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Qwen3.8 Flash vs GLM 5.3 Flash: API price, context & features · api14

Qwen: Qwen3.8 Flash vs Z.ai: GLM 5.3 Flash

A side-by-side comparison of Qwen: Qwen3.8 Flash ($0.52 / 1M tokens) and Z.ai: GLM 5.3 Flash ($0.52 / 1M tokens) using current USD prices and capabilities from the api14 catalog. Both run through the same OpenAI-compatible API.

Specifications

	Qwen: Qwen3.8 Flash	Z.ai: GLM 5.3 Flash
Provider	Qwen	Z.ai
Input	$0.52 / 1M tokens	$0.52 / 1M tokens
Output	$1.65 / 1M tokens	$1.75 / 1M tokens
Cache read	$0.06 / 1M tokens	$0.11 / 1M tokens
Context window	1,000,000	1,048,576
Max output	131,072	943,717
Input	text, image, video	text, image, video
Output	text	text
Tool calling
Structured output
Reasoning control
Added	Aug 26, 2026	Aug 26, 2026

Key differences

Both have about the same input price.
Qwen: Qwen3.8 Flash is 6% cheaper on output tokens.
A request with 1,000 input and 500 output tokens costs $0.00135 with Qwen: Qwen3.8 Flash and $0.00140 with Z.ai: GLM 5.3 Flash.

In depth

Qwen: Qwen3.8 Flash is a text model from Qwen, and Z.ai: GLM 5.3 Flash is a text model from Z.ai. Both are listed as text category models, both accept text, image and video as input, and both return text only. Both carry the same provider release date of 2026-08-26, so neither is presented here as newer than the other. For a developer, the practical starting point is that these are two competing flash tier models from two different providers, sold side by side through the same gateway.

On raw capacity the two are close. Qwen3.8 Flash has a context length of 1,000,000 tokens, while GLM 5.3 Flash lists 1,048,576 tokens, which is a marginal difference rather than a meaningful gap, so context size should not be the deciding factor. Both take image and video input alongside text, so multimodal input work is available on either. The parameter lists are where they diverge. Qwen3.8 Flash supports frequency penalty, include reasoning, logprobs, max tokens, presence penalty, reasoning, response format, seed, stop, structured outputs, temperature, tool choice, tools, top k, top logprobs and top p. GLM 5.3 Flash supports all of those plus logit bias, min p, parallel tool calls, reasoning effort and repetition penalty. That means GLM 5.3 Flash gives you finer control over sampling (min p, logit bias, repetition penalty), over how much reasoning effort is spent, and over whether tool calls can run in parallel. If your application depends on any of those specific knobs, GLM 5.3 Flash is the one that exposes them.

For task selection, think about what your workload actually needs. If you are doing long document or long video understanding where you want a very large context and straightforward text generation, either model fits, and the context difference is not worth optimizing around. If you are building agents that issue multiple tool calls per turn, or you want to tune reasoning effort per request, or you need min p, logit bias or repetition penalty to shape output, GLM 5.3 Flash is the more natural fit because those parameters exist there. If your pipeline is already written against the Qwen parameter set and you do not need those extra controls, Qwen3.8 Flash covers the common surface: temperature, top p, top k, penalties, seeds, stop sequences, structured outputs and tools. Cost also leans one way: GLM 5.3 Flash is the cheaper of the two on both input and output, so at equal quality and equal feature fit it is the lower cost call. Where the two are about the same is context size, and neither is listed as the only one supporting tools, structured outputs or reasoning, so those are not differentiators.

Calling either model through api14 uses the same OpenAI-compatible interface. You point your 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 Qwen3.8 Flash, set the model id to "qwen/qwen3.8-flash". To use GLM 5.3 Flash, set the model id to "z-ai/glm-5.3-flash". Everything else in the request body stays the same shape: messages, temperature, max tokens, tools, response format and the rest. The one thing to watch is that the two models do not accept an identical parameter set, so if you switch ids and send a parameter that only one side supports, expect that request to be rejected or ignored depending on how the gateway handles it. Keep your shared parameters in one place and gate the model specific ones (logit bias, min p, parallel tool calls, reasoning effort, repetition penalty) behind a check on which model id you are calling.

Switch with one string

Both models use the same endpoint and request shape. Change the model value from "qwen/qwen3.8-flash" to "z-ai/glm-5.3-flash" and prompts, streaming and tools keep working.

Qwen: Qwen3.8 Flash details & playground →Z.ai: GLM 5.3 Flash 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": "z-ai/glm-5.3-flash",
  "messages": [
    {
      "role": "user",
      "content": "Explain vector databases in two sentences."
    }
  ]
});
console.log(result.choices[0].message.content);

Qwen: Qwen3.8 Flash vs Z.ai: GLM 5.3 Flash

Specifications

Key differences

In depth

Switch with one string

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