Google, open weights

# Gemma 3 27B

> Gemma 3 27B by Google, released March 2025. Ranked #284 of 354 with a Noometry Index of 30.8. API: $0.08 in / $0.16 out per M tokens. 131K context. Scores, sources and comparisons.
- Canonical page: https://noometry.com/models/gemma-3-27b
- Last updated: 2026-10-10
- Title: Gemma 3 27B Benchmarks, Price & Rank (October 2026)

Gemma 3 27B by Google ranks 284th of 354 ranked models on the Noometry Index as of October 2026, with a score of 30.8. Its strongest category is multimodal, where it ranks 100th. API pricing starts at $0.08 per million input tokens and $0.16 per million output tokens, with a 131K-token context window.

Last verified October 10, 2026

## Specifications

- **Noometry rank:** #284 of 354
- **Index score:** 30.8
- **Evidence:** Confirmed 43 results
- **Provider:** [![](/logos/google.svg) Google](https://noometry.com/providers/google)
- **Released:** March 11, 2025
- **Weights:** Open weights
- **Reasoning:** No
- **Context window:** 131K
- **Max output:** 8K
- **Input price:** $0.08 / M
- **Output price:** $0.16 / M
- **Blended price:** $0.10 / M
- **Output speed:** 62 tokens/s [Kagi](https://help.kagi.com/kagi/ai/llm-benchmark.html)
- **Value:** #17 of 219
- **Knowledge cutoff:** August 2024
- **Input:** text, image
- **Hugging Face:** [google/gemma-3-27b-it](https://huggingface.co/google/gemma-3-27b-it)

## Category scores

Each category score combines every public result we have in that category.

Gemma 3 27B category scores

1.  Coding 22.5
2.  Agentic & Tool Use 25.1
3.  Reasoning 16.7
4.  Math 25.9
5.  Knowledge 25.5
6.  Multimodal 32.6
7.  Multilingual 46.9
8.  Instruction Following 70.6
9.  Long Context 27.6
10.  Writing & Preference 52.5
11.  020406080

Gemma 3 27B category ranks
| Category | Score | Rank | Results |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 22.5 | #334 | 4 |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 25.1 | #110 | 1 |
| [Reasoning](https://noometry.com/best/reasoning) | 16.7 | #301 | 8 |
| [Math](https://noometry.com/best/math) | 25.9 | #265 | 4 |
| [Knowledge](https://noometry.com/best/knowledge) | 25.5 | #261 | 4 |
| [Multimodal](https://noometry.com/best/multimodal) | 32.6 | #100 | 2 |
| [Multilingual](https://noometry.com/best/multilingual) | 46.9 | #155 | 1 |
| [Instruction Following](https://noometry.com/best/instruction-following) | 70.6 | #160 | 2 |
| [Long Context](https://noometry.com/best/long-context) | 27.6 | #293 | 2 |
| [Writing & Preference](https://noometry.com/best/writing) | 52.5 | #168 | 6 |

## Strengths and weaknesses

Categories where Gemma 3 27B places highest and lowest among the models ranked in each, with its score against that category's median.

### Strongest categories

Gemma 3 27B: strongest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Multilingual](https://noometry.com/best/multilingual) | 46.9 | −0.6 | #155 of 297, top 53% |
| [Instruction Following](https://noometry.com/best/instruction-following) | 70.6 | −0.6 | #160 of 305, top 53% |
| [Writing & Preference](https://noometry.com/best/writing) | 52.5 | −1.3 | #168 of 312, top 54% |

### Weakest categories

Gemma 3 27B: weakest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Long Context](https://noometry.com/best/long-context) | 27.6 | −13.3 | #293 of 296, top 99% |
| [Coding](https://noometry.com/best/coding) | 22.5 | −16.2 | #334 of 340, top 99% |
| [Reasoning](https://noometry.com/best/reasoning) | 16.7 | −6.9 | #301 of 350, top 86% |

## Closest competitors

The models ranked just above and below Gemma 3 27B. When scores are this close, price and speed are often the better way to choose.

