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Founder’s Open-Model Stack: GLM-4.7, Qwen3-VL, DeepSeek-V3.2, Kimi-K2, FLUX.2

Agent Native•January 22, 2026

If you’re building an AI product as a solo founder or a small team, you don’t need one “best” model.

You need a stack!

A small menu of models that each do one job extremely well, plus a router so your app automatically picks the right one based on the request.

And needles to say, with cost/latency constraints.

Below is a pragmatic, product-minded walkthrough based on this stack:

OCR / doc understanding: Qwen3-VL, GLM-4.6V
Cheap coding: Qwen3-Coder
Coding (stronger agents): GLM-4.7, MiniMax-M2.1
Reasoning: DeepSeek-V3.2-Speciale
Writing: Kimi-K2, Kimi-K2-Thinking
General-purpose: DeepSeek-V3.2
Image generation: FLUX.2-dev, Z-Image-Turbo
Image editing: Qwen-Image-Edit-2509

Let’s see how each one helps you shipping performant products.

One rule before you ship: “open source” vs “open weights”

In 2026, many “open” models ship as open weights with licenses that vary from permissive (Apache/MIT) to restricted.

Keep Reading

Code

python

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="token-abc123")

resp = client.chat.completions.create(
  model="Qwen/Qwen3-Coder-30B-A3B-Instruct",
  messages=[{"role": "user", "content": "Write a FastAPI endpoint that validates a JWT and returns the user id."}],
)

print(resp.choices[0].message.content)

Code

python

messages = [{
"role": "user",
"content": [
  {"type": "text", "text":
    "Extract all visible text and key fields from this receipt. "
    "Return STRICT JSON: {merchant, date, total, currency, line_items:[{name, qty, price}], raw_text}. "
    "If a field is missing, use null. Do not guess."
  },
  {"type": "image_url", "image_url": {"url": "data:image/png;base64,<...>"}}
]
}]

Code

bash

import torch
from diffusers import ZImagePipeline

pipe = ZImagePipeline.from_pretrained(
  "Tongyi-MAI/Z-Image-Turbo",
  torch_dtype=torch.bfloat16,
  low_cpu_mem_usage=False,

).to("cuda")

image = pipe(
  prompt="A clean SaaS landing page hero illustration, minimal, white background",
  height=1024, width=1024,
  num_inference_steps=9,
  guidance_scale=0.0,
).images[0]

Code

bash

import os
import torch
from PIL import Image
from diffusers import QwenImageEditPlusPipeline

pipeline = QwenImageEditPlusPipeline.from_pretrained(
  "Qwen/Qwen-Image-Edit-2509", torch_dtype=torch.bfloat16
).to("cuda")

image1 = Image.open("input1.png")
image2 = Image.open("input2.png")
prompt = "Put the product from image1 on a clean studio background from image2. Keep the logo sharp."

out = pipeline(
  image=[image1, image2],
  prompt=prompt,
  generator=torch.manual_seed(0),
  true_cfg_scale=4.0,
  negative_prompt=" ",
  num_inference_steps=40,
  guidance_scale=1.0,
).images[0]

out.save("edited.png")

print("saved:", os.path.abspath("edited.png"))

Code

python

TASK_TO_MODEL = {
"ocr": "Qwen/Qwen3-VL-8B-Instruct",
"cheap_code": "Qwen/Qwen3-Coder-30B-A3B-Instruct",
"agentic_code": "zai-org/GLM-4.7-Flash",   # or MiniMaxAI/MiniMax-M2.1
"reasoning": "deepseek-ai/DeepSeek-V3.2-Speciale",
"writing": "moonshotai/Kimi-K2",
"writing_thinking": "moonshotai/Kimi-K2-Thinking",
"general": "deepseek-ai/DeepSeek-V3.2",
}

def route(intent: str, hard: bool=False) -> str:
  if intent == "writing" and hard:
      return TASK_TO_MODEL["writing_thinking"]
  if intent == "code" and hard:
      return TASK_TO_MODEL["agentic_code"]
  return TASK_TO_MODEL.get(intent, TASK_TO_MODEL["general"])