Cost-effective text model from OpenAI with 46.2 MMLU-Pro score, accessed via OrcaRouter API.
openai/gpt-3.5-turbo-0125 is a text generation model developed by OpenAI and available through OrcaRouter's API. It is part of the GPT-3.5-Turbo family, optimized for conversational tasks and…
GPT-3.5-Turbo-0125 excels at a wide range of text-based tasks, including chat, summarization, translation, question answering, and content generation. It handles instructions well and can produce coherent, contextually appropriate responses for customer support, brainstorming, and data labeling. Its training data covers diverse domains up to April 2023, enabling reasonable performance on common knowledge and writing styles. For structured output, it can format JSON or follow custom schemas when prompted clearly.
If your application involves very high volume with simple, repetitive tasks such as straightforward classification or single-sentence generation, you might consider using smaller models like GPT-3.5-Turbo-Instruct or even open-source alternatives hosted on OrcaRouter. Cost savings can be significant when token counts are low and accuracy requirements are minimal. However, GPT-3.5-Turbo-0125 remains a cost-effective choice for most general-purpose uses, especially given its strong benchmark score of 46.2 on MMLU-Pro.
Yes, the model has been trained on multilingual text, though its strongest performance is on English. It can understand and generate text in many languages including Spanish, French, Chinese, Arabic, and others. Accuracy may degrade for languages with limited representation in training data or for highly idiomatic expressions. For tasks requiring precise multilingual fluency, consider supplementing with language-specific fine-tuning or using a native model. The input and output are always text, so non-English characters are supported.
Because the total context window is 16,385 tokens (widely known public specification), the model can process moderately long documents in a single pass. However, the output is limited to 4096 tokens per request. For longer outputs, you must implement a map-reduce or sliding window approach. The model's attention mechanism can maintain coherence across the entire context, but performance may degrade for tasks that require referencing the very beginning of a long prompt. Chunking and summarization strategies are recommended for very long texts.
MMLU-Pro is an enhanced version of the Massive Multitask Language Understanding benchmark, measuring a model's ability to answer questions across 57 subjects including science, law, and humanities. A score of 46.2 indicates that GPT-3.5-Turbo-0125 correctly answers about 46.2% of the questions, which is competitive among smaller models. This score reflects strong general knowledge but also shows room for improvement compared to larger models like GPT-4. For tasks requiring deep expertise, consider evaluating the model on domain-specific benchmarks.
Exact latency depends on request size, network conditions, and current load on OpenAI's infrastructure. In typical usage, GPT-3.5-Turbo-0125 responds within a few seconds for short prompts, often faster than larger models. OrcaRouter does not add significant overhead beyond the provider's response time. For real-time applications, test with your workload. The model's speed and cost make it a popular choice for interactive chatbots and production pipelines where per-request latency matters.
GPT-3.5-Turbo-0125 is widely used for its reliability, consistent formatting, and strong instruction-following capabilities. It rarely fails to produce a coherent response and handles typos or ambiguous phrasing gracefully. Its training data coverage up to April 2023 allows it to answer current events from that period accurately. The model also supports system messages to set behavior, making it easy to customize for different applications such as role-playing or constrained generation.
This model may struggle with complex reasoning, multi-step arithmetic, and very nuanced instructions. It can sometimes produce plausible-sounding but incorrect answers (hallucinations) and may not consistently enforce strict formatting rules. Its output length is capped at 4096 tokens, which may be insufficient for long-form content generation. Additionally, it lacks internet access by default and cannot retrieve real-time information or perform actions outside the chat interface. For advanced logic or up-to-date data, consider retrieval-augmented generation (RAG) or a more capable model.
OrcaRouter passes through OpenAI's exact pricing with zero markup: $0.50 per 1 million input tokens and $1.50 per 1 million output tokens. There is no additional platform fee, subscription cost, or per-request charge beyond these token rates. Input tokens include the prompt, system message, and conversation history; output tokens are the generated response. Billing is based on the number of tokens processed, measured by the tiktoken tokenizer for GPT-3.5.
While GPT-3.5-Turbo-0125 is already one of the most cost-effective models from OpenAI, you can further optimize by sending shorter prompts, trimming conversation history when possible, and using a smaller max_tokens value if your use case allows. Avoid excessively long system messages if they are not needed. For very high volume, consider whether a free or open-source model on OrcaRouter could meet your accuracy requirements. The trade-off is that cheaper models may require more rigorous prompt engineering or fine-tuning.
