Gemini 1.0 Pro: Complete Benchmark Performance Guide

google 1.0-pro 2023-12-06 Proprietary

In-depth analysis of Gemini 1.0 Pro performance on MMLU, HumanEval, GSM8K, MATH, BBH benchmarks, including pricing and use cases.

Key Specifications

Vendorgoogle
Version1.0-pro
Release Date2023-12-06
Context Window32768 tokens
Input Modalitiestext
Output Modalitiestext
LicenseProprietary
Documentationhttps://ai.google.dev/gemini-api/docs

Benchmark Performance

BenchmarkScoreUnitEvaluated AtNotesSource
MMLU84.5%2023-12-065-shotview
HUMANEVAL76.4pass@12023-12-06—view
GSM8K82.4%2023-12-060-shot CoTview
MATH58.2%2023-12-060-shot CoTview
BBH79.1%2023-12-063-shot CoTview
GPQA47.7%2023-12-060-shotview
IFEVAL75.1%2023-12-06prompt_strictview
ARC94.1%2023-12-06challengeview
MUSR57.8%2023-12-060-shotview
WINOGRANDE84.5%2023-12-060-shotview

Pricing

TierPriceCurrency
Input$0.5 / MtokUSD
Output$1.5 / MtokUSD
Cache Read$0 / MtokUSD
Cache Write$0 / MtokUSD

Source: https://ai.google.dev/pricing · as of 2023-12-06

Compliance

  • Data Residency: US
  • SOC2: ✓
  • HIPAA: ✗
  • GDPR: ✓
  • ISO 27001: ✓

Gemini 1.0 Pro

Aperçu du modèle

Google Gemini 1.0 Pro 首代模型, 32K 上下文, 在 MMLU/GSM8K 上超越 GPT-3.5, 多模态能力初步集成。

Spécifications principales

FournisseurVersionDate de sortieFenêtre de contexteModalités d’entréeModalités de sortieLicence
Google1.0-pro2023-12-0632KtexttextProprietary

Performance aux benchmarks

BenchmarkScoreUnitéNotes
MMLU (Massive Multitask Language Understanding)84.5%5-shot
HumanEval76.4pass@1—
GSM8K (Grade School Math 8K)82.4%0-shot CoT
MATH58.2%0-shot CoT
BBH (BIG-Bench Hard)79.1%3-shot CoT
GPQA47.7%0-shot
IFEval75.1%prompt_strict
ARC94.1%challenge
MUSR57.8%0-shot
WinoGrande84.5%0-shot

Tarification

EntréeSortieLecture cacheÉcriture cache
————

par million de jetons

Forces

  • MMLU score 84.5, strong knowledge reasoning.

Faiblesses

  • 闭源专有模型，不支持自托管。

Cas d’usage

  • 代码生成与调试

Références