What other models does OpenAI offer?

OpenAI's model lineup spans legacy text generation, multimodal reasoning, and open-weight options across a wide range of price and capability tiers. Here are some other models OpenAI supports:

• GPT-4o: OpenAI's general-purpose multimodal flagship, supporting text and image inputs with strong performance across reasoning, coding, and instruction-following tasks at a significantly higher intelligence level than GPT-3.5 Turbo

• GPT-4.1 Nano: A lightweight, cost-efficient non-reasoning model with a 1M-token context window, priced at $0.10 per 1M input tokens, and optimized for high-throughput workloads where speed matters more than top-tier accuracy

• GPT-5 mini: A mid-tier reasoning model with extended thinking, a 400k-token context window, and an Intelligence Index score of 41, suited for tasks that require deliberate multi-step reasoning without the cost of OpenAI's flagship models

• GPT-5.4 Nano: A fast reasoning model priced at $0.20 per 1M input tokens, delivering above-average intelligence for its cost tier with a 400k-token context window and output speeds around 151 tokens per second

• GPT-OSS 20B: An Apache 2.0-licensed open-weight reasoning model with 21B total parameters and a Mixture of Experts architecture, offering very low-cost inference at $0.05 per 1M input tokens with a 131k-token context window

• GPT-OSS 120B: The larger open-weight counterpart with 117B total parameters, scoring 33 on the Intelligence Index and delivering exceptional throughput at 358.5 tokens per second, also under the Apache 2.0 license

How does GPT-3.5 Turbo differ from OpenAI's other models?

GPT-3.5 Turbo is one of the oldest models in OpenAI's active lineup, sitting at the bottom of the intelligence tier with a knowledge cutoff of September 2021 and a very small context window.

• Context window: GPT-3.5 Turbo supports only 4,096 tokens of context, compared to 400k tokens for models like GPT-5.4 Nano or 1M tokens for GPT-4.1 Nano. This severely limits its usefulness for long-document tasks or multi-turn conversations

• Pricing vs. value: At $0.50 per 1M input tokens and $1.50 per 1M output tokens, GPT-3.5 Turbo is significantly more expensive than GPT-4.1 Nano ($0.10 input / $0.40 output) and GPT-OSS 20B ($0.05 input / $0.20 output), both of which score higher on intelligence benchmarks

• Intelligence Index: GPT-3.5 Turbo scores 9 out of 85 non-reasoning models, placing it in the bottom quartile. GPT-4.1 Nano scores 13 in the same category, and newer models in OpenAI's lineup score substantially higher

• Knowledge cutoff: The September 2021 training cutoff is over four years behind the current date, making GPT-3.5 Turbo unsuitable for any task requiring awareness of recent events, models, or APIs

GPT-3.5 Turbo is best suited only for legacy integrations where changing the model string would require significant re-testing. For new projects, a newer and cheaper OpenAI model is almost always the better choice.

What models should I consider using alongside GPT-3.5 Turbo?

No single model is optimal for every task. Here are models worth pairing with GPT-3.5 Turbo depending on what your product needs:

• GPT-4.1 Nano: For tasks currently handled by GPT-3.5 Turbo that need a longer context window or better instruction-following, GPT-4.1 Nano offers a 1M-token context window at a lower per-token cost with meaningfully higher accuracy

• GPT-OSS 20B: For high-volume classification or extraction tasks where cost is the primary constraint, GPT-OSS 20B at $0.05 input / $0.20 output delivers better benchmark scores than GPT-3.5 Turbo at a fraction of the price

• Gemini 2.0 Flash: For multimodal tasks or workloads that require processing images alongside text, Gemini 2.0 Flash provides strong performance at competitive pricing, filling a capability gap that GPT-3.5 Turbo cannot address

• Claude Haiku: For high-throughput customer-facing applications where response quality and speed both matter, Claude Haiku offers better reasoning and instruction-following than GPT-3.5 Turbo with comparable latency

• Mistral Small: For European deployments with data residency requirements, Mistral Small provides a cost-efficient alternative with regional hosting options that OpenAI does not offer

What are the challenges of using GPT-3.5 Turbo in my product?

Like any production LLM, GPT-3.5 Turbo comes with tradeoffs worth planning for:

• Severely limited context window: The 4,096-token limit means GPT-3.5 Turbo cannot process documents longer than roughly 6 pages in a single request. Applications that need to pass conversation history, retrieved chunks, or long system prompts will hit this ceiling quickly

• Outdated knowledge base: With a training cutoff of September 2021, GPT-3.5 Turbo has no awareness of events, technologies, or APIs released in the past four-plus years. Any product requiring current information must supplement requests with retrieval systems

• Poor value relative to current alternatives: At $0.50 per 1M input tokens, GPT-3.5 Turbo costs five times more than GPT-OSS 20B while delivering lower benchmark scores. Teams that have not re-evaluated pricing recently may be overpaying for inferior output

• Provider dependency: Relying exclusively on OpenAI for GPT-3.5 Turbo creates fragility when OpenAI deprecates the model or experiences an outage, with no automatic fallback unless one is explicitly configured

• Cost at scale: Output costs of $1.50 per 1M tokens compound quickly at high request volumes. Without active cost governance, a product running thousands of daily requests will spend more than it would on a newer, cheaper model with better output quality

Why should I use Merge Gateway to route LLM requests with GPT-3.5 Turbo and every other model?

Using GPT-3.5 Turbo through Merge Gateway gives you access to the model itself and the infrastructure layer around it:

• One API, every provider: Access GPT-3.5 Turbo 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-3.5 Turbo 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 routing requests to GPT-3.5 Turbo via Merge Gateway?

Getting GPT-3.5 Turbo 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-3.5 Turbo, the model string is openai/gpt-3.5-turbo. 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-3.5 Turbo as primary with one fallback.

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