What other models does OpenAI offer?

OpenAI's model lineup covers a wide range of sizes, pricing tiers, and capability levels, from lightweight text models to large reasoning systems. Here are some other models OpenAI supports:

• GPT-3.5 Turbo: An older, budget-tier text model with a 4,096-token context window and a September 2021 knowledge cutoff, primarily retained for legacy integrations where migrating to newer models would require significant re-testing

• GPT-4o: OpenAI's general-purpose multimodal flagship, supporting text and image inputs and delivering strong performance across reasoning, coding, and long-context tasks with a substantially higher Intelligence Index score than GPT-4.1 Nano

• GPT-5 mini: A mid-tier reasoning model with extended thinking capability, a 400k-token context window, and an Intelligence Index score of 41, designed for tasks requiring deliberate multi-step reasoning at a cost below flagship models

• GPT-5.4 Nano: A faster and more capable reasoning model than GPT-4.1 Nano, scoring 44 on the Intelligence Index with a 400k-token context window, priced at $0.20 per 1M input tokens

• GPT-OSS 20B: An Apache 2.0-licensed open-weight reasoning model with a Mixture of Experts architecture, offering inference at $0.05 per 1M input tokens with a 131k-token context window and 225 tokens per second output speed

• GPT-OSS 120B: The larger open-weight model with 117B total parameters, scoring 33 on the Intelligence Index and delivering 358.5 tokens per second, suitable for latency-sensitive deployments that need strong reasoning on open infrastructure

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

GPT-4.1 Nano occupies the entry-level cost tier in OpenAI's current lineup, positioning it as a fast and affordable non-reasoning option rather than a high-accuracy choice.

• Context window: At 1M tokens, GPT-4.1 Nano offers the largest context window in OpenAI's non-reasoning lineup. This makes it well suited for long-document retrieval, extended conversation history, and large-batch summarization tasks that would overflow smaller-context models

• Pricing: Input costs $0.10 per 1M tokens, with a 73% cache discount to $0.028 for cache hits. Output is $0.40 per 1M tokens. This is cheaper than GPT-4o and GPT-5.4 Nano ($0.20 input) while remaining in the same order of magnitude
• Intelligence Index (as of 6/2/2026): GPT-4.1 Nano scores 13 out of 85 non-reasoning models, placing it below average. By comparison, newer reasoning models in OpenAI's lineup score 40 or above, so GPT-4.1 Nano is not the right choice for complex reasoning or high-accuracy generation tasks

• Speed: At 162.1 tokens per second with a 0.67-second time to first token, GPT-4.1 Nano is among the fastest models in OpenAI's portfolio, ranking 8th in speed out of 85 non-reasoning models

• Capabilities: GPT-4.1 Nano accepts text and image inputs but outputs text only. It is not a reasoning model and does not support extended thinking

GPT-4.1 Nano is the right fit for high-volume, latency-sensitive tasks that benefit from a very large context window but do not require deep reasoning, such as document triage, structured extraction, or classification at scale.

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

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

• GPT-5.4 Nano: When a request requires reasoning or multi-step problem solving that GPT-4.1 Nano cannot handle reliably, routing to GPT-5.4 Nano gives you a reasoning model at a still-modest $0.20 per 1M input tokens with a 400k-token context window

• Claude Haiku: For instruction-following tasks where output formatting consistency is critical, Claude Haiku provides strong structured output reliability at comparable cost and latency to GPT-4.1 Nano

• GPT-OSS 20B: For on-premise or open-weight deployments where self-hosting is a requirement, GPT-OSS 20B covers a similar cost tier under the Apache 2.0 license with a 131k-token context window

• Gemini 2.0 Flash: For multimodal pipelines that need to process multiple images or video frames alongside text, Gemini 2.0 Flash handles higher-complexity visual inputs than GPT-4.1 Nano can support effectively

• Mistral Small: For European workloads with data residency requirements, Mistral Small provides a cost-efficient alternative with EU-hosted inference that OpenAI does not offer as a default option

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

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

• Below-average intelligence for complex tasks: Scoring 13 out of 85 non-reasoning models on the Intelligence Index, GPT-4.1 Nano is designed for throughput, not accuracy. Tasks that require nuanced reasoning, multi-step logic, or high factual precision will produce inconsistent results

• No reasoning capability: GPT-4.1 Nano does not support extended thinking or chain-of-thought reasoning. Pipelines that route all requests to this model without a fallback for harder queries will see quality degradation on complex inputs

• Knowledge cutoff: The May 2024 training cutoff means GPT-4.1 Nano lacks awareness of events, models, or APIs released in the past year or more. Retrieval augmentation is necessary for any time-sensitive application

• Provider dependency: Running exclusively on OpenAI's infrastructure means an outage or deprecation event directly impacts availability. Without a failover policy, any service disruption translates into downtime for your product

• Cost at scale: At $0.40 per 1M output tokens, costs accumulate quickly in high-volume generation scenarios. Without active budget governance, a product generating millions of tokens daily will see bills that were not anticipated during early-stage testing

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

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

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

Getting GPT-4.1 Nano 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 Nano, the model string is openai/gpt-4.1-nano. 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 Nano as primary with a reasoning model as fallback for complex queries.

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