Which is cheaper, GLM-4.5V or Qwen2.5 72B Instruct?
As of August 20, 2026, Qwen2.5 72B Instruct is 69% cheaper on blended LLM API pricing. Qwen2.5 72B Instruct costs $0.35 per million input tokens and $0.40 per million output tokens ($0.75 blended 1M-in + 1M-out). GLM-4.5V costs $0.60 / $1.80 per million tokens ($2.40 blended). Sources: https://docs.z.ai/guides/overview/pricing and https://bailian.console.aliyun.com/. The cheapest LLM for your app still depends on how many output tokens you generate.
How much does GLM-4.5V vs Qwen2.5 72B Instruct cost per million tokens?
GLM-4.5V Z AI API pricing is $0.60 input and $1.80 output per million tokens via Z AI. Qwen2.5 72B Instruct Qwen API pricing is $0.35 input and $0.40 output per million tokens via Qwen. Use those four numbers, not ChatGPT Plus or Claude Pro subscription prices, when you are comparing APIs.
Which is better for coding, GLM-4.5V or Qwen2.5 72B Instruct?
Qwen2.5 72B Instruct has a published LiveCodeBench score of 55.5%. GLM-4.5V does not have that eval on this page yet.
What is the context window for GLM-4.5V vs Qwen2.5 72B Instruct?
GLM-4.5V supports a 66K token context window, while Qwen2.5 72B Instruct supports 131K tokens. Qwen2.5 72B Instruct offers a larger context window. Filling a larger window bills more input tokens, so the cheaper-per-million model can still cost more on long documents.
Which model performs better on benchmarks, GLM-4.5V or Qwen2.5 72B Instruct?
Shared benchmark scores for GLM-4.5V and Qwen2.5 72B Instruct are still limited. Check the comparison table for the evals each model has published. Check the comparison table for GPQA Diamond, SWE-bench, MMLU, HLE, and the other evals we track. Token price and benchmark score together are the usual LLM comparison, not either number alone.
Is GLM-4.5V or Qwen2.5 72B Instruct better for production use?
Both GLM-4.5V and Qwen2.5 72B Instruct are production API models. For cost-sensitive production traffic, Qwen2.5 72B Instruct has better economics on the blended 1M-in + 1M-out scale. For maximum capability, weight the benchmark table for your domain. Many production stacks route cheap models for drafts and a frontier model for the hard turn, which is usually cheaper than sending everything to the expensive API.
Can I switch between GLM-4.5V and Qwen2.5 72B Instruct in my app?
Yes. Call GLM-4.5V through Z AI and Qwen2.5 72B Instruct through Qwen with separate API keys, or through a gateway that already wraps both. Keep prompts in tokens, not characters, when you estimate the invoice. AnotherWrapper templates can swap providers without rewriting auth and billing.
How accurate is this GLM-4.5V vs Qwen2.5 72B Instruct pricing data?
List rates are the latest published Z AI API pricing and Qwen API pricing, quoted per million tokens. Official rate sheets: https://docs.z.ai/guides/overview/pricing and https://bailian.console.aliyun.com/. Prompt caching, batch APIs, and committed-use discounts can change the invoice. Benchmark scores are from official publications and independent evals, updated as new numbers land.
What is the output token limit for GLM-4.5V and Qwen2.5 72B Instruct?
GLM-4.5V supports up to 131K output tokens per request, while Qwen2.5 72B Instruct supports up to 8K output tokens. Output tokens are usually the expensive half of API pricing, so a higher max output cap is a capability, not a discount.
Which model has better throughput, GLM-4.5V or Qwen2.5 72B Instruct?
Qwen2.5 72B Instruct has a measured throughput of approximately 10 tokens/second. Throughput data for GLM-4.5V is not yet available. Throughput (tokens per second) and time-to-first-token are why two models with the same per-million-token price can still feel different in chat and agents.
How do I switch the models in this GLM-4.5V vs Qwen2.5 72B Instruct comparison?
Use the model pickers at the top of this LLM comparison. Search any model in the index and the URL updates. Open GLM-4.5V or Qwen2.5 72B Instruct from the sidebar for the single-model API pricing page, or go back to the LLM pricing table to sort by cheapest blended cost.
