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Llama 3.1 8B Instruct vs gpt-oss-20b: API price, context & features · api14

Meta: Llama 3.1 8B Instruct vs OpenAI: gpt-oss-20b

A side-by-side comparison of Meta: Llama 3.1 8B Instruct ($0.18 / 1M tokens) and OpenAI: gpt-oss-20b ($0.11 / 1M tokens) using current USD prices and capabilities from the api14 catalog. Both run through the same OpenAI-compatible API.

Specifications

	Meta: Llama 3.1 8B Instruct	OpenAI: gpt-oss-20b
Provider	Meta	OpenAI
Input	$0.18 / 1M tokens	$0.11 / 1M tokens
Output	$0.28 / 1M tokens	$0.45 / 1M tokens
Cache read	$0.09 / 1M tokens	$0.11 / 1M tokens
Context window	131,072	131,072
Max output	117,964	117,964
Input	text	text
Output	text	text
Tool calling
Structured output
Reasoning control
Added	Jul 23, 2024	Aug 5, 2025

Key differences

OpenAI: gpt-oss-20b is 40% cheaper on input tokens.
Meta: Llama 3.1 8B Instruct is 38% cheaper on output tokens.
Only OpenAI: gpt-oss-20b supports reasoning effort control.
A request with 1,000 input and 500 output tokens costs $0.000315 with Meta: Llama 3.1 8B Instruct and $0.000333 with OpenAI: gpt-oss-20b.

In depth

Meta: Llama 3.1 8B Instruct is an open weights text model from Meta, released on 2024-07-23. OpenAI: gpt-oss-20b is an open weights text model from OpenAI, released on 2025-08-05. Both are instruction-tuned models that take text in and return text out, and both are served through api14 under the text category. The main structural difference between them is that gpt-oss-20b is a reasoning-capable model while Llama 3.1 8B Instruct is not.

The two models match on context length: each supports 131072 tokens, so neither gives you more room for long documents, transcripts or codebases. They also match on modalities, since both accept text only and produce text only, so there is no image or audio path in either case. Where they diverge is in the parameter surface. gpt-oss-20b adds reasoning, reasoning_effort and include_reasoning on top of the usual sampling and tool parameters, which means you can ask it to think through a problem before answering and control how much effort it spends. Llama 3.1 8B Instruct exposes a comparable set of sampling, tool and structured output parameters but has no reasoning controls at all. On price, gpt-oss-20b is the cheaper option for input tokens, while Llama 3.1 8B Instruct is the cheaper option for output tokens, so the better deal depends on whether your workload is input heavy or output heavy.

For straightforward generation work, pick Llama 3.1 8B Instruct. Summarising, rewriting, classification, extraction, chat responses and other tasks where you want a direct answer without a reasoning pass are a natural fit, and its lower output token price helps when responses are long. Choose gpt-oss-20b when the task benefits from explicit reasoning: multi step problems, careful analysis, or anything where you want to tune reasoning_effort and inspect the reasoning trace. Its cheaper input pricing also suits workloads that push large prompts through the model and return short answers.

Calling either model through api14 works the same way, because the API is OpenAI compatible. You point your existing client at the api14 base URL, send your request to the chat completions endpoint with your api14 key, and set the model field. To use Llama 3.1 8B Instruct, set the model id to meta-llama/llama-3.1-8b-instruct. To use gpt-oss-20b, set it to openai/gpt-oss-20b. Everything else in the request body, such as messages, temperature, max_tokens, tools and response_format, keeps the same shape, so switching between the two is a one line change. If you rely on reasoning parameters, remember those only apply to gpt-oss-20b.

There are still cases where the non reasoning model is the better pick even though gpt-oss-20b offers more controls. If your application needs fast, predictable single pass output and you do not want to pay for reasoning tokens, Llama 3.1 8B Instruct is the simpler choice, and its lower output price makes it attractive for high volume generation where answers are long. If your prompts are large but your answers are short, the input pricing advantage flips the decision toward gpt-oss-20b instead.

Switch with one string

Both models use the same endpoint and request shape. Change the model value from "meta-llama/llama-3.1-8b-instruct" to "openai/gpt-oss-20b" and prompts, streaming and tools keep working.

Meta: Llama 3.1 8B Instruct details & playground →OpenAI: gpt-oss-20b details & playground →

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://api14.com/api/v1",
  apiKey: process.env.API14_API_KEY,
});

const result = await client.chat.completions.create({
  "model": "openai/gpt-oss-20b",
  "messages": [
    {
      "role": "user",
      "content": "Explain vector databases in two sentences."
    }
  ]
});
console.log(result.choices[0].message.content);

Meta: Llama 3.1 8B Instruct vs OpenAI: gpt-oss-20b

Specifications

Key differences

In depth

Switch with one string

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