Gemini · Fast / Budget · 131.072K context window

Gemini Robotics ER 2 pricing for pdf summarization

$0.0225 per call at 15,000 input / 1,500 output tokens.

Per call

$0.0225

10K calls/mo

$225

Cache savings

90% off input

Batch savings

50% off all

Calculate your Gemini Robotics ER 2 costs

Adjust token counts, volume, and discount toggles to model your pdf summarization spend. Prices verified daily against provider pricing pages.

Configure your usage

15,000
100100K
1,500
5050K
10,000
1001M

Estimated cost

Monthly cost
$225
10,000 calls/month
Cost per call$0.0225
Daily cost$8
Input rate$1.00/MTok
Output rate$5.00/MTok
Analyze your real spend

Quick start

Call Gemini Robotics ER 2 with the Google Generative AI SDK:

import google.generativeai as genai

model = genai.GenerativeModel("gemini-robotics-er-2")
response = model.generate_content("Your prompt here")
# Cost: ~$0.0225 per call at standard rates
# Input:  $1.00/MTok
# Output: $5.00/MTok

How Gemini Robotics ER 2 compares on cost

Per-call cost for all models scoring 50+ on pdf summarization benchmarks. Hover for details.

Cheapest viable option: GPT 5.6 Luna at $0.004800/call (79% less, 84/100 quality).

What drives pdf summarization cost

PDFs are the worst case for input cost: extracted text carries page furniture, headers, and footnotes that inflate token counts well past what the content warrants. Cleaning the extraction before sending it often cuts more cost than changing models. Long PDFs also routinely cross long-context pricing thresholds, so chunking is both a quality and a cost decision.

For Gemini Robotics ER 2 specifically, that means output tokens cost 5.0x more than input, at $5.00 vs $1.00 per MTok. At this use case’s 15,000/1,500 token split, output accounts for 33% of the $0.0225 per-call cost.

Complete Gemini Robotics ER 2 pricing breakdown

Standard token rates

Direction$/MTokTokens per call
Input$1.0015,000
Output$5.001,500

Prompt caching: 90% off input tokens

Caching drops input cost from $1.00 to $0.10/MTok. For repeated system prompts, that saves $0.0135 per call.

Batch API: 50% off all tokens

Submit requests asynchronously and receive 50% off all token costs. This works best for offline pipelines where latency requirements are flexible.

Monthly cost projections

Monthly volumeCost per callStandard monthlyCached monthlyYou save with caching
10,000 calls$0.0225$225$90$135
50,000 calls$0.0225$1,125$450$675
100,000 calls$0.0225$2,250$900$1,350
500,000 calls$0.0225$11,250$4,500$6,750
1,000,000 calls$0.0225$22,500$9,000$13,500

Gemini Robotics ER 2 quality benchmarks for pdf summarization

Benchmark data for pdf summarization is pending for Gemini Robotics ER 2. Test against your own evaluation suite before moving to production.

Performance across all task types

Gemini Robotics ER 2 scores by task type. PDF Summarization highlighted.

Task typeConfidence scoreRank out of 72

Confidence scores are a third-party benchmark snapshot from week 29 of 2026. This snapshot is 10 weeks old, so treat the scores as directional and validate against your own evals.

Alternative models for pdf summarization

12 models score 80+ on pdf summarization benchmarks, sorted by input rate. Cheapest: GPT 5.6 Luna (79% less, 84/100 quality).

ModelProviderInput $/MTokOutput $/MTokQualityPer callvs Gemini
GPT 5.6 LunaGPT$0.20$1.2084/100$0.00480079% cheaper
Gemini 3 FlashGemini$0.50$3.0083/100$0.012047% cheaper
Gemini 3.5 FlashGemini$1.50$9.0085/100$0.036060% more
Claude Sonnet 5Claude$2.00$10.0091/100$0.0450100% more
GPT 5.6 TerraGPT$2.00$12.0092/100$0.0480113% more
Gemini 3.1 ProGemini$2.00$12.0087/100$0.0480113% more
GPT 5.4GPT$2.50$15.0091/100$0.0600167% more
Claude Sonnet 4.6Claude$3.00$15.0090/100$0.0675200% more
GPT 5.6 SolGPT$4.00$20.0090/100$0.0900300% more
Claude Opus 4.8Claude$5.00$25.0088/100$0.1125400% more
GPT 5.5GPT$5.00$30.0089/100$0.1200433% more
Claude Fable 5Claude$10.00$50.0090/100$0.2250900% more

About Gemini Robotics ER 2

Provider

Gemini

Tier

Fast / Budget

Context

131.072K tokens

Released

Not published

Backfilled on 2026-09-23. Prices confirmed by the provider pricing page and the LiteLLM cross-check. Release date not published in a machine-readable source. Not yet benchmarked, so never recommended.

