What are the strengths of this model?

Optimized for speed and efficiency in inference. Good balance of performance and reduced computational overhead. Effective for a broad range of general language tasks.

What are the weaknesses of this model?

May not match the peak accuracy of larger, more complex models. Capabilities in highly specialized or nuanced domains could be limited. Performance scales with efficient resource allocation.

What are the best use cases?

High-volume, latency-sensitive applications like chatbots. Quick prototyping and development of AI-powered features. Efficient processing of large text datasets for summarization or extraction.

Google Deep MindProprietary

Gemini 3.5 Flash-Lite

Name: Gemini 3.5 Flash-Lite
Author: Google Deep Mind

Compare this model

Gemini 3.5 Flash-Lite is Google DeepMind's cheapest Gemini 3 model, released July 21, 2026 for high-volume, latency-sensitive workloads like translation, classification, and tagging. It serves a 1,000,000-token multimodal context (text, image, audio, video, PDF) at just $0.30 per million input and $2.50 per million output tokens. It beats Gemini 3.1 Flash-Lite on agentic coding (SWE-Bench Pro 54.2%), computer use (OSWorld-Verified 74.0%), and terminal tasks (Terminal-Bench 2.1 54%), and streams at roughly 350 tokens per second on the Artificial Analysis Index.

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Parameters

Undisclosed

Context Window

License

Proprietary

Release Date

2026-07-21

API Pricing

Input Price (per 1M tokens)

$0.3

Output Price (per 1M tokens)

$2.5

Billing Mode: standard

Strengths

•Optimized for speed and efficiency in inference.
•Good balance of performance and reduced computational overhead.
•Effective for a broad range of general language tasks.

Weaknesses

•May not match the peak accuracy of larger, more complex models.
•Capabilities in highly specialized or nuanced domains could be limited.
•Performance scales with efficient resource allocation.

Use Cases

•High-volume, latency-sensitive applications like chatbots.
•Quick prototyping and development of AI-powered features.
•Efficient processing of large text datasets for summarization or extraction.

Deep Analysis

Context Window

1,048,576 tokens

1M multimodal (text, image, audio, video, PDF)

Artificial Analysis Intelligence Index

#12 of 152 models; top of its class

Input Price

$0.30 / 1M tokens

Output: $2.50 / 1M; batch 50% off

Throughput

~490 tok/s

2nd-fastest measured on Artificial Analysis

SWE-Bench Pro

54.2%

vs Gemini 3.1 Flash-Lite 38%

Terminal-Bench 2.1

54.0%

vs Gemini 3.1 Flash-Lite 31%

OSWorld-Verified

74.0%

computer use; ~2x predecessor

Strengths

・Cheapest paid Gemini 3 tier at $0.30/$2.50 per 1M tokens, undercutting GPT-5.4 mini and Claude Haiku 4.5 on input price
・1M-token multimodal context with thinking, function calling, structured outputs, code execution and search grounding
・Near-top speed (~490 tok/s) makes it ideal for high-volume, latency-sensitive production traffic

Weaknesses

・Loses to GPT-5.4 mini on sustained multi-step reasoning (Terminal-Bench 2.1, GDPval-AA v2)
・No computer use, image/audio generation, or Live API — a text-in/text-out engine for volume
・Time-to-first-token around 6s (includes thinking), so better suited to background jobs than snappy live chat

Competitor Comparison

Model	Arena	SWE	GPQA	Price
Gemini 3.5 Flash-Lite	N/A	54.2%	N/A	$0.30 / $2.50 per 1M
GPT-5.4 mini	N/A	N/A	N/A	$0.75 / $4.50 per 1M
Claude Haiku 4.5	N/A	N/A	N/A	$1.00 / $5.00 per 1M
Gemini 3.1 Flash-Lite	N/A	38%	N/A	prior budget tier

Overview

Gemini 3.5 Flash-Lite is Google DeepMind's cheapest Gemini 3 model, released July 21, 2026 alongside Gemini 3.6 Flash for high-volume, latency-sensitive workloads such as translation, classification, and tagging. It carries a 1,048,576-token multimodal context (text, image, audio, video, PDF) at just $0.30 per million input and $2.50 per million output tokens, with a flat 50% discount in batch mode for non-real-time jobs. Google positions Flash-Lite as the layer beneath Gemini 3.6 Flash: 3.6 Flash for complex agent work, 3.5 Flash-Lite for the high-volume, latency-sensitive jobs that make up most production traffic — agentic search, document processing, translation, classification. On Artificial Analysis it lands an Intelligence Index of 36 (#12 of 152 models, well above the class average) while running at roughly 490 output tokens per second, the second-fastest model the service has measured. The generation jump over its March 2026 preview predecessor, Gemini 3.1 Flash-Lite, is the headline: SWE-Bench Pro nearly closes a 16-point gap (54.2% vs 38%), OSWorld-Verified climbs almost 20 points to 74.0%, and Terminal-Bench 2.1 doubles to 54.0%.

