Key Specifications
| Vendor | google |
|---|
| Version | text-bison |
|---|
| Release Date | 2023-05-10 |
|---|
| Context Window | 8192 tokens |
|---|
| Input Modalities | text |
|---|
| Output Modalities | text |
|---|
| License | Proprietary |
|---|
| Documentation | https://ai.google.dev/gemini-api/docs |
|---|
Benchmark Performance
| Benchmark | Score | Unit | Evaluated At | Notes | Source |
|---|
| MMLU | 53.9 | % | 2023-05-10 | 5-shot | view |
| HUMANEVAL | 45.1 | pass@1 | 2023-05-10 | — | view |
| GSM8K | 42.8 | % | 2023-05-10 | 0-shot CoT | view |
| MATH | 12.1 | % | 2023-05-10 | 0-shot CoT | view |
| BBH | 48.2 | % | 2023-05-10 | 3-shot CoT | view |
| GPQA | 27.7 | % | 2023-05-10 | 0-shot | view |
| IFEVAL | 59.5 | % | 2023-05-10 | prompt_strict | view |
| ARC | 86.7 | % | 2023-05-10 | challenge | view |
| MUSR | 40.8 | % | 2023-05-10 | 0-shot | view |
| WINOGRANDE | 74.1 | % | 2023-05-10 | 0-shot | view |
Pricing
| Tier | Price | Currency |
|---|
| Input | $0.5 / Mtok | USD |
| Output | $1 / Mtok | USD |
| Cache Read | $0 / Mtok | USD |
| Cache Write | $0 / Mtok | USD |
Source:
https://ai.google.dev/pricing
· as of 2023-05-10
Compliance
- Data Residency: US
- SOC2: ✓
- HIPAA: ✗
- GDPR: ✓
- ISO 27001: ✓
Text Bison
Model Overview
Google Vertex AI Text Bison 文本生成模型, 基于 PaLM 2, 8K 上下文, 适合指令跟随与生成任务。
Core Specifications
| Vendor | Version | Release Date | Context Window | Input Modalities | Output Modalities | License |
|---|
| Google | text-bison | 2023-05-10 | 8K | text | text | Proprietary |
| Benchmark | Score | Unit | Notes |
|---|
| MMLU (Massive Multitask Language Understanding) | 53.9 | % | 5-shot |
| HumanEval | 45.1 | pass@1 | — |
| GSM8K (Grade School Math 8K) | 42.8 | % | 0-shot CoT |
| MATH | 12.1 | % | 0-shot CoT |
| BBH (BIG-Bench Hard) | 48.2 | % | 3-shot CoT |
| GPQA | 27.7 | % | 0-shot |
| IFEval | 59.5 | % | prompt_strict |
| ARC | 86.7 | % | challenge |
| MUSR | 40.8 | % | 0-shot |
| WinoGrande | 74.1 | % | 0-shot |
Pricing
| Input | Output | Cache Read | Cache Write |
|---|
| — | — | — | — |
per million tokens
Strengths
- Reliable general-purpose model.
Weaknesses
- MMLU 53.9, weak knowledge reasoning.
- HumanEval 45.1, coding weak.
- Proprietary, not self-hostable.
- Context window 8K is limited.
Use Cases
References