GoogleTextVisionAudio

gemini-3.5-flash

gemini-3.5-flash is Google's model at $1.50 input and $9.00 output per million tokens, with a 1049K context window. On a typical 3:1 input-to-output mix that blends to roughly $3.38 per million tokens, placing it in the premium band of the 89 models tracked here — cheaper than 28% of them.

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MODEL OVERVIEW

Context window1049K

Input price$1.5 / 1M

Output price$9 / 1M

Output speed190 tok/s

Quality score86

Data updatedUpdated Aug 2026

Price verified2026-08-19 · Official

Price noteBatch and Flex tiers are 50% off ($0.75 / $4.50); cache-hit input is $0.15 / 1M tokens

By price, gemini-3.5-flash ranks 59th cheapest of 89 on input and 65th on output. Its quality score of 86 ranks 45th of 89, and on value (quality score ÷ output price) it ranks 59th of 89. Its blended rate sits 10% above the catalog average.

In real billing terms: a support chatbot handling 50,000 turns a month (2,000 input / 500 output tokens each) costs about $375 per month on gemini-3.5-flash, while a RAG workload of 20,000 queries (8,000 input / 800 output tokens each) runs about $384. Because output is priced 6.0× input here, long-generation tasks scale cost fastest — cap output length before you optimise anything else.

A 1049K context window holds roughly 1,399 pages of A4 text (at ~750 tokens per page), ranking 8th of 89 here. That is enough to drop in an entire technical manual or several source files at once, which suits long-document summarization and codebase-wide analysis. Output runs at about 190 tok/s (13th fastest of 89), fast enough for real-time interaction and high-throughput batches.

We track 8 Google models, and gemini-3.5-flash's blended rate sits 57% above that provider's average. If your task tolerates a little less headroom, Hunyuan HY3 (Tencent) blends about 97% cheaper at a quality score of 87, and is worth benchmarking first. Going the other way, qwen3.5 scores 91 on quality for about 97% less.

The closest model in Google's line-up is gemini-2.5-pro ($1.25 input / $10.00 output, quality 93). Against it, gemini-3.5-flash scores 7 points lower on quality, runs 125 tok/s faster (about 192%), costs 2% less blended. When two models sit this close on price, the deciding factor is usually the quality ceiling — run both against your real prompts before picking.

Our read on it

It is called Flash and charges $9 for output. The real discount is not in the list price — it is in the billing tiers.

$1.50 in, $9.00 out: a counter-intuitive ratio

gemini-3.5-flash prices input to output at 1:6. Comparable models mostly land between 1:2 and 1:5, and the Flash name signals cheap-and-fast, so the $9 output rate reads like a typo on first look.

It is not. It describes a model built to consume a great deal of input and emit very little. Pair 190 tokens/second with a 1,049K context and text, vision and audio, and the intended shape becomes obvious: transcript summarisation, long-document extraction, multimodal classification — enormous input, a few hundred tokens out. In that shape the output rate is not what your invoice is made of.

The price that matters lives in Batch, Flex and caching

Google offers Batch and Flex tiers on this model at half list — $0.75/$4.50. Cache-hit input is cheaper still at $0.15 per 1M tokens, a tenth of the list rate.

Which means what you actually pay depends far more on whether your workload tolerates asynchronous execution and repeated prefixes than on which model you picked. A pipeline that can run in Batch behind a stable system prompt costs a fraction of the two headline numbers. A synchronous one-shot request with a fresh prefix every time pays list. Comparison tables cannot see that distinction, and factoring it in frequently reverses the answer.

Check that you do not want gemini-2.5-flash instead

Google's own gemini-2.5-flash is $0.30/$2.50 at quality 84. This one is $1.50/$9.00 at quality 86 — five times the input rate and 3.6 times the output rate for two points. Unless you specifically need the 1,049K context (2.5-flash does not have it) or the audio modality, that upgrade is hard to justify.

Across vendors: claude-haiku-4-5 at $1.00/$5.00 and 180 tokens/second is cheaper for text-only interactive work, and if you need multimodal on a tight budget, gpt-5.6-luna at $0.20/$1.20 with quality 90 and the same three modalities beats it on essentially every axis. What gemini-3.5-flash genuinely owns is high-throughput multimodal processing over a million-token context inside Google's ecosystem. Outside that intersection, price the other two first.

Estimated monthly cost

Monthly API spend across four common workloads, next to the cheapest model we track.

Workload	Usage assumption	This model	Cheapest model
Support chatbot	2,000 input / 500 output tokens per turn, 50,000 turns per month	$375	$3.00
RAG document Q&A	8,000 input / 800 output tokens per query, 20,000 queries per month	$384	$3.84
Coding assistant	12,000 input / 2,000 output tokens per request, 10,000 requests per month	$360	$3.20
Batch summarization	4,000 input / 400 output tokens per item, 200,000 items per month	$1,920	$19

Derived directly from the rates on this page. Excludes prompt caching, batch discounts and free tiers.

How it compares

The alternatives worth benchmarking alongside it, from the same dataset.

Model	Provider	Blended /1M	Quality	Price delta
gemini-3.5-flash	Google	$3.38	86	—
Hunyuan HY3Cheaper option	Tencent	$0.10	87	-97%
qwen3.5Higher quality	Alibaba	$0.11	91	-97%
gemini-2.5-proSame provider	Google	$3.44	93	+2%
qwen3.7-flashBest value overall	Alibaba	$0.055	82	-98%

Best for

ChatbotFastMultimodal

Modalities

TextVisionAudio

Where it fits — and where it doesn't

Good fit

Chat and support: natural multi-turn conversation for user-facing products.
Long documents: a 1049K window ingests a full contract, paper or large codebase in one pass.
High throughput and real-time UX: 190 tok/s keeps streamed responses feeling instant.
Multimodal input: handles images, screenshots and scanned documents directly.

Poor fit

High-frequency batch jobs: at $3.38 blended per million tokens, bills scale quickly at volume.

Frequently asked questions

How much does the gemini-3.5-flash API cost?

gemini-3.5-flash costs $1.50 per million input tokens and $9.00 per million output tokens. On a 3:1 input-to-output mix that blends to about $3.38 per million tokens.

What does gemini-3.5-flash cost per month?

It depends on volume. Using the four worked scenarios on this page: a support chatbot runs about $375/month, RAG document Q&A about $384/month, a coding assistant about $360/month, and batch summarization about $1,920/month. Plug your own token counts into the cost calculator for a tailored figure.

How large is the gemini-3.5-flash context window?

1049K tokens — roughly 1,399 pages of A4 text at ~750 tokens per page, ranking 8th of the 89 models tracked here.

gemini-3.5-flash or Hunyuan HY3 — which is better value?

Hunyuan HY3 blends to about $0.10 per million tokens versus $3.38 for gemini-3.5-flash — roughly 97% cheaper — at a quality score of 87 against 86. If that quality gap does not show up on your task, take the cheaper one; if it does, stay with gemini-3.5-flash. Benchmark both on your own data before deciding.

What is gemini-3.5-flash best suited for?

Tagged use cases: chatbot, fast, multimodal. Chat and support: natural multi-turn conversation for user-facing products. Less suitable: High-frequency batch jobs: at $3.38 blended per million tokens, bills scale quickly at volume.

How current is this gemini-3.5-flash pricing?

Pricing here was last verified on 2026-08-19 against official documentation. llmprice.app runs a daily collection job, but providers can change rates between runs — confirm against Google's official pricing page before you commit.