How much does Gemma 2 9B cost per million tokens?
As of August 30, 2026, Gemma 2 9B Gemini API pricing is $0.20 per million input tokens and $0.20 per million output tokens via Groq. Input tokens are the prompt you send; output tokens are the completion. Long answers therefore cost more than short ones even when the prompt stays the same. Source: https://ai.google.dev/gemini-api/docs/pricing
What is the blended API cost for Gemma 2 9B?
On this page the blended Gemma 2 9B API cost is $0.40 for 1 million input tokens plus 1 million output tokens. That 1:1 mix is the comparison scale used across the LLM pricing table. A 3:1 mix (three million input tokens per one million output tokens), which some calculators use, would be about $0.80.
How much does a typical Gemma 2 9B request cost?
A typical Gemma 2 9B API request with 100K input tokens and 20K output tokens costs about $0.02 at current rates as of August 30, 2026. Multiply that by daily or monthly volume to estimate spend. Caching, batch APIs, and volume discounts from Groq can bring the real invoice lower than the public list rate at https://ai.google.dev/gemini-api/docs/pricing.
What is the context window for Gemma 2 9B?
Gemma 2 9B supports a 8K token context window. The context window is the total prompt plus conversation history the model can see in one call. Larger windows cost more when you fill them, because every input token is billed per million tokens.
Is Gemma 2 9B open source?
Yes. Gemma 2 9B is an open-weight model by Google. You can download the weights and self-host, or pay Groq's hosted API and skip GPU ops. Hosted API pricing on this page is the public per-million-token rate, not the electricity cost of running the weights yourself.
What benchmarks does Gemma 2 9B perform well on?
Gemma 2 9B currently reports GPQA Diamond 27.5%, AIME 2025 0.6%, MMLU 71.3%. See the performance table for the full eval set. Use those scores with the token prices above when you are choosing between a cheaper LLM and a frontier model. GPQA, SWE-bench, and HLE are the evals people usually compare first.
Is Gemma 2 9B good for coding?
Gemma 2 9B scores 40.2% on HumanEval. Pair that score with Gemini API pricing on this page: a slightly worse SWE-bench model can still win if it is much cheaper per million tokens. Open the SWE-bench or coding leaderboard from the sidebar for the full ranking, then jump into an LLM comparison against a coding specialist.
How do I compare Gemma 2 9B with another LLM?
Use the Compare picker on this page to start an LLM comparison. Search by model or provider, pick a second model, and AnotherWrapper opens a dedicated Gemma 2 9B vs page with per-million-token prices, blended 1M-in / 1M-out cost, context window, throughput, and benchmarks. You can also open any model from the sidebar, or go back to the LLM pricing table to sort the full index.
What are the cheapest alternatives to Gemma 2 9B?
The cheapest LLM alternative depends on whether you optimize for blended token cost, coding evals, or context window. Start with the popular matchups on this page, then sort the LLM pricing table by blended cost to see which hosted APIs undercut Gemma 2 9B. Open-weight models can be cheaper still if you self-host, but that is not the same as Gemini API pricing on this page.
How do I use Gemma 2 9B in my application?
Gemma 2 9B is called through Groq's API with an API key. Bill against input and output tokens separately; cache or shorten prompts if spend is high. If you are shipping a full product, AnotherWrapper includes pre-built API routes and templates for Groq and 5+ other providers with auth, payments, and deployment already configured.
How accurate is this Gemma 2 9B pricing data?
This Gemma 2 9B page tracks the latest published Gemini API pricing from Groq, quoted per million tokens. List rates can differ from invoices because of prompt caching, batch discounts, committed-use tiers, and private contracts. Official rate sheet: https://ai.google.dev/gemini-api/docs/pricing. Benchmark scores come from official publications and independent evals, not from a private lab run by AnotherWrapper.