Nanbeige4.2 3B vs GPT-5 nano: choosing a small model

Short answer: Nanbeige4.2 3B when hosting in Turkey matters

LLMTR is a gateway that gives access to language models from different providers through one OpenAI-compatible API. Nanbeige4.2 3B (llmtr/nanbeige4-2-3b) and GPT-5 nano (openai/gpt-5-nano) are called on the /v1/chat/completions endpoint at https://llmtr.com/v1 with the same API key from your llmtr.com account. For summaries and classification whose requests must be handled by a model hosted in Turkey, use the llmtr/nanbeige4-2-3b id on LLMTR. For a small, low-priced OpenAI model that reads images, use the openai/gpt-5-nano id; OpenAI has announced it will shut this model down on 11 December 2026, so before that date move the same request to the openai/gpt-6-luna id.

Nanbeige4.2 3B is an Apache 2.0 open-weight model from Nanbeige LLM Lab; according to its model card it has 3 billion parameters excluding embeddings. LLMTR hosts Nanbeige4.2 3B in Turkey, and requests are never forwarded to a third-party provider. According to OpenAI's model page, GPT-5 nano takes text and image input, produces reasoning tokens and supports tool calling and structured outputs; its knowledge cutoff is 31 May 2024.

• Nanbeige LLM Lab, Nanbeige4.2-3B model card · 2026-10-26
• OpenAI, GPT-5 nano model page · 2026-10-26

Nanbeige4.2 3B or GPT-5 nano?

Three questions decide it: does your work need Nanbeige4.2 3B's hosting in Turkey, does the input contain images, and will the flow keep running after 11 December 2026? Price favours GPT-5 nano, but GPT-5 nano's reasoning tokens are billed as output too. We make no claim about quality; compare the models on your own examples.

The table shows three rows with the values in the LLMTR catalog as of 26 October 2026; prices are per 1M tokens. The GPT-6 Luna column is the row to use once GPT-5 nano shuts down.

What does the same summary request cost on each model?

Say you turn a 3,000-token text into a 300-token summary. On Nanbeige4.2 3B with thinking off this request costs $0.0039. On GPT-5 nano the request costs $0.00027 before reasoning tokens; the model also produces reasoning tokens before the answer and they are billed at the output price: for example, 1,000 reasoning tokens add $0.0004 and the total becomes $0.00067. The LLMTR catalog has no cache price for GPT-5 nano, so a repeated prompt is billed at the input price too. On Nanbeige4.2 3B, tokens reported as cached_tokens are billed at the $0.25 rate.

The Python example below sends the same text to both models and prints finish_reason, the usage values, the reasoning token count and the cost. GPT-5 nano gets a higher max_tokens so that reasoning has room; no reasoning_effort is sent. For Nanbeige4.2 3B the client timeout is 300 seconds and the system message asks for a Turkish answer. Token counts differ from model to model; compare the quality of the summaries on your own texts.

OpenAI Python SDK example that sends the same text to Nanbeige4.2 3B and GPT-5 nano through https://llmtr.com/v1/chat/completions and prints finish_reason, token usage and cost.

import os

from openai import OpenAI

# Nanbeige4.2 3B answers slowly: keep the client timeout at 300 seconds or more.
client = OpenAI(base_url="https://llmtr.com/v1", api_key=os.environ["LLMTR_API_KEY"], timeout=300.0)

# Per 1M tokens on 26 October 2026: (input, output, max_tokens).
# GPT-5 nano spends reasoning tokens from max_tokens before the visible answer.
ROWS = {
    "llmtr/nanbeige4-2-3b": (1.00, 3.00, 512),
    "openai/gpt-5-nano": (0.05, 0.40, 4096),
}
with open("metin.txt", encoding="utf-8") as source:
    text = source.read()
messages = [
    {"role": "system", "content": "Her zaman Türkçe cevap ver."},
    {"role": "user", "content": "Bu metni beş maddede özetle:\n\n" + text},
]

for model, (inp, out, max_tokens) in ROWS.items():
    response = client.chat.completions.create(model=model, messages=messages, max_tokens=max_tokens)
    usage = response.usage
    details = usage.completion_tokens_details
    reasoning = (details.reasoning_tokens or 0) if details else 0
    cost = (usage.prompt_tokens * inp + usage.completion_tokens * out) / 1_000_000
    print(model, response.choices[0].finish_reason, usage.prompt_tokens, usage.completion_tokens, reasoning, f"${cost:.6f}")
    print(response.choices[0].message.content)

Reasoning, JSON and tool calling: what changes?