Other models from Gemini

ModelTierInput $/MTokOutput $/MTokContext
Gemini 3.1 ProMid-tier$2.00$12.001,048.576K
Gemini 3.5 FlashFast / Budget$1.50$9.001,048.576K
Gemini 3 FlashFast / Budget$0.50$3.001,048.576K
Gemini 3.5 Flash-LiteFast / Budget$0.30$2.501,048.576K
Gemini 2.5 Computer UseFast / Budget$1.25$10.00128K
Gemini 2.5 FlashFast / Budget$0.30$2.501,048.576K
Gemini 2.5 Flash-LiteFast / Budget$0.10$0.401,048.576K
Gemini 2.5 ProMid-tier$1.25$10.001,048.576K
Gemini 3.1 Flash-LiteFast / Budget$0.25$1.501,048.576K
Gemini 3.1 Pro Custom ToolsMid-tier$2.00$12.001,048.576K
Gemini 3.6 FlashFast / Budget$0.75$3.751,048.576K
Gemini 3.7 FlashFast / Budget$0.75$3.751,048.576K
Gemini 3.8 FlashFast / Budget$0.75$3.751,048.576K
Gemini Omni 1.1 FlashFast / Budget$1.50$9.001,048.576K
Gemini Omni FlashFast / Budget$1.50$9.001,048.576K

You can compare Gemini Robotics ER 2 head-to-head against any other model on our model comparison pages, or track how pricing has changed over time on the pricing history tracker.

Frequently asked questions

How much does Gemini Robotics ER 2 cost for pdf summarization per API call?

A typical pdf summarization call using Gemini Robotics ER 2 costs about $0.0225 per call. This estimate uses 15,000 input tokens and 1,500 output tokens at standard rates. Standard rates are $1.00 per million input tokens and $5.00 per million output tokens.

Is Gemini Robotics ER 2 the best model for pdf summarization?

Our benchmark data does not yet include a pdf summarization ranking for Gemini Robotics ER 2. We recommend testing it against your own evaluation criteria before production usage.

What is the cheapest model for pdf summarization with good quality?

The most affordable model for pdf summarization above 80/100 quality is GPT 5.6 Luna. It comes from GPT at $0.20 per million input tokens. Output costs $1.20 per million tokens with a quality score of 84/100. Switching from Gemini Robotics ER 2 to GPT 5.6 Luna saves about 79% per API call.

How can I reduce my Gemini Robotics ER 2 costs for pdf summarization?

Three strategies lower Gemini Robotics ER 2 costs for this task type effectively. Prompt caching gives a 90% discount on input tokens when reusing system prompts. The batch API gives 50% off all tokens for asynchronous processing workloads. Prompt optimization reduces token counts through more concise instructions and context windows. Combining caching with batching brings your cost from $0.0225 to about $0.001125 per call.

How does Gemini Robotics ER 2 pricing compare to other Gemini models?

Gemini has 15 other models alongside Gemini Robotics ER 2 today: Gemini 3.1 Pro at $2.00/$12.00 per million tokens (mid-tier), Gemini 3.5 Flash at $1.50/$9.00 per million tokens (fast / budget), Gemini 3 Flash at $0.50/$3.00 per million tokens (fast / budget), Gemini 3.5 Flash-Lite at $0.30/$2.50 per million tokens (fast / budget), Gemini 2.5 Computer Use at $1.25/$10.00 per million tokens (fast / budget), Gemini 2.5 Flash at $0.30/$2.50 per million tokens (fast / budget), Gemini 2.5 Flash-Lite at $0.10/$0.40 per million tokens (fast / budget), Gemini 2.5 Pro at $1.25/$10.00 per million tokens (mid-tier), Gemini 3.1 Flash-Lite at $0.25/$1.50 per million tokens (fast / budget), Gemini 3.1 Pro Custom Tools at $2.00/$12.00 per million tokens (mid-tier), Gemini 3.6 Flash at $0.75/$3.75 per million tokens (fast / budget), Gemini 3.7 Flash at $0.75/$3.75 per million tokens (fast / budget), Gemini 3.8 Flash at $0.75/$3.75 per million tokens (fast / budget), Gemini Omni 1.1 Flash at $1.50/$9.00 per million tokens (fast / budget), Gemini Omni Flash at $1.50/$9.00 per million tokens (fast / budget). The right choice depends on whether your pdf summarization workload prioritizes quality, cost, or speed.

What context window does Gemini Robotics ER 2 support?

Gemini Robotics ER 2 supports a context window of 131.072K tokens per API call. This sets the maximum combined length of your input prompt and generated response. For pdf summarization, typical calls use 15,000 input and 1,500 output tokens. That is well within this limit, leaving room for longer prompts or multi-turn conversations.

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