Last refreshed 2026-04-19. Next refresh: weekly.
Why use Gemma 2 27B Instruct on Replicate API?
Replicate API offers Gemma 2 27B Instruct with pay-as-you-go pricing at $0.40/1M input tokens. Replicate is a cloud-based platform that enables users to run machine learning models easily and efficiently.
Compare Gemma 2 27B Instruct across 7 providers to find the best fit for your use caseSetup recipe
Python + curlpip install replicateexport REPLICATE_API_TOKEN=...import replicate
output = replicate.run(
"google-deepmind/gemma2-27b-it",
input={"prompt": "Hello"}google-deepmind/gemma2-27b-itRequest example
import replicate
# reads REPLICATE_API_TOKEN from env
# google-deepmind/gemma2-27b-it format: "owner/model-name" (latest version) or "owner/model-name:version-hash"
output = replicate.run(
"google-deepmind/gemma2-27b-it",
input={"prompt": "Hello"}
)
# Output is a list or generator depending on the model
print("".join(output))Gotchas
- Use provider model ID "google-deepmind/gemma2-27b-it", not the LLMReference slug "gemma-2-27b-it".
- Replicate uses "owner/model-name" format (e.g. "meta/meta-llama-3-8b-instruct") for the latest version, or "owner/model-name:version-sha" to pin to a specific version. The REST endpoint splits owner and model-name into the path: /v1/models/{owner}/{model-name}/predictions.
- The examples expect REPLICATE_API_TOKEN; rename it only if your application config maps the new variable.
Compare Gemma 2 27B Instruct Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| NVIDIA NIM | — | — |
| OpenRouter | $0.65 | $0.65 |
| Fireworks AI | $0.90 | $0.90 |
| Arcee AI | $0.25 | $0.75 |
| Replicate API | $0.40 | $0.40 |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.40 |
| Output tokens | $0.40 |
Capabilities
About Gemma 2 27B Instruct
Gemma 2 27B Instruct is a cutting-edge large language model from Google, excelling in text generation, question answering, summarization, and reasoning tasks. It features a decoder-only transformer architecture, utilizing 27 billion parameters, and supports context length processing of up to 8,192 tokens. The model incorporates innovative mechanisms like Grouped Query Attention and Sliding Window Attention to enhance efficiency and effectiveness in handling long texts. Its instruction-tuned variants are designed for improved interaction in conversational tasks, and it benefits from knowledge distillation techniques for enhanced performance. Additionally, Gemma 2 27B Instruct is openly accessible, promoting wider innovation in AI applications.