Models ranked closest to Gemma 3 27B
| Model | Rank | Score | Blended $/M | Speed |  |
| --- | --- | --- | --- | --- | --- |
| [Mistral Small 3.2](https://noometry.com/models/mistral-small-3-2) | #280 | 31.2 | $0.13 | 68 | [Compare](https://noometry.com/compare/gemma-3-27b-vs-mistral-small-3-2) |
| [Amazon Nova Pro](https://noometry.com/models/amazon-nova-pro) | #281 | 31.0 | $1.40 | — | [Compare](https://noometry.com/compare/amazon-nova-pro-vs-gemma-3-27b) |
| [Llama 4 Maverick](https://noometry.com/models/llama-4-maverick) | #282 | 30.9 | $0.30 | 456 | [Compare](https://noometry.com/compare/gemma-3-27b-vs-llama-4-maverick) |
| [Phi-4 Mini](https://noometry.com/models/phi-4-mini) | #283 | 30.9 | $0.13 | — | [Compare](https://noometry.com/compare/gemma-3-27b-vs-phi-4-mini) |
| [Qwen1.5-72B](https://noometry.com/models/qwen1-5-72b) | #285 | 30.8 | — | — | [Compare](https://noometry.com/compare/gemma-3-27b-vs-qwen1-5-72b) |
| [Granite 3.0 2b Instruct](https://noometry.com/models/granite-3-0-2b-instruct) | #286 | 30.8 | — | — | [Compare](https://noometry.com/compare/gemma-3-27b-vs-granite-3-0-2b-instruct) |
| [Codellama 34b Instruct](https://noometry.com/models/codellama-34b-instruct) | #287 | 30.8 | — | — | [Compare](https://noometry.com/compare/codellama-34b-instruct-vs-gemma-3-27b) |
| [Llama 3.1-405B](https://noometry.com/models/llama-3-1-405b) | #288 | 30.7 | — | 78 | [Compare](https://noometry.com/compare/gemma-3-27b-vs-llama-3-1-405b) |

Sponsored placements are available on pages like this one. [Advertise on Noometry](https://noometry.com/advertise)

## Benchmark results

Every published result we track, with its source. Bold rows are the ones used for ranking; where several exist we prefer independent runs over self-reported numbers.

### Coding

Gemma 3 27B Coding benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Aider Polyglot](https://noometry.com/benchmarks/aider-polyglot) | 4.9% | #43 of 44, top 98% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SciCode](https://noometry.com/benchmarks/scicode) | 21.2% | #117 of 121, top 97% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LiveBench Coding](https://noometry.com/benchmarks/livebench-coding) | 39.9% | #23 of 39, top 59% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1322 | #183 of 294, top 63% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Agentic & Tool Use

Gemma 3 27B Agentic & Tool Use benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Berkeley Function Calling Leaderboard](https://noometry.com/benchmarks/bfcl) | 29.5% | #34 of 49, top 70% | prompt | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |

### Reasoning

Gemma 3 27B Reasoning benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Kagi LLM Benchmark](https://noometry.com/benchmarks/kagi-reasoning) | 35.1% |  |  | [Kagi LLM Benchmark](https://help.kagi.com/kagi/ai/llm-benchmark.html) |  |
| [Kagi LLM Benchmark](https://noometry.com/benchmarks/kagi-reasoning) | 40.4% | #78 of 99, top 79% |  | [Kagi LLM Benchmark](https://help.kagi.com/kagi/ai/llm-benchmark.html) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 0% | #108 of 134, top 81% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 0% | #112 of 129, top 87% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [LiveBench Reasoning](https://noometry.com/benchmarks/livebench-reasoning) | 43.8% | #24 of 39, top 62% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1340 | #164 of 297, top 56% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 52.5% | #129 of 151, top 86% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LiveBench Data Analysis](https://noometry.com/benchmarks/livebench-data-analysis) | 51.5% | #21 of 39, top 54% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 12.3% | #108 of 125, top 87% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 130.04 | #138 of 213, top 65% |  | [Epoch AI](https://epoch.ai/eci) | 2025-03-12 |
| [LiveBench](https://noometry.com/benchmarks/livebench) | 50% | #20 of 39, top 52% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Math

