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Gemini 3.5 Flash Lite vs Morph V3 Large: API price, context & features · api14

Google: Gemini 3.5 Flash Lite vs Morph: Morph V3 Large

A side-by-side comparison of Google: Gemini 3.5 Flash Lite ($1.05 / 1M tokens) and Morph: Morph V3 Large ($3.15 / 1M tokens) using current USD prices and capabilities from the api14 catalog. Both run through the same OpenAI-compatible API.

Specifications

	Google: Gemini 3.5 Flash Lite	Morph: Morph V3 Large
Provider	Google	Morph
Input	$1.05 / 1M tokens	$3.15 / 1M tokens
Output	$8.75 / 1M tokens	$6.65 / 1M tokens
Cache read	$0.11 / 1M tokens	-
Cache write	$0.29 / 1M tokens	-
Reasoning	$8.75 / 1M tokens	-
Audio input	$1.05 / 1M tokens	-
Image	$0.000001 / image	-
Web search	$49.00 / 1K searches	-
Context window	1,048,576	262,144
Max output	65,536	131,072
Input	text, image, video, file, audio	text
Output	text	text
Tool calling
Structured output
Reasoning control
Added	Jul 21, 2026	Jul 7, 2025

Key differences

Google: Gemini 3.5 Flash Lite is 67% cheaper on input tokens.
Morph: Morph V3 Large is 24% cheaper on output tokens.
Google: Gemini 3.5 Flash Lite reads 4.0× more context (1,048,576 tokens).
Only Google: Gemini 3.5 Flash Lite supports tool calling.
Only Google: Gemini 3.5 Flash Lite supports reasoning effort control.
A request with 1,000 input and 500 output tokens costs $0.00542 with Google: Gemini 3.5 Flash Lite and $0.00648 with Morph: Morph V3 Large.

In depth

Google: Gemini 3.5 Flash Lite is a text model from Google, and Morph: Morph V3 Large is a text model from Morph. Both are served through api14 under the same OpenAI-compatible interface, so the choice between them comes down to what your workload actually needs rather than to any difference in how you connect.

The two differ most in context and in what they accept as input. Gemini 3.5 Flash Lite has a much larger context window, over a million tokens, while Morph V3 Large sits at a quarter of a million tokens. Gemini 3.5 Flash Lite also accepts text, images, video, files and audio as input and returns text, whereas Morph V3 Large takes text only. On parameters, Gemini 3.5 Flash Lite is the only one of the two that exposes tools, tool choice and reasoning controls, including reasoning effort and include reasoning, so anything that needs function calling or explicit reasoning behaviour points to it. Morph V3 Large is the only one that exposes logprobs and top logprobs, which matters if you want token level probabilities. Both support structured outputs and response format, so that is not a deciding factor. On price, Gemini 3.5 Flash Lite is the cheaper of the two for input, while Morph V3 Large is the cheaper of the two for output.

For practical work, reach for Gemini 3.5 Flash Lite when you are feeding in long documents, mixed media, or anything that needs tool calls, structured extraction driven by reasoning, or a very large working context. Its multimodal input makes it the natural fit for tasks that start from a screenshot, a recording, a PDF or an audio clip. Reach for Morph V3 Large when your task is text in, text out, your prompts fit comfortably inside a quarter of a million tokens, and you either want logprobs for scoring, classification confidence or sampling work, or you are generating a lot of output tokens and want the cheaper side of that trade. If your pipeline is pure text and modest in length, Morph V3 Large is a reasonable default; the moment media or tools enter the picture, it is not.

Calling either model is the same operation. Point your OpenAI-compatible client at api14, keep your existing request shape, and set the model field to google/gemini-3.5-flash-lite or morph/morph-v3-large. Everything else, your messages, temperature, max tokens and response format, carries over unchanged. The one thing to watch is that parameters are not identical across the two: tools, tool choice and the reasoning options only apply to Gemini 3.5 Flash Lite, and logprobs and top logprobs only apply to Morph V3 Large, so a request that works against one may need those fields removed before it works against the other.

There are still cases where the less obvious model wins. If you want token level probabilities alongside your text generation, Morph V3 Large is the only option here, since Gemini 3.5 Flash Lite does not expose logprobs. And if your workload is output heavy rather than input heavy, Morph V3 Large is the cheaper side on output, which can matter more than context size when your prompts are short and your completions are long.

Switch with one string

Both models use the same endpoint and request shape. Change the model value from "google/gemini-3.5-flash-lite" to "morph/morph-v3-large" and prompts, streaming and tools keep working.

Google: Gemini 3.5 Flash Lite details & playground →Morph: Morph V3 Large 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": "morph/morph-v3-large",
  "messages": [
    {
      "role": "user",
      "content": "Explain vector databases in two sentences."
    }
  ]
});
console.log(result.choices[0].message.content);

Google: Gemini 3.5 Flash Lite vs Morph: Morph V3 Large

Specifications

Key differences

In depth

Switch with one string

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