How much does Gemini 2.5 Pro cost per million tokens?

Gemini 2.5 Pro costs $1.25 per million input tokens and $10 per million output tokens on the paid tier for prompts of 200K tokens or fewer. Prompts longer than 200K tokens double the input rate to $2.50 per million and raise the output rate to $15 per million. Context caching costs $0.125 per million cached input tokens at short context and $0.25 at long context. Storage is billed separately at $4.50 per million tokens per hour (Google AI pricing page, September 2026).

How much does Gemini 2.5 Flash cost?

Gemini 2.5 Flash costs $0.30 per million input tokens (text, image, video) and $2.50 per million output tokens. Audio input is priced separately at $1 per million input tokens. Context caching is $0.03 per million cached input tokens for text/image/video and $0.10 for audio. Storage costs $1 per million tokens per hour. The 2.5 Flash tier is the price-performance sweet spot in the Gemini family — 4x cheaper on input than 2.5 Pro and 3x cheaper on output (Google AI pricing page, September 2026).

What is Gemini 3.1 Flash-Lite?

Gemini 3.1 Flash-Lite is Google's lowest-cost Gemini model as of September 2026, priced at $0.25 per million input tokens and $1.50 per million output tokens. Cached inputs are $0.025 per million tokens, the cheapest cache rate in the Gemini family. The model supports a 1M-token context window. Flash-Lite is designed for high-volume classification, routing, and extraction workloads where accuracy matters less than cost per request (Google AI pricing page, September 2026).

What is the 200K context threshold for Gemini Pro?

Gemini 2.5 Pro prices the entire request at the long-context rate when input tokens exceed 200,000. The input rate doubles from $1.25 to $2.50 per million and the output rate rises from $10 to $15 per million. The 200K threshold is a pricing boundary, not the model's context-window limit — Gemini 2.5 Pro supports up to 2 million tokens of context. Long-document RAG workflows must chunk requests under 200K to avoid the long-context surcharge (Google AI pricing page, September 2026).

What is the difference between Gemini API and Vertex AI pricing?

Standard token rates are identical between the Gemini Developer API and Google Vertex AI as of September 2026. The differences are in the deployment, billing, and enterprise features: Vertex AI bills into a Google Cloud project, supports VPC Service Controls, customer-managed encryption keys, and data residency in 12+ regions. The Gemini Developer API is simpler to set up and includes a free tier, but lacks the enterprise governance that production deployments typically need (Google AI pricing page, September 2026).

Does Gemini offer Batch API pricing?

Yes. Gemini 2.5 Pro Batch runs at $0.625 input / $5 output per million tokens at short context — 50% off the standard rate. Gemini 2.5 Flash Batch is $0.15 input / $1.25 output per million tokens, also 50% off. Cached inputs in Batch are $0.015-$0.07 per million tokens depending on the model. Batch returns results within 24 hours and is the cheapest Gemini route for offline RAG ingestion, bulk classification, and document summarisation (Vertex AI pricing, September 2026).

Gemini API Pricing 2026: Cost Per Million Tokens

Q: How much does Gemini grounding cost?

Gemini grounding with Google Search is free for the first 1,500 grounded prompts per day on paid Gemini API access, then $35 per 1,000 additional grounded prompts. Grounding with Google Maps is free for the first 10,000 grounded prompts per day, then $25 per 1,000 additional grounded prompts. Vertex AI charges $2.50 per 1,000 custom-data grounding requests. The free tier is shared between Flash and Flash-Lite and reset daily (Google AI pricing page, September 2026).

Quick Answer

Gemini API pricing 2026: Gemini 2.5 Pro $1.25 input / $10 output per million tokens at short context (≤200K); $2.50/$15 long-context past 200K. Gemini 2.5 Flash $0.30/$2.50 with $0.03 cache reads. Gemini 3.1 Flash-Lite $0.25/$1.50 with $0.025 cache reads. Grounding Google Search $35 per 1K prompts past 1,500 free RPD.

Last verified: Sep 16, 2026.

At a glance

Gemini 2.5 Pro: $1.25 input / $10 output per 1M tokens (≤200K); $2.50/$15 (>200K)
Gemini 2.5 Flash: $0.30 input / $2.50 output per 1M tokens; $0.03 cache reads
Gemini 3.1 Flash-Lite: $0.25 input / $1.50 output per 1M tokens; $0.025 cache reads
Audio input surcharge: 2.5 Flash $1/MTok audio (vs $0.30 text)
Cache storage: $4.50/MTok/hour (Pro), $1.00/MTok/hour (Flash)
Grounding Google Search: 1,500 RPD free then $35/1K prompts
Grounding Google Maps: 10,000 RPD free then $25/1K prompts
Batch API: 50% off standard rates; 24-hour turnaround

Three pricing tiers in the Gemini family

Google prices Gemini by capability tier — Pro for deep reasoning, Flash for price-performance, Flash-Lite for high-volume low-cost workloads.

