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Gemma 3 12B Instruct

GoogleGemma開源權重Gemma · 商用許可

描述

Gemma 3 12B is a 12-billion-parameter vision-language model from Google, handling text and image input and generating text output. It features a 128K context window, multilingual support, and open weights. Suitable for question answering, summarization, reasoning, and image understanding tasks.

發布日期
2025-03-12
參數規模
12.0B
上下文長度
131K
支援模態
image, text

能力雷達圖

22
general
10
coding
31
reasoning
23
science
25
agents
80
multimodal

排行榜排名

領域#排名分數來源
程式碼能力榜519
8.0
AA
通用能力榜474
25.0
AA
多模態榜57
43.0
LS
科學能力501
21.0
AA

基準測試分數 (LLM Stats)

(LLM Stats (zeroeval))

Biology

GPQANYU + Cohere + Anthropic (2023)40.9%自報

Code

HumanEvalOpenAI (2021)85.4%自報
LiveCodeBench24.6%自報

Factuality

FACTS Grounding75.8%自報
SimpleQA6.3%自報

Finance

MMLU-Pro60.6%自報

General

IFEvalGoogle Research (2023)88.9%自報
Natural2Code80.7%自報
MBPP0.73 / 100自報
Global-MMLU-Lite69.5%自報
MMMU (val)59.6%自報
BIG-Bench Extra Hard16.3%自報

Image To Text

DocVQADocVQA (2020)87.1%自報
VQAv2 (val)71.6%自報
TextVQA67.7%自報

Language

BIG-Bench Hard85.7%自報
WMT24++51.6%自報
ECLeKTic10.3%自報

Math

GSM8k94.4%自報
MATH83.8%自報
MathVista-Mini62.9%自報
HiddenMath54.5%自報

Multimodal

AI2D84.2%自報
ChartQAMasry et al. (2022)75.7%自報
InfoVQA64.9%自報

Reasoning

Bird-SQL (dev)47.9%自報

AA 評測指數

(Artificial Analysis)
Math Index(Artificial Analysis)
18.3
Coding Index(Artificial Analysis)
5.8
Intelligence Index(Artificial Analysis)
5.5
Math 500(OpenAI (2024), subset of Hendrycks et al. MATH (2021))
0.9
Mmlu Pro(TIGER-Lab (Univ. of Waterloo, Toronto, CMU, 2024))
0.6
Ifbench(Google Research (2023))
0.4
Gpqa(NYU + Cohere + Anthropic (2023))
0.3
Aime(MAA (Mathematical Association of America))
0.2
Aime 25(MAA (Mathematical Association of America))
0.2
Scicode(UIUC + Argonne National Lab (2024))
0.2
Livecodebench(UC Berkeley + MIT + Cornell (2024))
0.1
Tau2(Sierra + U Toronto + Vector Institute (2025))
0.1
Lcr(Artificial Analysis)
0.1
Hle(Center for AI Safety + Scale AI (2025))
0.0
Tau Banking
0.0
Terminalbench Hard(Stanford × Laude Institute (2026))
0.0
Terminalbench V2 1
0.0

LLM Stats 分類評分

(LLM Stats (zeroeval))
Structured Output
90
Instruction Following
90
Image To Text
80
Grounding
80
Math
70
Multimodal
70
Vision
70
Legal
60
Reasoning
60
Finance
60
General
60
Healthcare
60
Code
60
Language
50
Physics
40
Factuality
40
Biology
40
Chemistry
40

定價

輸入價格免費
輸出價格免費
混合價格(3:1)免費

速度

Tokens/秒0.0
首Token延遲0.00s
首回答延遲0.00s

供應商價格排行

供應商價格排行

1 個供應商

供應商輸入輸出
1Neon
$0.15
$0.5

比較該模型在不同 API 供應商之間的定價。

外部連結