The best cheap model for long-document pipelines, but don't expect flagship-level reasoning.
58
Coding
55
Writing
52
Research
20
Images
93
Value
88
Long Context
Use this when
High-volume, latency-sensitive applications like document triage, chatbot pipelines, and content classification at scale.
Strengths
Extremely low cost at $0.10/$0.40 per 1M tokens — cheaper than GPT-4.1 Mini and competitive with Claude Haiku 3.5
Massive 1M token context window is rare at this price tier, enabling long-document processing on a budget
Fast inference makes it practical for real-time applications and user-facing chatbots
Solid baseline reasoning inherited from the Gemini 2.5 architecture, outperforming older budget models like Gemini 1.5 Flash
Weaknesses
Noticeably weaker on complex multi-step reasoning and nuanced instruction-following compared to Gemini 2.5 Flash or Pro
Monthly cost estimate
See what Google: Gemini 2.5 Flash Lite actually costs at your usage level
Input tokens / month1M
10k50M
Output tokens / month500k
10k25M
Input cost
$0.100
Output cost
$0.200
Total / month
$0.300
Based on Google: Gemini 2.5 Flash Lite API pricing: $0.09999999999999999/1M input · $0.39999999999999997/1M output. Real costs vary by provider discounts and caching. Check the provider for exact current rates.
Price History
Google: Gemini 2.5 Flash Lite pricing over time
→0% since May 9
4 data points · tracked daily since May 9, 2026
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Recommendations are made independently based on real-world use and public benchmarks. See our disclosures for details.
Compare alternatives
Similar models worth checking before you commit.
GoogleBudget
Gemma 4 26B A4B
Gemma 4 26B A4B is a sparse mixture-of-experts open model from Google, activating only ~4B parameters per forward pass despite having 26B total parameters. It offers a 262K context window at budget pricing, making it one of the more capable open-weight models for its cost tier.
Verdict
A lean, fast, and surprisingly capable budget model best suited for high-volume text tasks where cost efficiency trumps peak quality.
Quality score
59%
Pricing
$0.13/1M in
$0.40/1M out
Speed
Change history
Pricing moves, ranking shifts, and capability updates.
New ModelMar 27, 2026
Google: Gemini 2.5 Flash Lite — added to UseRightAI
Google: Gemini 2.5 Flash Lite (Google) is now indexed. The best cheap model for long-document pipelines, but don't expect flagship-level reasoning.
Google: Gemini 2.5 Flash Lite is best for high-volume, latency-sensitive applications like document triage, chatbot pipelines, and content classification at scale.. It is a strong fit when that workflow matters more than the tradeoffs around budget pricing and very fast speed.
When should I avoid Google: Gemini 2.5 Flash Lite?
You need high-quality code generation, complex reasoning chains, or nuanced long-form writing — upgrade to Gemini 2.5 Flash or Pro instead.
What is a cheaper alternative to Google: Gemini 2.5 Flash Lite?
Meta: Llama 3.1 8B Instruct is the lower-cost option to compare first when you want a similar workflow fit with less token spend.
What is a faster alternative to Google: Gemini 2.5 Flash Lite?
Gemma 4 26B A4B is the better pick when response time matters more than maximum depth or premium quality.
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Skip this if
You need high-quality code generation, complex reasoning chains, or nuanced long-form writing — upgrade to Gemini 2.5 Flash or Pro instead.
Code generation quality lags behind GPT-4.1 Mini and Claude Haiku 3.5 on harder algorithmic problems
Not suitable for tasks requiring deep analysis, synthesis, or creative depth
Fast
Best for cost-sensitive applications needing long-context processing with reasonable quality, such as document summarization pipelines or lightweight coding assistants.
Context
262k tokens
As an open-weight model, Gemma 4 26B can also be self-hosted, making API pricing largely irrelevant at scale. The 'A4B' suffix denotes the active parameter count in its MoE configuration. Listed as superseding Gemini 3 Flash Preview, though Gemini 2.0 Flash remains a stronger hosted alternative.
Open-weightBudgetMoELong ContextGoogle
Best for
Cost-sensitive applications needing long-context processing with reasonable quality, such as document summarization pipelines or lightweight coding assistants.
Gemma 4 31B is Google's open-weight instruction-tuned model offering a strong balance of capability and cost efficiency at just $0.14/$0.40 per million tokens. It features a 262K context window and is designed for developers who need capable on-premise or API-hosted inference without flagship pricing.
Verdict
A well-priced, long-context open-weight model that's ideal for high-volume developer workloads but won't match frontier models on complex reasoning.
Quality score
66%
Pricing
$0.14/1M in
$0.40/1M out
Speed
Fast
Best for cost-conscious developers needing a capable open-weight model for coding assistance, summarization, and document analysis at scale.
Context
262k tokens
As an open-weight model, Gemma 4 31B can be self-hosted via Ollama or Hugging Face in addition to Google's API. Pricing shown is for hosted inference. No image input capability confirmed at launch.
Open WeightBudgetLong ContextCodingSelf-Hostable
Best for
Cost-conscious developers needing a capable open-weight model for coding assistance, summarization, and document analysis at scale.
Gemini 2.0 Flash Lite is Google's ultra-budget, high-speed model designed for high-volume, cost-sensitive applications. It sits below Gemini 2.0 Flash in capability but offers the lowest price point in the Gemini 2.0 family with a massive 1M token context window.
Verdict
The go-to model when cost and throughput are everything and task complexity is low.
Quality score
57%
Pricing
$0.07/1M in
$0.30/1M out
Speed
Very fast
Best for high-throughput, cost-sensitive pipelines where speed and price matter more than top-tier reasoning quality.
Context
1.0M tokens
Pricing is among the lowest available in any major provider's lineup as of mid-2025. Context window of 1M tokens is a significant differentiator at this price tier. Check Google AI Studio and Vertex AI for rate limits on high-volume usage.
