Key Specifications
| Vendor | mistral |
|---|
| Version | tiny |
|---|
| Release Date | 2024-02-26 |
|---|
| Context Window | 32000 tokens |
|---|
| Input Modalities | text |
|---|
| Output Modalities | text |
|---|
| License | Apache 2.0 |
|---|
| Documentation | https://docs.mistral.ai/ |
|---|
Benchmark Performance
| Benchmark | Score | Unit | Evaluated At | Notes | Source |
|---|
| MMLU | 54.2 | % | 2024-02-26 | 5-shot | view |
| HUMANEVAL | 45.8 | pass@1 | 2024-02-26 | — | view |
| GSM8K | 57.2 | % | 2024-02-26 | 0-shot CoT | view |
| MATH | 16 | % | 2024-02-26 | 0-shot CoT | view |
| BBH | 51.3 | % | 2024-02-26 | 3-shot CoT | view |
| GPQA | 28.7 | % | 2024-02-26 | 0-shot | view |
| IFEVAL | 51.7 | % | 2024-02-26 | prompt_strict | view |
| ARC | 82.4 | % | 2024-02-26 | challenge | view |
| MUSR | 41.4 | % | 2024-02-26 | 0-shot | view |
| WINOGRANDE | 66.6 | % | 2024-02-26 | 0-shot | view |
Pricing
| Tier | Price | Currency |
|---|
| Input | $0.15 / Mtok | USD |
| Output | $0.15 / Mtok | USD |
| Cache Read | $0 / Mtok | USD |
| Cache Write | $0 / Mtok | USD |
Source:
https://mistral.ai/technology/
· as of 2024-02-26
Compliance
- Data Residency: EU
- SOC2: ✓
- HIPAA: ✗
- GDPR: ✓
- ISO 27001: ✓
Mistral Tiny
Modellübersicht
Mistral AI Tiny 经济型模型, 32K 上下文, 基于 Mistral 7B, 价格最低, 适合简单任务。
Kernspezifikationen
| Anbieter | Version | Veröffentlichungsdatum | Kontextfenster | Eingabemodalitäten | Ausgabemodalitäten | Lizenz |
|---|
| Mistral | tiny | 2024-02-26 | 32K | text | text | Apache 2.0 |
Benchmark-Leistung
| Benchmark | Ergebnis | Einheit | Notizen |
|---|
| MMLU (Massive Multitask Language Understanding) | 54.2 | % | 5-shot |
| HumanEval | 45.8 | pass@1 | — |
| GSM8K (Grade School Math 8K) | 57.2 | % | 0-shot CoT |
| MATH | 16.0 | % | 0-shot CoT |
| BBH (BIG-Bench Hard) | 51.3 | % | 3-shot CoT |
| GPQA | 28.7 | % | 0-shot |
| IFEval | 51.7 | % | prompt_strict |
| ARC | 82.4 | % | challenge |
| MUSR | 41.4 | % | 0-shot |
| WinoGrande | 66.6 | % | 0-shot |
Preise
| Eingabe | Ausgabe | Cache-Lesen | Cache-Schreiben |
|---|
| — | — | — | — |
pro Million Token
Stärken
Schwächen
- MMLU 仅 54.2，知识推理偏弱。
- HumanEval 45.8，代码能力较弱。
- 闭源专有模型，不支持自托管。
Anwendungsfälle
Referenzen