A capable open-weight budget model hamstrung by a frustratingly small context window.
58
Coding
62
Writing
48
Research
0
Images
88
Value
18
Long Context
Use this when
Lightweight text tasks, classification, and summarization where cost matters more than frontier-level quality.
Strengths
Extremely low cost at $0.03/$0.09 per 1M tokens — cheaper than most comparable small models
Strong instruction-following for its parameter count, competitive with Llama 3 8B and Mistral 7B
Open weights allow self-hosting and fine-tuning for specialized use cases
Reliable for structured output tasks like classification, extraction, and summarization
Weaknesses
8,192 token context window is restrictive — cannot handle long documents or extended conversations
Monthly cost estimate
See what Google: Gemma 2 9B actually costs at your usage level
Input tokens / month1M
10k50M
Output tokens / month500k
10k25M
Input cost
$0.030
Output cost
$0.045
Total / month
$0.075
Based on Google: Gemma 2 9B API pricing: $0.03/1M input · $0.09/1M output. Real costs vary by provider discounts and caching. Check the provider for exact current rates.
Price History
Google: Gemma 2 9B pricing over time
→0% since May 9
4 data points · tracked daily since May 9, 2026
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Lightweight text tasks, classification, and summarization where cost matters more than frontier-level quality.. Start free — no card required.
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: Gemma 2 9B — added to UseRightAI
Google: Gemma 2 9B (Google) is now indexed. A capable open-weight budget model hamstrung by a frustratingly small context window.
Google: Gemma 2 9B is best for lightweight text tasks, classification, and summarization where cost matters more than frontier-level quality.. 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: Gemma 2 9B?
You need to process documents longer than a few pages, require strong reasoning, or need multimodal (image/audio) inputs.
What is a cheaper alternative to Google: Gemma 2 9B?
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: Gemma 2 9B?
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 to process documents longer than a few pages, require strong reasoning, or need multimodal (image/audio) inputs.
Noticeably behind GPT-4o mini and Claude Haiku 3.5 on complex reasoning and multi-step coding tasks
No multimodal capabilities — text-only input and output
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.
Claude 3.5 Haiku is Anthropic's fastest and most affordable model in the Claude 3.5 family, designed for high-throughput tasks requiring quick responses without sacrificing Claude's core instruction-following quality. It handles a massive 200K context window while maintaining speed suitable for production pipelines.
Verdict
The fastest way to get Claude's quality in production — just don't confuse 'fast' with 'cheap'.
Quality score
64%
Pricing
$0.80/1M in
$4.00/1M out
Speed
Very fast
Best for high-volume, latency-sensitive applications like chatbots, classification, data extraction, and agentic tool use where speed and cost matter more than peak reasoning depth.
Context
200k tokens
Output cost of $4/1M is notably higher than competing fast/mini models. Input cost at ~$0.80/1M is competitive. Best value emerges in input-heavy pipelines like document classification or RAG retrieval where output tokens are minimal.
High-volume, latency-sensitive applications like chatbots, classification, data extraction, and agentic tool use where speed and cost matter more than peak reasoning depth.