Gemma 3 27B Math benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 22.5% | #124 of 173, top 72% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [LiveBench Math](https://noometry.com/benchmarks/livebench-math) | 55.4% | #17 of 39, top 44% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1312 | #175 of 285, top 62% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [MATH Level 5](https://noometry.com/benchmarks/math-level-5) | 74% | #28 of 79, top 36% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-03-13 |

### Knowledge

Gemma 3 27B Knowledge benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 47.7% | #133 of 186, top 72% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [Confabulations](https://noometry.com/benchmarks/confabulations) (lower is better) | 40.3% | #51 of 51, top 100% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/confabulations) |  |
| [Vectara Hallucination Rate](https://noometry.com/benchmarks/vectara-hallucination) (lower is better) | 7.4% | #31 of 96, top 33% |  | [Vectara Hallucination Leaderboard](https://github.com/vectara/hallucination-leaderboard) |  |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1304 | #177 of 273, top 65% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Multimodal

Gemma 3 27B Multimodal benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Vision](https://noometry.com/benchmarks/arena-vision) | 1164 | #93 of 122, top 77% |  | [LMArena](https://lmarena.ai/leaderboard/vision) | 2026-10-09 |
| [GeoBench](https://noometry.com/benchmarks/geobench) | 52% | #18 of 25, top 72% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Multilingual

Gemma 3 27B Multilingual benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1334 | #155 of 297, top 53% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1346 | #169 of 285, top 60% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1368 | #137 of 223, top 62% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1362 | #122 of 231, top 53% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1287 | #127 of 211, top 61% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1308 | #127 of 213, top 60% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1349 | #148 of 283, top 53% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1349 | #145 of 226, top 65% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Instruction Following

Gemma 3 27B Instruction Following benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LiveBench Instruction Following](https://noometry.com/benchmarks/livebench-if) | 74.9% | #15 of 39, top 39% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1321 | #161 of 298, top 55% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Long Context

Gemma 3 27B Long Context benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Fiction.LiveBench](https://noometry.com/benchmarks/fiction-livebench) | 33.3% | #45 of 47, top 96% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1333 | #161 of 291, top 56% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Writing & Preference

Gemma 3 27B Writing & Preference benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1358 | #152 of 297, top 52% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1346 | #133 of 295, top 46% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [Short-Story Creative Writing](https://noometry.com/benchmarks/lech-mazur-writing) | 79.9% | #16 of 39, top 42% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [EQ-Bench Creative Writing](https://noometry.com/benchmarks/eqbench-creative-writing) | 1266 | #81 of 115, top 71% |  | [EQ-Bench](https://eqbench.com/creative_writing.html) |  |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1345 | #156 of 295, top 53% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LiveBench Language](https://noometry.com/benchmarks/livebench-language) | 34.6% | #23 of 39, top 59% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

## API pricing by provider

Gemma 3 27B API prices
| Route | Input $/M | Output $/M | Cached input $/M | Checked |
| --- | --- | --- | --- | --- |
| [bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html) | $0.23 | $0.38 | — | 2026-10-10 |
| [deepinfra](https://deepinfra.com/models) | $0.08 | $0.16 | — | 2026-10-10 |
| [openrouter](https://openrouter.ai/google/gemma-3-27b-it) | $0.08 | $0.45 | $0.04 | 2026-10-10 |

[All Google API prices →](https://noometry.com/llm-pricing/google) [Estimate your cost →](https://noometry.com/tools/cost-calculator)