Benchmarks & Performance

Gemini 3.5 Flash-Lite demonstrates strong, cost-focused performance across agentic and multimodal benchmarks, with most figures drawn from Google DeepMind's official Gemini 3.5 Flash-Lite model card. Treat cross-vendor columns as directional rather than neutral. | Benchmark | Gemini 3.5 Flash-Lite | GPT-5.4 mini | Claude Haiku 4.5 | | :--- | :--- | :--- | :--- | | **SWE-Bench Pro (Public)** | **54.2%** | higher | lower | | **Terminal-Bench 2.1** | 54.0% | higher | lower | | **MLE-Bench** | 39.2% | — | — | | **GDPval-AA v2 (Elo)** | 1140 | higher | higher | | **OSWorld-Verified** | **74.0%** | lower | lower | | **GDM-MRCR v2 (128K, avg)** | 72.2% | — | — | | **GDM-MRCR v2 (1M, pointwise)** | 21.3% | — | — | **Key takeaways:** * **Generational leap:** vs Gemini 3.1 Flash-Lite, SWE-Bench Pro nearly closes a 16-point gap, OSWorld-Verified climbs ~20 points, and knowledge work (GDPval-AA v2) jumps from 642 to 1140. * **Price leadership:** At $0.30 / $2.50 it undercuts GPT-5.4 mini ($0.75 / $4.50) and Claude Haiku 4.5 ($1.00 / $5.00) while sitting in the same rough quality band on most agentic benchmarks. * **Reasoning ceiling:** GPT-5.4 mini still wins on Terminal-Bench 2.1 and GDPval-AA v2, the benchmarks that reward sustained multi-step reasoning. * **Speed:** ~490 tok/s (2nd-fastest on Artificial Analysis); time-to-first-token ~6s including thinking, so better for background jobs than snappy live chat.

Detailed Comparison

**vs. GPT-5.4 mini (OpenAI):** Flash-Lite undercuts it on price ($0.30/$2.50 vs $0.75/$4.50) and wins on computer use (OSWorld-Verified) and long-context recall (GDM-MRCR v2 128K), but loses on Terminal-Bench 2.1 and knowledge-work Elo where GPT-5.4 mini's stronger reasoning shows. **vs. Claude Haiku 4.5 (Anthropic):** Flash-Lite is ~3x cheaper on input and beats it on OSWorld-Verified and long-context retrieval, while Haiku holds a small edge on raw coding and reasoning scores. **vs. Gemini 3.1 Flash-Lite (predecessor):** The generation jump is clean — SWE-Bench Pro 54.2% vs 38%, OSWorld-Verified 74.0% vs ~54%, Terminal-Bench 2.1 54.0% vs 31%. Flash-Lite is the current cheapest stable option in the Gemini 3 family for teams migrating off Gemini 2.5 Flash-Lite (shutdown Oct 16, 2026). **vs. Gemini 3.6 Flash (sibling):** 3.6 Flash is for complex agentic work; 3.5 Flash-Lite is the high-volume, latency-sensitive workhorse. Google routes the two as a tier, not competitors.

Community Feedback

Developer reaction centers on its price-to-performance for volume work. Early adopters cited by Google include Ashler, Palo Alto Networks, and Ramp, all using it to scale agentic pipelines where per-call cost compounds. It is already rolling out inside Google Search's AI Mode. The main caveats raised in community discussions are the ~6s time-to-first-token (includes thinking), the lack of computer use / image-audio generation / Live API, and that the free tier trains on user content while paid tiers do not. The 50% batch discount ($0.15 / $1.25 per 1M) is widely noted as the right lever for overnight document processing and bulk translation.

Use Cases

1. **High-Volume Document Processing & Classification:** At $0.30/$2.50 with a 1M-token context, Flash-Lite is built for ingesting large PDFs, contracts, and support tickets at scale — e.g., first-pass triage on 50,000 tickets/month runs about $40 in raw token cost versus ~$150 on 3.6 Flash. 2. **Bulk Translation & Localization:** Multilingual translation across text, image, audio, and video inputs with batch mode at half price; ideal for overnight localization pipelines where latency is not the constraint. 3. **Agentic Search & Subagent Tooling:** As the layer beneath 3.6 Flash, it handles the millions of simple, repeated calls (retrieval, tagging, routing) that make up most production traffic, keeping per-call cost low while freeing the frontier model for hard cases. 4. **Long-Context Retrieval & Summarization:** GDM-MRCR v2 128K avg 72.2% makes it strong for needle-in-haystack recall over long documents, useful for RAG sidecars and meeting-note summarization.

Latest News

**Release (July 21, 2026):** Google shipped Gemini 3.5 Flash-Lite alongside Gemini 3.6 Flash and a gated Gemini 3.5 Flash Cyber security variant. Flash-Lite is the budget tier: $0.30/$2.50 per 1M tokens, undercutting GPT-5.4 mini and Claude Haiku 4.5 on input price. **Benchmarks & Positioning:** Google's model card shows SWE-Bench Pro 54.2%, Terminal-Bench 2.1 54.0%, OSWorld-Verified 74.0%, MLE-Bench 39.2%, GDPval-AA v2 Elo 1140. Intelligence Index 36 (#12 of 152) at ~490 tok/s. **Deployment:** Rolling out inside Google Search's AI Mode; named early adopters Ashler, Palo Alto Networks, and Ramp. Google's deprecation schedule still lists Gemini 3.1 Flash-Lite (not 3.5) as the migration target for Gemini 2.5 Flash-Lite, which shuts down October 16, 2026. **Pricing detail:** Standard $0.30/$2.50; batch and Flex $0.15/$1.25; Priority $0.54/$4.50; free tier trains on user content, paid tiers do not. Search grounding metered separately ($14 per 1,000 queries after 5,000 free/month).

Google positions Flash-Lite as the layer beneath Gemini 3.6 Flash: 3.6 Flash for complex agent work, 3.5 Flash-Lite for the high-volume, latency-sensitive jobs that make up most production traffic — agentic search, document processing, translation, classification. On Artificial Analysis it lands an Intelligence Index of 36 (#12 of 152 models, well above the class average) while running at roughly 490 output tokens per second, the second-fastest model the service has measured. The generation jump over its March 2026 preview predecessor, Gemini 3.1 Flash-Lite, is the headline: SWE-Bench Pro nearly closes a 16-point gap (54.2% vs 38%), OSWorld-Verified climbs almost 20 points to 74.0%, and Terminal-Bench 2.1 doubles to 54.0%.

Sources

Analysis generated: 2026-09-02