Side-by-side comparison
GLM 4.6V vs Qwen3 VL 235B A22B Instruct
Qwen
Qwen3 VL 235B A22B Instruct
👁 Vision
🔧 Tools
{} JSON
Input / 1M
$0.2000
Output / 1M
$0.8800
| GLM 4.6V | Qwen3 VL 235B A22B Instruct | |
|---|---|---|
| Provider | Z.ai | Qwen |
| Context window Maximum tokens (input + output) the model can process in a single request. Glossary → | 131,072 | 262,144 |
| Capabilities Optional capabilities the model advertises: vision (images), tools (function calling), json_mode (structured output). | vision, tools | vision, tools, json_mode |
| Input $ / 1M tokens Cost for tokens you send (prompt + context). Cheaper side highlighted. Glossary → | 0.3000 | 0.2000 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 0.9000 | 0.8800 |
Frequently asked questions
Which is cheaper, GLM 4.6V or Qwen3 VL 235B A22B Instruct?
Qwen3 VL 235B A22B Instruct is cheaper than GLM 4.6V on a 50/50 input/output blend by about $0.06 per 1M tokens. Exact savings depend on your input-vs-output ratio — use the cost calculator on this page for a workload-specific estimate.
Which has a larger context window, GLM 4.6V or Qwen3 VL 235B A22B Instruct?
Qwen3 VL 235B A22B Instruct has the larger context window at 262k tokens versus 131k tokens for GLM 4.6V. That means Qwen3 VL 235B A22B Instruct can ingest about 2.0x as much text per request.
What is the difference between GLM 4.6V and Qwen3 VL 235B A22B Instruct?
GLM 4.6V comes from Z.ai; Qwen3 VL 235B A22B Instruct comes from Qwen. They differ in pricing, context window, and supported capabilities — see the side-by-side table on this page for the exact figures, refreshed nightly.