi-step problems where a non-reasoning model like GPT-4.1 mini would fall short on analytical depth

• o4-mini: o4-mini is a compact reasoning model with 159.9 tokens per second output speed, providing fast reasoning capability for high-volume tasks that go beyond GPT-4.1 mini's non-reasoning capabilities

• GPT-5: GPT-5 is OpenAI's most capable model with extended thinking, used for the most demanding tasks where top-tier quality is the priority over cost

How does GPT-4.1 mini differ from OpenAI's other models?

GPT-4.1 mini occupies the cost-efficient tier of OpenAI's lineup while offering a context window far larger than typical budget models.

• Context window: GPT-4.1 mini supports a 1M token context window, matching its full-scale sibling GPT-4.1 and far exceeding GPT-4o mini's 128k limit, enabling long-document and multi-document tasks at a low price

• Pricing: At $0.40 per 1M input tokens and $1.60 per 1M output tokens, GPT-4.1 mini costs 5x less on input than GPT-4.1 while sharing the same context window, making it the stronger choice for cost-sensitive long-context pipelines

• Intelligence: GPT-4.1 mini scores 23 on the Artificial Analysis Intelligence Index, ranking 26/85 and assessed as above average among non-reasoning models. It outperforms GPT-4o mini (ranked 13/85) on quality while staying well below the o-series reasoning models

• Speed: Output speed of 82.3 tokens per second is middle-of-the-pack for its tier, noticeably slower than o4-mini (159.9 tokens/sec) but faster than GPT-4o mini (60.2 tokens/sec)

• Modalities: Supports text and image input with text output, the same multimodal profile shared across the GPT-4.1 family

GPT-4.1 mini is well-suited for applications that need a large context window at a low price, such as document Q&A, long conversation agents, and code review workflows where reasoning depth is secondary to throughput and cost.

What models should I consider using alongside GPT-4.1 mini?

No single model is optimal for every task. Here are models worth pairing with GPT-4.1 mini depending on what your product needs:

• GPT-4.1: Escalate to GPT-4.1 for tasks that return low-confidence or low-quality results from GPT-4.1 mini, such as nuanced instruction following or complex multi-document synthesis, where the higher per-token cost is justified by accuracy requirements

• o4-mini: Route to o4-mini for requests requiring step-by-step reasoning, such as math, structured problem solving, or multi-hop logic, where GPT-4.1 mini's non-reasoning architecture produces unreliable outputs

• Claude Haiku 3.5 (Anthropic): Claude Haiku 3.5 competes in the same cost-efficient tier and provides a cross-provider failover option for when OpenAI availability degrades, without requiring a full upgrade to a more expensive model

• Gemini 2.0 Flash (Google): For high-frequency extraction and classification workloads, Gemini 2.0 Flash is a low-cost alternative that can run in parallel with GPT-4.1 mini to provide redundancy and cost benchmarking across providers

• Mistral Small (Mistral AI): Mistral Small is a compact, efficient model worth using as a cost floor alternative for simple tasks where even GPT-4.1 mini's pricing is higher than needed

What are the challenges of using GPT-4.1 mini in my product?

Like any production LLM, GPT-4.1 mini comes with tradeoffs worth planning for:

• Provider dependency: Routing production traffic exclusively through OpenAI means a service interruption or model version deprecation becomes your problem immediately, without a tested fallback in place

• Cost at scale: At $1.60 per 1M output tokens, output costs in verbose generation workloads still accumulate meaningfully at high volume without per-project budget enforcement

• Non-reasoning ceiling: GPT-4.1 mini's above-average Intelligence Index ranking reflects its strength among non-reasoning models, but it lacks the extended thinking capability of the o-series, meaning complex analytical and mathematical tasks need to be routed elsewhere

• Latency for streaming applications: At 82.3 tokens per second, GPT-4.1 mini is not the fastest model in its class. Real-time streaming interfaces that require fast initial output may see noticeable lag at scale

• Knowledge cutoff: The May 31, 2024 knowledge cutoff means GPT-4.1 mini will produce outdated answers for queries about recent events, requiring retrieval augmentation for time-sensitive use cases

Why should I use Merge Gateway to route LLM requests with GPT-4.1 mini and every other model?

Using GPT-4.1 mini through Merge Gateway gives you access to the model itself and the infrastructure layer around it:

• One API, every provider: Access GPT-4.1 mini 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-4.1 mini 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-4.1 mini?

Getting GPT-4.1 mini 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-4.1 mini, the model string is openai/gpt-4.1-mini. 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-4.1 mini as primary with one fallback.

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