GoogleTextVisionAudio

gemini-3.8-flash

gemini-3.8-flash is Google's model at $0.75 input and $3.75 output per million tokens, with a 1049K context window. On a typical 3:1 input-to-output mix that blends to roughly $1.50 per million tokens, placing it in the mid-range band of the 90 models tracked here — cheaper than 47% of them.

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MODEL OVERVIEW

Context window1049K

Input price$0.75 / 1M

Output price$3.75 / 1M

Output speed302 tok/s

Quality score92

Data updatedUpdated Sep 2026

Price verified2026-09-16 · Official

Price notePromotional rate through 2026-12-31; rises to $1.50 / $7.50 on 2027-01-01. Same per-token rate as 3.7-flash, but materially more verbose — Artificial Analysis measured ~70% more output tokens to finish the same index, which works out around 40% more per task

By price, gemini-3.8-flash ranks 44th cheapest of 90 on input and 48th on output. Its quality score of 92 ranks 19th of 90, and on value (quality score ÷ output price) it ranks 44th of 90. Its blended rate sits 51% below 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 $169 per month on gemini-3.8-flash, while a RAG workload of 20,000 queries (8,000 input / 800 output tokens each) runs about $180. Because output is priced 5.0× 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 9th of 90 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 302 tok/s (2nd fastest of 90), fast enough for real-time interaction and high-throughput batches.

We track 8 Google models, and gemini-3.8-flash's blended rate sits 30% below that provider's average. If your task tolerates a little less headroom, MAI-DS-R1 (Microsoft) blends about 88% cheaper at a quality score of 90, and is worth benchmarking first. Going the other way, deepseek-v4-pro scores 93 on quality for about 34% less.

The closest model in Google's line-up is gemini-3.7-flash ($0.75 input / $3.75 output, quality 90). Against it, gemini-3.8-flash scores 2 points higher on quality, runs 14 tok/s faster (about 5%). When two models sit this close on price, the deciding factor is usually the quality ceiling — run both against your real prompts before picking.

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	$169	$3.00
RAG document Q&A	8,000 input / 800 output tokens per query, 20,000 queries per month	$180	$3.84
Coding assistant	12,000 input / 2,000 output tokens per request, 10,000 requests per month	$165	$3.20
Batch summarization	4,000 input / 400 output tokens per item, 200,000 items per month	$900	$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-3.8-flash	Google	$1.50	92	—
MAI-DS-R1Cheaper option	Microsoft	$0.18	90	-88%
deepseek-v4-proHigher quality	DeepSeek	$0.99	93	-34%
gemini-3.7-flashSame provider	Google	$1.50	90	+0%
qwen3.7-flashBest value overall	Alibaba	$0.055	82	-96%

Best for

ReasoningCodingMultimodalLong Context

Modalities

TextVisionAudio

Where it fits — and where it doesn't

Good fit

Coding: writing, refactoring and debugging across multi-file project context.
Reasoning: multi-step decomposition, planning and tasks that need rigorous inference.
Long documents: a 1049K window ingests a full contract, paper or large codebase in one pass.
High throughput and real-time UX: 302 tok/s keeps streamed responses feeling instant.
Multimodal input: handles images, screenshots and scanned documents directly.

Poor fit

No obvious disqualifiers — still benchmark on your own data before committing.

Frequently asked questions

How much does the gemini-3.8-flash API cost?

gemini-3.8-flash costs $0.75 per million input tokens and $3.75 per million output tokens. On a 3:1 input-to-output mix that blends to about $1.50 per million tokens.

What does gemini-3.8-flash cost per month?

It depends on volume. Using the four worked scenarios on this page: a support chatbot runs about $169/month, RAG document Q&A about $180/month, a coding assistant about $165/month, and batch summarization about $900/month. Plug your own token counts into the cost calculator for a tailored figure.

How large is the gemini-3.8-flash context window?

1049K tokens — roughly 1,399 pages of A4 text at ~750 tokens per page, ranking 9th of the 90 models tracked here.

gemini-3.8-flash or MAI-DS-R1 — which is better value?

MAI-DS-R1 blends to about $0.18 per million tokens versus $1.50 for gemini-3.8-flash — roughly 88% cheaper — at a quality score of 90 against 92. If that quality gap does not show up on your task, take the cheaper one; if it does, stay with gemini-3.8-flash. Benchmark both on your own data before deciding.

What is gemini-3.8-flash best suited for?

Tagged use cases: reasoning, coding, multimodal, long-context. Coding: writing, refactoring and debugging across multi-file project context. Less suitable: No obvious disqualifiers — still benchmark on your own data before committing.

How current is this gemini-3.8-flash pricing?

Pricing here was last verified on 2026-09-16 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.