API cost decision in 10 seconds

GLM 4.5 Air (free) vs Qwen3 30B A3B Instruct 2507

Pick GLM 4.5 Air (free) for lower cost; pick Qwen3 30B A3B Instruct 2507 only if the larger context window matters more.

Page updated: 2026-05-26T15:08:08Z Data confirmed: 2026-05-26T18:08:02Z Prices normalized to USD per 1M tokens Sample workload: 1M input + 500K output

Budget verdict

Pick GLM 4.5 Air (free) for lower cost; pick Qwen3 30B A3B Instruct 2507 only if the larger context window matters more.

On the standard 1M input plus 500K output workload, GLM 4.5 Air (free) is estimated at $0 vs $0.24 for Qwen3 30B A3B Instruct 2507, saving $0.24 (100% lower).

Cost-first pickGLM 4.5 Air (free)

Context-first pickQwen3 30B A3B Instruct 2507

Sample savings$0.24100%

10x traffic gap$2.4

Qwen3 30B A3B Instruct 2507 has more context, but GLM 4.5 Air (free) saves $0.24 on the standard workload. At 10x that traffic, the same price gap is about $2.4. Use the calculator below to replace the sample workload with your own token volume.

Cost sensitivity

Workload Sensitivity

Same prices, different token mixes.

GLM 4.5 Air (free) stays cheaper across input-heavy, balanced, and output-heavy sample workloads.

Workload shape	Token mix	Better pick	GLM 4.5 Air (free)	Qwen3 30B A3B Instruct 2507
Input-heavy / RAG	5M input + 500K output	GLM 4.5 Air (free)	$0	$0.6
Balanced workload	1M input + 1M output	GLM 4.5 Air (free)	$0	$0.39
Output-heavy chatbot	1M input + 5M output	GLM 4.5 Air (free)	$0	$1.59

Cheaper input GLM 4.5 Air (free) $0 vs $0.09 / 1M

GLM 4.5 Air (free) is free for input tokens while Qwen3 30B A3B Instruct 2507 costs $0.09 per 1M tokens.

Cheaper output GLM 4.5 Air (free) $0 vs $0.3 / 1M

GLM 4.5 Air (free) is free for output tokens while Qwen3 30B A3B Instruct 2507 costs $0.3 per 1M tokens.

Larger context Qwen3 30B A3B Instruct 2507 131.07K vs 262.14K

Qwen3 30B A3B Instruct 2507 has 131.07K more context (2x larger).

Sample workload GLM 4.5 Air (free) $0 vs $0.24

GLM 4.5 Air (free) is free for the standard workload while the other model is estimated at $0.24.

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 (free) Calculating… Estimated API cost

Qwen3 30B A3B Instruct 2507 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 (free) has the lower input price; GLM 4.5 Air (free) has the lower output price; Qwen3 30B A3B Instruct 2507 offers the larger context window. For the 1M input plus 500K output sample, GLM 4.5 Air (free) is cheaper for the standard workload.

For a 1M input token plus 500K output token workload, the estimated API cost is $0 for GLM 4.5 Air (free) and $0.24 for Qwen3 30B A3B Instruct 2507.

Best Fit

Choose GLM 4.5 Air (free) when you care most about lower input-token price, and lower output-token price.

Choose Qwen3 30B A3B Instruct 2507 when you care most about larger context window.

Decision Notes

On the standard 1M input plus 500K output workload, GLM 4.5 Air (free) is estimated at $0 vs $0.24 for Qwen3 30B A3B Instruct 2507, saving $0.24 (100% lower).
GLM 4.5 Air (free) is free for the standard workload while the other model is estimated at $0.24.
GLM 4.5 Air (free) is free for input tokens while Qwen3 30B A3B Instruct 2507 costs $0.09 per 1M tokens.
GLM 4.5 Air (free) is free for output tokens while Qwen3 30B A3B Instruct 2507 costs $0.3 per 1M tokens.
Qwen3 30B A3B Instruct 2507 has 131.07K more context (2x larger).

Head-to-Head Specs

Feature	GLM 4.5 Air (free) (Z.ai)	Qwen3 30B A3B Instruct 2507 (Qwen)
Input Price prompt tokens per 1M	$0	$0.09
Completion Price per 1M tokens	$0	$0.3
Sample Workload Cost 1M input + 500K output	$0	$0.24
Context Window	131.07K	262.14K
Release Date	2025-07-25	2025-07-29
Popularity	#41	#110

Use-Case Decision Matrix

Use case	Better pick	Why
Budget-constrained production	GLM 4.5 Air (free)	On the standard 1M input plus 500K output workload, GLM 4.5 Air (free) is estimated at $0 vs $0.24 for Qwen3 30B A3B Instruct 2507, saving $0.24 (100% lower).
High-volume input processing	GLM 4.5 Air (free)	Lower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill.
Long responses and chatbots	GLM 4.5 Air (free)	Lower output-token price matters most when assistants generate many completion tokens.
RAG or long-document work	Qwen3 30B A3B Instruct 2507	A larger context window leaves more room for retrieved passages, conversation history, or source files.

Related Alternatives

Same-provider lower-cost swaps

Qwen3 Next 80B A3B Instruct (free) can replace Qwen3 30B A3B Instruct 2507 when lower sample workload cost matters most: $0.
Qwen3 Coder 480B A35B (free) can replace Qwen3 30B A3B Instruct 2507 when lower sample workload cost matters most: $0.
Qwen2.5 7B Instruct can replace Qwen3 30B A3B Instruct 2507 when lower sample workload cost matters most: $0.09.
Qwen3.5-9B can replace Qwen3 30B A3B Instruct 2507 when lower sample workload cost matters most: $0.11.

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.
MiMo-V2.5 offers 1.05M context with $0.28 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

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Larger context alternatives

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Provider catalogs

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Z.ai catalog

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Qwen catalog

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GLM 4.5 Air (free)

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...

Qwen3 30B A3B Instruct 2507

Qwen3-30B-A3B-Instruct-2507 is a 30.5B-parameter mixture-of-experts language model from Qwen, with 3.3B active parameters per inference. It operates in non-thinking mode and is designed for high-quality instruction following, multilingual understanding, and...