API cost decision in 10 seconds

GLM 4.5 Air vs Qwen2.5 VL 72B Instruct

Pick GLM 4.5 Air when budget is the priority.

Page updated: 2026-05-26T19:08:13Z Data confirmed: 2026-05-26T19:08:13Z Prices normalized to USD per 1M tokens Sample workload: 1M input + 500K output

Budget verdict

Pick GLM 4.5 Air when budget is the priority.

On the standard 1M input plus 500K output workload, GLM 4.5 Air is estimated at $0.54 vs $0.62 for Qwen2.5 VL 72B Instruct, saving $0.08 (12.8% lower).

Cost-first pickGLM 4.5 Air

Context-first pickBoth models

Sample savings$0.0812.8%

10x traffic gap$0.8

The reported context window is tied, so cost and provider fit carry more weight. At 10x that traffic, the same price gap is about $0.8. Use the calculator below to replace the sample workload with your own token volume.

Cost sensitivity

Workload Sensitivity

Same prices, different token mixes.

Cost winner changes by workload shape: input-heavy / RAG favors GLM 4.5 Air, balanced workload favors GLM 4.5 Air, and output-heavy chatbot favors Qwen2.5 VL 72B Instruct.

Workload shape	Token mix	Better pick	GLM 4.5 Air	Qwen2.5 VL 72B Instruct
Input-heavy / RAG	5M input + 500K output	GLM 4.5 Air	$1.04	$1.62
Balanced workload	1M input + 1M output	GLM 4.5 Air	$0.96	$1
Output-heavy chatbot	1M input + 5M output	Qwen2.5 VL 72B Instruct	$4.33	$4

Cheaper input GLM 4.5 Air $0.125 vs $0.25 / 1M

GLM 4.5 Air is $0.12 cheaper per 1M input tokens (50% lower; 2x difference).

Cheaper output Qwen2.5 VL 72B Instruct $0.84 vs $0.75 / 1M

Qwen2.5 VL 72B Instruct is $0.09 cheaper per 1M output tokens (10.7% lower; 1.12x difference).

Larger context Tie 131.07K vs 131.07K

Both models report the same context window at 131.07K tokens.

Sample workload GLM 4.5 Air $0.54 vs $0.62

GLM 4.5 Air is $0.08 cheaper on the standard workload (12.8% lower).

Estimate your workload cost

Your Workload Cost

Prices are normalized to USD per 1M tokens.

Monthly input tokens Monthly output tokens

GLM 4.5 Air Calculating… Estimated API cost

Qwen2.5 VL 72B Instruct Calculating… Estimated API cost

Cheaper for this workload Calculating… Difference: calculating…

This estimate uses normalized public API pricing per 1M tokens. It is a planning aid, not a billing quote. Verify provider pricing, limits, and terms before production use.

Quick Decision

Verdict

GLM 4.5 Air has the lower input price; Qwen2.5 VL 72B Instruct has the lower output price; both models report the same context window. For the 1M input plus 500K output sample, GLM 4.5 Air is cheaper for the standard workload.

For a 1M input token plus 500K output token workload, the estimated API cost is $0.54 for GLM 4.5 Air and $0.62 for Qwen2.5 VL 72B Instruct.

Best Fit

Choose GLM 4.5 Air when you care most about lower input-token price.

Choose Qwen2.5 VL 72B Instruct when you care most about lower output-token price.

Decision Notes

On the standard 1M input plus 500K output workload, GLM 4.5 Air is estimated at $0.54 vs $0.62 for Qwen2.5 VL 72B Instruct, saving $0.08 (12.8% lower).
GLM 4.5 Air is $0.08 cheaper on the standard workload (12.8% lower).
GLM 4.5 Air is $0.12 cheaper per 1M input tokens (50% lower; 2x difference).
Qwen2.5 VL 72B Instruct is $0.09 cheaper per 1M output tokens (10.7% lower; 1.12x difference).
Both models report the same context window at 131.07K tokens.

Head-to-Head Specs

Feature	GLM 4.5 Air (Z.ai)	Qwen2.5 VL 72B Instruct (Qwen)
Input Price prompt tokens per 1M	$0.125	$0.25
Completion Price per 1M tokens	$0.84	$0.75
Sample Workload Cost 1M input + 500K output	$0.54	$0.62
Context Window	131.07K	131.07K
Release Date	2025-07-25	2025-02-01
Popularity	#37	#150

Use-Case Decision Matrix

Use case	Better pick	Why
Budget-constrained production	GLM 4.5 Air	On the standard 1M input plus 500K output workload, GLM 4.5 Air is estimated at $0.54 vs $0.62 for Qwen2.5 VL 72B Instruct, saving $0.08 (12.8% lower).
High-volume input processing	GLM 4.5 Air	Lower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill.
Long responses and chatbots	Qwen2.5 VL 72B Instruct	Lower output-token price matters most when assistants generate many completion tokens.
RAG or long-document work	Tie	A larger context window leaves more room for retrieved passages, conversation history, or source files.

Related Alternatives

Same-provider lower-cost swaps

GLM 4.5 Air (free) can replace GLM 4.5 Air when lower sample workload cost matters most: $0.
GLM 4 32B can replace GLM 4.5 Air when lower sample workload cost matters most: $0.15.
GLM 4.7 Flash can replace GLM 4.5 Air when lower sample workload cost matters most: $0.26.
Qwen3 Next 80B A3B Instruct (free) can replace Qwen2.5 VL 72B Instruct when lower sample workload cost matters most: $0.

Larger context near this budget

Llama 4 Scout offers 10M context with $0.23 sample workload cost.
Owl Alpha offers 1.05M context with $0 sample workload cost.
DeepSeek V4 Flash offers 1.05M context with $0.2 sample workload cost.
Gemini 2.5 Flash Lite offers 1.05M context with $0.3 sample workload cost.

Popular competitors

DeepSeek V4 Flash · DeepSeek · #1
Hy3 preview · Tencent · #2
Claude Opus 4.7 · Anthropic · #3
Claude Sonnet 4.6 · Anthropic · #4

Cheaper alternatives

Review low-cost models sorted by a standard 1M input plus 500K output workload.

Open cheapest models

Larger context alternatives

Find models with larger context windows for RAG, long documents, and codebase review.

Open largest context models

Provider catalogs

Compare models within provider hubs before choosing a final API vendor.

Open provider hubs

Z.ai catalog

Review all tracked Z.ai models before deciding whether this matchup is the right shortlist.

Open Z.ai models

Qwen catalog

Check other Qwen models with comparable pricing, context, or release timing.

Open Qwen models

GLM 4.5 Air

GLM-4.5-Air is the lightweight variant of our latest flagship model family, also purpose-built for agent-centric applications. Like GLM-4.5, it adopts the Mixture-of-Experts (MoE) architecture but with a more compact parameter...

Qwen2.5 VL 72B Instruct

Qwen2.5-VL is proficient in recognizing common objects such as flowers, birds, fish, and insects. It is also highly capable of analyzing texts, charts, icons, graphics, and layouts within images.