Jamba 1.5 Large vs Mistral Large 2: Benchmark Comparison
Detailed comparison of Jamba 1.5 Large and Mistral Large 2 covering benchmarks, pricing, context window, and compliance.
Key Specifications
| Specification | Jamba 1.5 Large | Mistral Large 2 |
|---|---|---|
| Vendor | other | mistral |
| Version | jamba-1-5-large | large-2 |
| Release Date | 2024-08-07 | 2024-07-24 |
| Context Window | 256000 tokens | 128000 tokens |
| Input Modalities | text | text |
| Output Modalities | text | text |
| License | Jamba Open Model License | Mistral Research License |
| SOC2 | ✗ | ✓ |
| HIPAA | ✗ | ✗ |
| GDPR | ✗ | ✓ |
| ISO 27001 | ✗ | ✓ |
Benchmark Results
| Benchmark | Jamba 1.5 Large | Mistral Large 2 | Winner |
|---|---|---|---|
| ARC | 93.8 | — | Jamba 1.5 Large |
| BBH | 82.8 | 81 | Jamba 1.5 Large |
| GPQA | 50.8 | — | Jamba 1.5 Large |
| GSM8K | 83.5 | 93 | Mistral Large 2 |
| HUMANEVAL | 73.7 | 92 | Mistral Large 2 |
| IFEVAL | 70.8 | — | Jamba 1.5 Large |
| MATH | 60 | 71 | Mistral Large 2 |
| MMLU | 84.3 | 84 | Jamba 1.5 Large |
| MUSR | 51.1 | — | Jamba 1.5 Large |
| WINOGRANDE | 84.3 | — | Jamba 1.5 Large |
Pricing Comparison
| Tier (per Mtok) | Jamba 1.5 Large | Mistral Large 2 |
|---|---|---|
| Input | $2 | $2 |
| Output | $8 | $6 |
| Cache Read | $0 | $0 |
| Cache Write | $0 | $0 |
Jamba 1.5 Large contra Mistral Large 2
Visão geral do modelo
Jamba 1.5 Large and Mistral Large 2 are both notable options in the AI model market. This page compares their benchmarks, pricing, and compliance.
Especificações principais
| Fornecedor | Data de lançamento | Janela de contexto | Licença |
|---|---|---|---|
| Other / Mistral | 2024-08-07 / 2024-07-24 | 256K / 128K | Jamba Open Model License / Mistral Research License |
Desempenho em benchmarks
| Benchmark | Jamba 1.5 Large | Mistral Large 2 | Vencedor |
|---|---|---|---|
| ARC | 93.8 | — | A |
| BBH (BIG-Bench Hard) | 82.8 | 81.0 | A |
| GPQA | 50.8 | — | A |
| GSM8K (Grade School Math 8K) | 83.5 | 93.0 | B |
| HumanEval | 73.7 | 92.0 | B |
| IFEval | 70.8 | — | A |
| MATH | 60.0 | 71.0 | B |
| MMLU (Massive Multitask Language Understanding) | 84.3 | 84.0 | Tie |
| MUSR | 51.1 | — | A |
| WinoGrande | 84.3 | — | A |
Comparação de preços
| Entrada | Saída | Leitura de cache | Escrita de cache |
|---|---|---|---|
| — / — | — / — | — / — | — / — |
por milhão de tokens — A / B
Pontos fortes & Pontos fracos
Jamba 1.5 Large
- ✅ MMLU score 84.3, strong knowledge reasoning.
- ✅ 上下文窗口 256K，支持长文本。
- ✅ 采用 MoE 混合专家架构。
- ⚠️ 闭源专有模型，不支持自托管。
Mistral Large 2
- ✅ MMLU score 84.0, strong knowledge reasoning.
- ✅ HumanEval 92.0, excellent code generation.
- ✅ GSM8K 93.0, robust math reasoning.
- ⚠️ 闭源专有模型，不支持自托管。
Opinião do editor
Jamba 1.5 Large and Mistral Large 2 each have their strengths. Choose based on workload (code, long context, vision), referencing the tables above.
Perguntas frequentes
Which model is better for coding tasks?
Refer to the HumanEval benchmark table; the model with a higher score is better suited for coding tasks.
Which model is cheaper?
Refer to the pricing comparison table above; the model with lower input/output prices is more cost-effective.
Which has a longer context window?
Refer to the key specifications table; the model with a larger context window is better for long documents.
Referências
Editor's Take
See Editor's Take section.