According to OpenAI's reasoning guide, reasoning tokens are not visible through the API but are billed as output tokens; the generation limit can be reached before any visible answer, and then you pay for the input and the reasoning without receiving a visible response. So do not keep max_tokens low on GPT-5 nano, and if finish_reason is length, send the request again with a higher limit. On LLMTR the GPT-5 nano row lists no reasoning level; a request that sends reasoning_effort returns 400.

On Nanbeige4.2 3B thinking is off by default, so a short answer comes back without spending thinking tokens; :think or "reasoning": true turns it on. For JSON you can use json_schema through response_format on GPT-5 nano; on Nanbeige4.2 3B response_format returns 400, so define the fields in a function's parameters schema and use tool calling. On Nanbeige4.2 3B, when you send tools, thinking is switched on for that request and tool_choice is auto only. Nanbeige4.2 3B generates answers slowly; use "stream": true wherever a user watches the answer.

• OpenAI, reasoning models guide · 2026-10-26

Which one when?

The choices below follow from the differences in price, input type, thinking behaviour, hosting and shutdown date; compare output quality on your own examples.

• Summaries and classification whose requests must be handled by a model hosted in Turkey: Nanbeige4.2 3B on LLMTR, llmtr/nanbeige4-2-3b; requests are never forwarded to a third-party provider.
• Short answers without paying for thinking tokens: Nanbeige4.2 3B; thinking is off by default and turns on with :think, "reasoning": true or tools.
• A small, low-priced model that reads images, until 11 December 2026: GPT-5 nano, openai/gpt-5-nano, at $0.05 input and $0.40 output per 1M tokens.
• JSON output with fixed fields: json_schema through response_format on GPT-5 nano or GPT-6 Luna; tool calling on Nanbeige4.2 3B.
• An OpenAI flow that has to keep running after 11 December 2026: GPT-6 Luna, openai/gpt-6-luna, at $0.10 input and $0.50 output per 1M tokens for input up to 272,000 tokens.

After 11 December 2026

OpenAI's deprecations page gives 11 December 2026 as the shutdown date for GPT-5 nano. Make the move before that date. OpenAI's page names GPT-5.6 Luna as the replacement; LLMTR names GPT-6 Luna as the successor of GPT-5 nano's dated version. To stay with the OpenAI family on LLMTR, set the model id to openai/gpt-6-luna; the request goes to the same /v1/chat/completions endpoint. Do not send temperature or top_p to GPT-6 Luna, as the row supports neither; the default level is medium, and for an answer without reasoning send reasoning_effort: none.

If you want to move to a model hosted in Turkey, set the model id to llmtr/nanbeige4-2-3b, remove response_format and move the schema into a function, drop image parts, check that the prompt fits in 32,768 tokens and set the client timeout to at least 300 seconds.

• OpenAI, deprecations · 2026-10-26

Comparing Nanbeige4.2 3B and GPT-5 nano on the same text

Send the same text to both models, then compare the summary, the finish_reason value, the reasoning tokens and the cost.

1. Key. Create an API key on llmtr.com and top up your balance under Dashboard > Billing.
2. Same text. Send your own texts with both model ids to the https://llmtr.com/v1/chat/completions endpoint; give GPT-5 nano a high max_tokens.
3. Choice. Compare the summaries, token counts, cost, your hosting needs and the 11 December 2026 date, then choose the model id.

Frequently asked questions

When does GPT-5 nano shut down?

On 11 December 2026, according to OpenAI's deprecations page. On LLMTR, move before that date to openai/gpt-6-luna to stay with the OpenAI family, or to llmtr/nanbeige4-2-3b for a model hosted in Turkey.

Can reasoning_effort be sent to GPT-5 nano?

Not on LLMTR; the GPT-5 nano row lists no reasoning level and the request returns 400. Because reasoning tokens are billed as output, do not keep max_tokens low. If you want to choose a level, set reasoning_effort from none to max on GPT-6 Luna.

What does a 300-token summary of a 3,000-token text cost?

$0.0039 on Nanbeige4.2 3B. On GPT-5 nano $0.00027 before reasoning tokens, and $0.00067 with 1,000 reasoning tokens.

Related posts

• Nanbeige4.2 3B vs Ministral 3 3B: two small models
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• Turkish text classification: Nanbeige4.2 3B or Leyla?

• Nanbeige4.2 3B model
• GPT-5 nano model
• GPT-6 Luna model
• Nanbeige4.2 3B documentation
• Streaming documentation