OrcaRouter does not currently offer a built-in caching mechanism for prompts or responses. Each API call is billed based on the tokens sent and received in that request. If you reuse the same prompt across multiple calls (e.g., identical system messages), you are charged for those tokens each time. To reduce costs, consider caching identical requests at the application level or batching multiple queries into a single prompt with multiple user messages.
Use the standard OpenAI Chat Completions endpoint with base URL https://api.orcarouter.ai/v1. Set the model parameter to 'openai/gpt-3.5-turbo-0125'. Include your OrcaRouter API key in the Authorization header. Example using cURL: curl https://api.orcarouter.ai/v1/chat/completions -H 'Authorization: Bearer YOUR_API_KEY' -H 'Content-Type: application/json' -d '{"model":"openai/gpt-3.5-turbo-0125","messages":[{"role":"user","content":"Hello"}]}'. The response format matches OpenAI's specification.
All standard OpenAI chat completions parameters are available, including: 'messages', 'max_tokens' (up to 4096), 'temperature', 'top_p', 'frequency_penalty', 'presence_penalty', 'stop', and 'stream'. 'n' (number of completions) is also supported. There is no 'logprobs' or 'logit_bias' support for this model on the OrcaRouter gateway. Note that the 'max_tokens' parameter is capped at 4096 to match the model's output limit. For streaming, set 'stream': true to receive incremental deltas.
If you currently use OpenAI directly, migrating to OrcaRouter is straightforward: change the base URL from https://api.openai.com/v1 to https://api.orcarouter.ai/v1, update the model ID to 'openai/gpt-3.5-turbo-0125' if you were using a generic alias, and replace your API key with the one from OrcaRouter. No code changes to the message format or parameters are needed. If you were using a different provider, ensure your client library supports the OpenAI-compatible schema. OrcaRouter provides a drop-in replacement.
GPT-4 generally outperforms GPT-3.5-Turbo-0125 in complex reasoning, factual accuracy, and following nuanced instructions, as reflected in higher benchmark scores across MMLU and other tests. However, GPT-4 is significantly more expensive (typically 10x the cost per token) and may have higher latency. GPT-3.5-Turbo-0125 offers a compelling price-performance sweet spot for tasks that don't require GPT-4's depth. Choose the larger model for advanced analysis or where margin of error is narrow.
Anthropic's Claude 3 Haiku is a competing low-cost model with fast inference and strong safety features. Both models are text-only and designed for high-volume applications. While specific benchmark comparisons vary, GPT-3.5-Turbo-0125 is widely adopted and benefits from extensive documentation and ecosystem. Claude 3 Haiku may have different strengths in creative writing and refusal behavior. The choice depends on developer preference and integration requirements. OrcaRouter supports both models, so you can compare directly.
Open-source models (e.g., Llama 3, Mistral) can be run on your own hardware or via OrcaRouter for potentially lower per-token costs, especially at scale. However, they often require more setup, prompt engineering, and may have weaker instruction following. GPT-3.5-Turbo-0125 offers turnkey reliability, consistent quality, and zero infrastructure management. For teams without ML expertise, the managed API is often preferable. Run benchmarks on your specific workload to decide.
OpenAI may release newer versions of GPT-3.5-Turbo or deprecate this specific snapshot. OrcaRouter will update the available models accordingly. If your application relies on consistent behavior, you may want to pin a specific model version by using the exact model ID. OrcaRouter provides advance notice of deprecations. For high-stakes production systems, consider testing against new versions in a staging environment before switching.
OpenAI-compatible — keep the SDK you already use
https://api.orcarouter.ai/v1import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.orcarouter.ai/v1",
api_key=os.environ["ORCAROUTER_API_KEY"],
)
response = client.chat.completions.create(
model="openai/gpt-3.5-turbo-0125",
messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)frequency_penaltylogit_biaslogprobsmax_tokenspresence_penaltyresponse_formatseedstopstreamtemperaturetool_choicetoolstop_logprobstop_pparallel_tool_calls| Input / 1M tokens | $0.500 |
|---|---|
| Output / 1M tokens | $1.50 |
| Currency | USD |
Estimate based on list price
Estimate only — actual token counts depend on the provider's tokenizer.
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GET /api/public/models/openai/gpt-3.5-turbo-0125Open @misc{orcarouter_gpt_3_5_turbo_0125,
title = {openai/gpt-3.5-turbo-0125 API},
author = {openai},
year = {n.d.},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/openai/gpt-3.5-turbo-0125}
}openai. (n.d.). openai/gpt-3.5-turbo-0125 API. OrcaRouter. https://www.orcarouter.ai/models/openai/gpt-3.5-turbo-0125