Key Specifications
| Vendor | mistral |
|---|
| Version | ministral-3b |
|---|
| Release Date | 2024-10-16 |
|---|
| Context Window | 128000 tokens |
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| Input Modalities | text |
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| Output Modalities | text |
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| License | Mistral Research License |
|---|
| Documentation | https://docs.mistral.ai/ |
|---|
Benchmark Performance
| Benchmark | Score | Unit | Evaluated At | Notes | Source |
|---|
| MMLU | 42.4 | % | 2024-10-16 | 5-shot | view |
| HUMANEVAL | 37.2 | pass@1 | 2024-10-16 | — | view |
| GSM8K | 32.2 | % | 2024-10-16 | 0-shot CoT | view |
| MATH | 10.1 | % | 2024-10-16 | 0-shot CoT | view |
| BBH | 50.1 | % | 2024-10-16 | 3-shot CoT | view |
| GPQA | 19 | % | 2024-10-16 | 0-shot | view |
| IFEVAL | 47.8 | % | 2024-10-16 | prompt_strict | view |
| ARC | 81.5 | % | 2024-10-16 | challenge | view |
| MUSR | 29.4 | % | 2024-10-16 | 0-shot | view |
| WINOGRANDE | 64.8 | % | 2024-10-16 | 0-shot | view |
Pricing
| Tier | Price | Currency |
|---|
| Input | $0.04 / Mtok | USD |
| Output | $0.04 / Mtok | USD |
| Cache Read | $0 / Mtok | USD |
| Cache Write | $0 / Mtok | USD |
Source:
https://mistral.ai/technology/
· as of 2024-10-16
Compliance
- Data Residency: EU
- SOC2: ✓
- HIPAA: ✗
- GDPR: ✓
- ISO 27001: ✓
Ministral 3B
Modellübersicht
Mistral Ministral 3B 超轻量边缘模型, 128K 上下文, 3B 参数, 最具性价比的边缘部署模型。
Kernspezifikationen
| Anbieter | Version | Veröffentlichungsdatum | Kontextfenster | Eingabemodalitäten | Ausgabemodalitäten | Lizenz |
|---|
| Mistral | ministral-3b | 2024-10-16 | 128K | text | text | Mistral Research License |
Benchmark-Leistung
| Benchmark | Ergebnis | Einheit | Notizen |
|---|
| MMLU (Massive Multitask Language Understanding) | 42.4 | % | 5-shot |
| HumanEval | 37.2 | pass@1 | — |
| GSM8K (Grade School Math 8K) | 32.2 | % | 0-shot CoT |
| MATH | 10.1 | % | 0-shot CoT |
| BBH (BIG-Bench Hard) | 50.1 | % | 3-shot CoT |
| GPQA | 19.0 | % | 0-shot |
| IFEval | 47.8 | % | prompt_strict |
| ARC | 81.5 | % | challenge |
| MUSR | 29.4 | % | 0-shot |
| WinoGrande | 64.8 | % | 0-shot |
Preise
| Eingabe | Ausgabe | Cache-Lesen | Cache-Schreiben |
|---|
| — | — | — | — |
pro Million Token
Stärken
Schwächen
- MMLU 仅 42.4,知识推理偏弱。
- HumanEval 37.2,代码能力较弱。
- 闭源专有模型,不支持自托管。
Anwendungsfälle
Referenzen