Gemini 2.5 Pro at $1.25 input / $10 output per million tokens is the mid-tier reasoning model with a 2M-token context window. The long-context surcharge doubles input to $2.50 and raises output to $15 once a request exceeds 200K tokens. Gemini 2.5 Flash at $0.30 input / $2.50 output is 4x cheaper on input and 3x cheaper on output than Pro, with a 1M-token context window. Gemini 3.1 Flash-Lite at $0.25 input / $1.50 output is the cheapest option in the family (Google AI pricing page, September 2026).

The gap between Pro and Flash-Lite is 5x on input and 6.7x on output. For most production workloads, Flash hits the best price-performance ratio. Flash-Lite is justified only when the accuracy gap matters less than cost per request — for example, bulk classification, intent detection, or routing models that sit in front of a more expensive model (Google AI pricing page, September 2026).

The 200K long-context cliff

Gemini 2.5 Pro prices the entire request at the long-context rate when input tokens exceed 200,000 — not just the portion above the threshold.

Standard input doubles from $1.25 to $2.50 per million tokens and output rises from $10 to $15 per million tokens for the whole request once the threshold is crossed. The 200K threshold is a pricing boundary — Gemini 2.5 Pro's actual context window is 2 million tokens. A request at 201,000 input tokens costs roughly twice what it would at 199,000, even though only 2,000 tokens moved (Google AI pricing page, September 2026).

This is the single biggest cost cliff in the Gemini catalogue and the reason most production systems are designed around the 200K threshold. For long-document RAG workflows, it is cheaper to chunk and run two separate requests than to combine the full document into one that trips the threshold. Flash-Lite does not have a long-context surcharge — it stays at $0.25/$1.50 regardless of context length up to its 1M-token limit (Google AI pricing page, September 2026).

Prompt caching at 90% off

Gemini context caching is the cheapest in the family at $0.025-$0.125 per million cached tokens, depending on model and context length.

Gemini 2.5 Pro cached input is $0.125 per million tokens at short context and $0.25 at long context — a 90% discount off the standard input rate. Gemini 2.5 Flash cached input is $0.03 per million tokens for text, image, and video workloads and $0.10 for audio. Gemini 3.1 Flash-Lite is the cheapest at $0.025 per million cached tokens (Google AI pricing page, September 2026).

Storage is billed separately at $4.50 per million tokens per hour for Pro and $1 per million tokens per hour for Flash. For a 100,000-token system prompt cached for an hour, that adds $0.45 (Pro) or $0.10 (Flash) to the bill regardless of cache hit volume. The economics work best for high-reuse prefixes (10x+ per hour); below that, the storage cost dominates the cache-read savings (Google AI pricing page, September 2026).

Grounding: free tier and per-prompt costs

Grounding with Google Search and Maps has a generous free tier, then charges per grounded prompt above it.

Google Search grounding on Gemini API is free for the first 1,500 grounded prompts per day on paid Gemini API access, then $35 per 1,000 additional grounded prompts. Google Maps grounding is free for the first 10,000 grounded prompts per day, then $25 per 1,000 additional grounded prompts. The free tier is shared between Flash and Flash-Lite and resets daily (Google AI pricing page, September 2026).

Vertex AI charges $2.50 per 1,000 requests for custom-data grounding — significantly more than the $35 per 1,000 standard Search grounding. Custom-data grounding is required when the model needs to query a private corpus (a Vertex AI Search index, BigQuery table, or other Google Cloud data source). For pure Google Search grounding, the Gemini Developer API is cheaper than Vertex AI (Google AI pricing page, September 2026).

What enterprise buyers should do next

Three actions for organisations evaluating Gemini API pricing in 2026.

Set up a guardrail at the 200K threshold. Either chunk requests under the threshold or accept the long-context rate. Most production systems need explicit code to enforce this — Gemini 2.5 Pro's 2M context window tempts teams to combine documents that would have been cheaper split.
Audit prompt-caching strategy. A 100K-token system prompt reused 10x per hour costs $0.45/hour on Pro and $0.10/hour on Flash in storage. Below 10x reuse, the storage cost outweighs the cache-read savings. Tune storage TTL to actual reuse patterns.
Plan grounding volume. Search grounding is essentially free for low-volume apps (1,500 RPD covers most production RAG pipelines). At higher volumes, $35 per 1K grounded prompts adds up — for a 100,000-prompt/day RAG system, grounding adds $3,500/month.

What to watch next

Three near-term datapoints. First, the Gemini 3.0 Pro general availability — Google published Gemini 3.0 Pro pricing at $1.25/MTok in Q2 2026 but has not yet confirmed GA for the third-generation model. Second, Vertex AI's introduction of separate fine-tuning pricing for Gemini 3.x — fine-tuning is currently bundled into per-token training costs that are not always published. Third, the rate of change on the Flash-Lite line — Google typically refreshes Flash-Lite every 6-9 months; a Flash-Lite 3.5 or 4.0 is plausible for Q1 2027 with a new price point.

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Written by

Fazlur RahmanAI, Entrepreneurship, EdTech

Fazlur Rahman is the founder of Tutorsbot, building AI-powered tools for learning and career growth. He writes about applying AI in real products and the practi… Read more

Fazlur Rahman is the founder of Tutorsbot, building AI-powered tools for learning and career growth. He writes about applying AI in real products and the practical side of building an ed-tech startup.