LLM Reference

Gemma 2 27B Instruct

Released
2024-06-27
Last refreshed
2026-05-11
Status
Researched 54d ago
Open WeightsCommercial use with conditionsClassificationJSON / Tool use

Gemma 2 27B Instruct is worth evaluating for classification and json / tool use when its provider route and context window match the workload.

Use it for

  • Teams evaluating classification and json / tool use
  • Workloads that can use a 8k context window
  • Buyers comparing 4 tracked provider routes

Do not use it for

  • Vision or document-understanding workloads
Specifications
Family
Gemma 2
Released
2024-06-27
Context
8k
Parameters
27B
Architecture
Decoder Only
Specialization
general
Openness
Open weights
License
GemmaCommercial use with conditions
Training
finetuned
Created by

Pioneering artificial intelligence research.

London, United Kingdom
Founded 2014
Website
Pricing
Output / 1M
$0.400
Input / 1M
$0.400

Cheapest of 5 routes · Replicate API

About

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.

Gemma 2 27B Instruct is an open-weight model in the Gemma 2 family. The structured metadata tracks a 8k-token context window and structured outputs. This page tracks provider routes through NVIDIA NIM, OpenRouter, Fireworks AI, and 2 more, with the cheapest tracked route listed at $0.25 input and $0.75 output per 1M tokens. Headline tracked benchmarks include Massive Multitask Language Understanding 82.3.

Top use-case fit

Classification

Q/$ B

1 relevant benchmark in the decision map.

JSON / Tool use

Included by capability and metadata signals in the decision map.

Capabilities

Structured Outputs

Benchmark peer barsfor Classification

Benchmark scores(1)

Scores are benchmark-specific and are direction-aware: the same numeric gap can mean very different outcomes across suites. Use the leaderboard context and this model's provider route to decide whether the winning margin is meaningful for your workload.
BenchmarkScoreVersionSource
Massive Multitask Language Understanding82.35-shothttps://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard

Migration checks

No linked migration route is available for this model yet.