API cost decision in 10 seconds

Nano Banana (Gemini 2.5 Flash Image) vs GLM 4.6

Pick GLM 4.6 when budget and context both matter.

Page updated: 2026-05-26T15:08:08Z Data confirmed: 2026-05-30T18:07:59Z Prices normalized to USD per 1M tokens Sample workload: 1M input + 500K output

Budget verdict

Pick GLM 4.6 when budget and context both matter.

On the standard 1M input plus 500K output workload, GLM 4.6 is estimated at $1.3 vs $1.55 for Nano Banana (Gemini 2.5 Flash Image), saving $0.25 (16.1% lower).

Cost-first pickGLM 4.6

Context-first pickGLM 4.6

Sample savings$0.2516.1%

10x traffic gap$2.5

GLM 4.6 is cheaper on the standard workload and also has the larger context window. At 10x that traffic, the same price gap is about $2.5. 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 Nano Banana (Gemini 2.5 Flash Image), balanced workload favors GLM 4.6, and output-heavy chatbot favors GLM 4.6.

Workload shape	Token mix	Better pick	Nano Banana (Gemini 2.5 Flash Image)	GLM 4.6
Input-heavy / RAG	5M input + 500K output	Nano Banana (Gemini 2.5 Flash Image)	$2.75	$3.02
Balanced workload	1M input + 1M output	GLM 4.6	$2.8	$2.17
Output-heavy chatbot	1M input + 5M output	GLM 4.6	$12.8	$9.13

Cheaper input Nano Banana (Gemini 2.5 Flash Image) $0.3 vs $0.43 / 1M

Nano Banana (Gemini 2.5 Flash Image) is $0.13 cheaper per 1M input tokens (30.2% lower; 1.43x difference).

Cheaper output GLM 4.6 $2.5 vs $1.74 / 1M

GLM 4.6 is $0.76 cheaper per 1M output tokens (30.4% lower; 1.44x difference).

Larger context GLM 4.6 32.77K vs 202.75K

GLM 4.6 has 169.98K more context (6.19x larger).

Sample workload GLM 4.6 $1.55 vs $1.3

GLM 4.6 is $0.25 cheaper on the standard workload (16.1% lower).

Estimate your workload cost

Your Workload Cost

Prices are normalized to USD per 1M tokens.

Monthly input tokens Monthly output tokens

Nano Banana (Gemini 2.5 Flash Image) Calculating… Estimated API cost

GLM 4.6 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

Nano Banana (Gemini 2.5 Flash Image) has the lower input price; GLM 4.6 has the lower output price; GLM 4.6 offers the larger context window. For the 1M input plus 500K output sample, GLM 4.6 is cheaper for the standard workload.

For a 1M input token plus 500K output token workload, the estimated API cost is $1.55 for Nano Banana (Gemini 2.5 Flash Image) and $1.3 for GLM 4.6.

Best Fit

Choose Nano Banana (Gemini 2.5 Flash Image) when you care most about lower input-token price.

Choose GLM 4.6 when you care most about lower output-token price, and larger context window.

Decision Notes

On the standard 1M input plus 500K output workload, GLM 4.6 is estimated at $1.3 vs $1.55 for Nano Banana (Gemini 2.5 Flash Image), saving $0.25 (16.1% lower).
GLM 4.6 is $0.25 cheaper on the standard workload (16.1% lower).
Nano Banana (Gemini 2.5 Flash Image) is $0.13 cheaper per 1M input tokens (30.2% lower; 1.43x difference).
GLM 4.6 is $0.76 cheaper per 1M output tokens (30.4% lower; 1.44x difference).
GLM 4.6 has 169.98K more context (6.19x larger).

Head-to-Head Specs

Feature	Nano Banana (Gemini 2.5 Flash Image) (Google)	GLM 4.6 (Z.ai)
Input Price prompt tokens per 1M	$0.3	$0.43
Completion Price per 1M tokens	$2.5	$1.74
Sample Workload Cost 1M input + 500K output	$1.55	$1.3
Context Window	32.77K	202.75K
Release Date	2025-10-07	2025-09-30
Popularity	#113	#122

Use-Case Decision Matrix

Use case	Better pick	Why
Budget-constrained production	GLM 4.6	On the standard 1M input plus 500K output workload, GLM 4.6 is estimated at $1.3 vs $1.55 for Nano Banana (Gemini 2.5 Flash Image), saving $0.25 (16.1% lower).
High-volume input processing	Nano Banana (Gemini 2.5 Flash Image)	Lower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill.
Long responses and chatbots	GLM 4.6	Lower output-token price matters most when assistants generate many completion tokens.
RAG or long-document work	GLM 4.6	A larger context window leaves more room for retrieved passages, conversation history, or source files.

Related Alternatives

Same-provider lower-cost swaps

Gemma 4 31B (free) can replace Nano Banana (Gemini 2.5 Flash Image) when lower sample workload cost matters most: $0.
Gemma 4 26B A4B (free) can replace Nano Banana (Gemini 2.5 Flash Image) when lower sample workload cost matters most: $0.
Lyria 3 Clip Preview can replace Nano Banana (Gemini 2.5 Flash Image) when lower sample workload cost matters most: $0.
Lyria 3 Pro Preview can replace Nano Banana (Gemini 2.5 Flash Image) 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.
MiMo-V2.5 offers 1.05M context with $0.28 sample workload cost.

Popular competitors

Hy3 preview · Tencent · #1
DeepSeek V4 Flash · DeepSeek · #2
MiMo-V2.5 · Xiaomi · #3
Claude Sonnet 4.6 · Anthropic · #4

Cheaper alternatives

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Larger context alternatives

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Provider catalogs

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

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Google catalog

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

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Z.ai catalog

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

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Nano Banana (Gemini 2.5 Flash Image)

Gemini 2.5 Flash Image, a.k.a. "Nano Banana," is now generally available. It is a state of the art image generation model with contextual understanding. It is capable of image generation,...

GLM 4.6

Compared with GLM-4.5, this generation brings several key improvements: Longer context window: The context window has been expanded from 128K to 200K tokens, enabling the model to handle more complex...