Specifications
Context Window
—
Input Price/1M
—
Output Price/1M
—
Parameters
—
Benchmarks
Jamba 1.7 Mini results on the main AI model evaluation benchmarks. Higher scores indicate better performance.
Agentic
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| Terminal-Bench Hard | 0.0 | 100.0 | — |
Coding
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| SciCode | 9.0 | 100.0 | — |
| LiveCodeBench | 6.0 | 100.0 | Artificial Analysis official API |
| AA Coding Index | 3.1 | 100.0 | Artificial Analysis official API |
Knowledge
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| MMLU-Pro | 39.0 | 100.0 | — |
Long Context
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| AA-LCR | 13.0 | 100.0 | — |
Math
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| MATH-500 | 25.8 | 100.0 | Artificial Analysis official API |
| AA Math Index | 0.3 | 100.0 | Artificial Analysis official API |
| AIME 2025 | 0.0 | 100.0 | Artificial Analysis official API |
overall
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| AA Intelligence Index | 8.1 | 100.0 | Artificial Analysis official API |
Reasoning
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| MMLU Pro | 38.8 | 100.0 | Artificial Analysis official API |
| GPQA Diamond | 32.0 | 100.0 | Artificial Analysis official API |
| IFBench | 31.0 | 100.0 | — |
| HLE | 5.0 | 100.0 | — |
Tool Use
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| Tau²-Bench | 13.0 | 100.0 | — |
Information
- Release date
- July 07, 2025
- Tool Calling
- ❌ Not supported
- Vision
- ❌ Not supported
- Audio
- ❌ Not supported
Full Analysis: Jamba 1.7 Mini
What is Jamba 1.7 Mini?
Jamba 1.7 Mini is an AI model developed by AI21 Labs, classified as a text model. It focuses on text processing and natural language generation. As a proprietary model, it is available via AI21 Labs's cloud API.
Pricing & Costs in 2026
Jamba 1.7 Mini does not have public per-token pricing available at this time. Some models offer access via enterprise plans or research programs. Check AI21 Labs's official website for up-to-date availability and pricing.
Benchmarks & Performance
Jamba 1.7 Mini was evaluated on 15 different benchmarks, covering categories like Agentic, Coding, Knowledge, Long Context, Math, overall, Reasoning, Tool Use. Results show moderate performance across available evaluations.
It's important to note that benchmarks measure specific aspects and don't capture the full user experience. Factors like instruction adherence, behavior in long conversations, and real-world task quality vary significantly between models and aren't always reflected in standard scores.
Recommended Use Cases
Jamba 1.7 Mini specializes in text, offering advanced capabilities for creating and processing text content.
Comparison with Alternatives
In the 2026 AI model ecosystem, Jamba 1.7 Mini competes directly with similarly capable models. AI21 Labs competes in this segment against OpenAI, Anthropic, Google, and Meta. The choice between models depends on the specific use case, budget, latency requirements, and need for features like multimodality and tool calling.
For a detailed side-by-side comparison, use our comparison tool or check the overall model ranking.
Frequently Asked Questions
What is Jamba 1.7 Mini?
Jamba 1.7 Mini is an AI model developed by AI21 Labs. It is a text model.
How much does Jamba 1.7 Mini cost?
Jamba 1.7 Mini does not have public per-token pricing available at this time. Check AI21 Labs's official website for up-to-date information.
How does Jamba 1.7 Mini compare with other models?
In available benchmarks, Jamba 1.7 Mini scored: Terminal-Bench Hard: 0/100, SciCode: 9/100, LiveCodeBench: 6/100. See the full table above for a detailed comparison.
Is Jamba 1.7 Mini open source?
No, Jamba 1.7 Mini is a proprietary model from AI21 Labs. It is available via cloud API. For open source alternatives, check our open source model ranking.
What is Jamba 1.7 Mini best for?
Jamba 1.7 Mini excels at general-purpose language tasks.
Last updated: June 01, 2026 • View methodology →