Gemini · Fast / Budget · 1,048.576K context window

Gemini Omni Flash pricing for sentiment analysis

$0.001650 per call at 500 input / 100 output tokens.

Per call

$0.001650

10K calls/mo

$16.5

Cache savings

0% off input

Batch savings

50% off all

Calculate your Gemini Omni Flash costs

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

Configure your usage

500
100100K
100
5050K
10,000
1001M

Estimated cost

Monthly cost
$17
10,000 calls/month
Cost per call$0.001650
Daily cost$0.55
Input rate$1.50/MTok
Output rate$9.00/MTok
Analyze your real spend

Quick start

Call Gemini Omni Flash with the Google Generative AI SDK:

import google.generativeai as genai

model = genai.GenerativeModel("gemini-omni-flash")
response = model.generate_content("Your prompt here")
# Cost: ~$0.001650 per call at standard rates
# Input:  $1.50/MTok
# Output: $9.00/MTok

How Gemini Omni Flash compares on cost

Per-call cost for all models scoring 50+ on sentiment analysis benchmarks. Hover for details.

Cheapest viable option: GPT 5.6 Luna at $0.000220/call (87% less, 91/100 quality).

What drives sentiment analysis cost

Short text in, a label or score out: the cheapest shape an API call can take. Output pricing is irrelevant at this size. Because these run in bulk over historical data as often as in real time, the batch API discount typically applies, and the effective rate lands well below list.

For Gemini Omni Flash specifically, that means output tokens cost 6.0x more than input, at $9.00 vs $1.50 per MTok. At this use case’s 500/100 token split, output accounts for 55% of the $0.001650 per-call cost.

Complete Gemini Omni Flash pricing breakdown

Standard token rates

Direction$/MTokTokens per call
Input$1.50500
Output$9.00100

Prompt caching: 0% off input tokens

Caching drops input cost from $1.50 to $1.50/MTok. For repeated system prompts, that saves $0.0000 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$0.001650$16.5$16.5$0.0000
50,000 calls$0.001650$82.5$82.5$0.0000
100,000 calls$0.001650$165$165$0.0000
500,000 calls$0.001650$825$825$0.0000
1,000,000 calls$0.001650$1,650$1,650$0.0000

Gemini Omni Flash quality benchmarks for sentiment analysis

Benchmark data for sentiment analysis is pending for Gemini Omni Flash. Test against your own evaluation suite before moving to production.

Performance across all task types

Gemini Omni Flash scores by task type. Sentiment Analysis 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 sentiment analysis

12 models score 80+ on sentiment analysis benchmarks, sorted by input rate. Cheapest: GPT 5.6 Luna (87% less, 91/100 quality).

ModelProviderInput $/MTokOutput $/MTokQualityPer callvs Gemini
GPT 5.6 LunaGPT$0.20$1.2091/100$0.00022087% cheaper
Gemini 3.5 Flash-LiteGemini$0.30$2.5085/100$0.00040076% cheaper
Gemini 3 FlashGemini$0.50$3.0088/100$0.00055067% cheaper
Claude Haiku 4.5Claude$1.00$5.0090/100$0.00100039% cheaper
Gemini 3.5 FlashGemini$1.50$9.0092/100$0.001650same cost
Claude Sonnet 5Claude$2.00$10.0086/100$0.00200021% more
GPT 5.6 TerraGPT$2.00$12.0089/100$0.00220033% more
Gemini 3.1 ProGemini$2.00$12.0086/100$0.00220033% more
GPT 5.4GPT$2.50$15.0088/100$0.00275067% more
Claude Sonnet 4.6Claude$3.00$15.0084/100$0.00300082% more
GPT 5.6 SolGPT$4.00$20.0086/100$0.004000142% more
GPT 5.5GPT$5.00$30.0085/100$0.005500233% more

About Gemini Omni Flash

Provider

Gemini

Tier

Fast / Budget

Context

1,048.576K 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 Robotics ER 2Fast / Budget$1.00$5.00131.072K

You can compare Gemini Omni Flash 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 Omni Flash cost for sentiment analysis per API call?

A typical sentiment analysis call using Gemini Omni Flash costs about $0.001650 per call. This estimate uses 500 input tokens and 100 output tokens at standard rates. Standard rates are $1.50 per million input tokens and $9.00 per million output tokens.

Is Gemini Omni Flash the best model for sentiment analysis?

Our benchmark data does not yet include a sentiment analysis ranking for Gemini Omni Flash. We recommend testing it against your own evaluation criteria before production usage.

What is the cheapest model for sentiment analysis with good quality?

The most affordable model for sentiment analysis 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 91/100. Switching from Gemini Omni Flash to GPT 5.6 Luna saves about 87% per API call.

How can I reduce my Gemini Omni Flash costs for sentiment analysis?

Three strategies lower Gemini Omni Flash costs for this task type effectively. Prompt caching gives a 0% 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 $0.001650 to about $0.000825 per call.

How does Gemini Omni Flash pricing compare to other Gemini models?

Gemini has 15 other models alongside Gemini Omni Flash 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 Robotics ER 2 at $1.00/$5.00 per million tokens (fast / budget). The right choice depends on whether your sentiment analysis workload prioritizes quality, cost, or speed.

What context window does Gemini Omni Flash support?

Gemini Omni Flash supports a context window of 1,048.576K tokens per API call. This sets the maximum combined length of your input prompt and generated response. For sentiment analysis, typical calls use 500 input and 100 output tokens. That is well within this limit, leaving room for longer prompts or multi-turn conversations.

Track what you spend on AI

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