## Compare Gemma 3 27B

-   [Gemma 3 27B vs Gemma 2 27B](https://noometry.com/compare/gemma-2-27b-vs-gemma-3-27b)
-   [Gemma 3 27B vs Phi-4 Mini](https://noometry.com/compare/gemma-3-27b-vs-phi-4-mini)
-   [Gemma 3 27B vs Qwen1.5-72B](https://noometry.com/compare/gemma-3-27b-vs-qwen1-5-72b)
-   [Gemma 3 27B vs Llama 4 Maverick](https://noometry.com/compare/gemma-3-27b-vs-llama-4-maverick)
-   [Gemma 3 27B vs Granite 3.0 2b Instruct](https://noometry.com/compare/gemma-3-27b-vs-granite-3-0-2b-instruct)
-   [Gemma 3 27B vs Amazon Nova Pro](https://noometry.com/compare/amazon-nova-pro-vs-gemma-3-27b)
-   [Gemma 3 27B vs Codellama 34b Instruct](https://noometry.com/compare/codellama-34b-instruct-vs-gemma-3-27b)
-   [Gemma 3 27B vs GPT-6 Astra](https://noometry.com/compare/gemma-3-27b-vs-gpt-6-astra)
-   [Gemma 3 27B vs Claude Fable 5.1](https://noometry.com/compare/claude-fable-5-1-vs-gemma-3-27b)
-   [Gemma 3 27B vs Kimi K3](https://noometry.com/compare/gemma-3-27b-vs-kimi-k3)
-   [Gemma 3 27B vs Grok 4.6](https://noometry.com/compare/gemma-3-27b-vs-grok-4-6)
-   [Gemma 3 27B vs Qwen3.8 Max](https://noometry.com/compare/gemma-3-27b-vs-qwen3-8-max)
-   [Gemma 3 27B vs GLM-5.3](https://noometry.com/compare/gemma-3-27b-vs-glm-5-3)
-   [Gemma 3 27B vs Muse Spark 1.3](https://noometry.com/compare/gemma-3-27b-vs-muse-spark-1-3)

## Other Google models

-   [Gemini 3.8 Flash](https://noometry.com/models/gemini-3-8-flash)61.8
-   [Gemini 3.7 Flash](https://noometry.com/models/gemini-3-7-flash)59.8
-   [Gemini 3.1 Pro Preview](https://noometry.com/models/gemini-3-1-pro-preview)56.7
-   [Gemini 4 Argon](https://noometry.com/models/gemini-4-argon)56.5
-   [Gemini 3 Pro](https://noometry.com/models/gemini-3-pro)54.8
-   [Gemini 3.5 Flash](https://noometry.com/models/gemini-3-5-flash)54.2
-   [Gemini 3.6 Flash](https://noometry.com/models/gemini-3-6-flash)54.1
-   [Gemini 3 Flash Preview](https://noometry.com/models/gemini-3-flash-preview)52.3

## Frequently asked questions

### How good is Gemma 3 27B?

Gemma 3 27B by Google ranks 284th of 354 ranked models on the Noometry Index as of October 2026, with a score of 30.8. Its strongest category is multimodal, where it ranks 100th. API pricing starts at $0.08 per million input tokens and $0.16 per million output tokens, with a 131K-token context window.

### How much does Gemma 3 27B cost?

Gemma 3 27B costs $0.08 per million input tokens and $0.16 per million output tokens on deepinfra.

### What is Gemma 3 27B's context window?

Gemma 3 27B accepts up to 131K tokens of input and can write up to 8K tokens in one response.

### Is Gemma 3 27B open source?

Yes. Gemma 3 27B's weights are downloadable from Hugging Face (google/gemma-3-27b-it); check the license for commercial terms.

### How fast is Gemma 3 27B?

Gemma 3 27B generated about 62 output tokens per second in the Kagi LLM Benchmark's timed runs. Speed varies by provider, load and reasoning effort.

### What are Gemma 3 27B's strengths and weaknesses?

Relative to other ranked models, Gemma 3 27B places best in multilingual, instruction following, writing & preference and lowest in long context, coding, reasoning.

### What is Gemma 3 27B best at?

Its best category is multimodal, where it ranks 100th on Noometry.

### Cite this page

Noometry. (2026). Gemma 3 27B benchmarks and pricing. Retrieved October 10, 2026, from https://noometry.com/models/gemma-3-27b

Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/models/gemma-3-27b.md).