Intelligence
5 scoreAI21 Labs · Flagships Analysis
Jamba 1.7 Large
Intelligence, Performance & Price Analysis
Canonical slug: jamba-1-7-large · Canonical model registry at build time
Speed
58.3 output tokens/secLatency
1.57s TTFTInput Price
$2.00 / 1M tokensOutput Price
$8.00 / 1M tokensExecutive Assessment
Routing Verdict & Tradeoffs
Ethen Routing Verdict
Budget-friendly / task-specific model
Jamba 1.7 Large scores 5 (estimated) on the Artificial Analysis Intelligence Index, placing it at the lower end among other open weight non-reasoning models of similar size (median: 17). Jamba 1.7 Large generates output at 58.3 tokens per second (based on AI21 Labs's API), which is above average compared to other open weight non-reasoning models of similar size (median: 58.3 t/s). Jamba 1.7 Large costs $2.00 per 1M input tokens (at the higher end, median: $0.60) and $8.00 per 1M output tokens (at the higher end, median: $2.40), based on AI21 Labs's API.
Best for high-volume, simple, or domain-specific tasks where cost or speed matters more than deep reasoning.
Task Fit Assessment
| Workload | Rating | Notes |
|---|---|---|
| Complex reasoning & agentic workflows | suboptimal | Intelligence score 5 is better suited for straightforward tasks than multi-step reasoning. |
| High-volume chat & customer-facing | optimal | Output speed 58.3 tokens/sec is adequate for chat. |
| Latency-sensitive applications | optimal | TTFT 1.57s — adequate latency for most interactive use cases. |
| Cost-sensitive pipelines | viable | Output pricing at $8.00 is premium. Route high-volume simple tasks to cheaper alternatives. |
Cost Pressure Analysis
High
Pricing is premium — input $2.00, output $8.00. This model is expensive for high-volume or output-heavy workloads.
Technical Specifications
Architecture and Limits
| Specification | Value | Validation Authority |
|---|---|---|
| Model type | Open weights | inferred |
| Reasoning | No | faq |
| Input modalities | Jamba 1.7 Large supports text input. | faq |
| Output modalities | Jamba 1.7 Large supports text output. | faq |
| Context window | 260k tokens | faq |
| Open weights / source | Yes, Jamba 1.7 Large is open weights. The model weights are publicly available and can be downloaded for self-hosting. | faq |
| Parameters | Jamba 1.7 Large has 398 billion parameters (94 billion active). | faq |
| Active parameters | Jamba 1.7 Large is a Mixture of Experts (MoE) model with 398 billion total parameters, but only 94 billion active parameters are used during inference. | faq |
| License | Jamba 1.7 Large is released under the Jamba Open Model License Agreement license. This license allows commercial use. | faq |
| Knowledge cutoff | Jamba 1.7 Large has a knowledge cutoff of August 2024. The model's training data includes information up to this date. | faq |
| API availability | Yes, Jamba 1.7 Large is available via API through 1 provider. | faq |
Evidence charts
Profile visualizations
Charts are the same canonical evidence cards previously published for this slug, contained inside the D18D page grammar.
AA-Omniscience Index
AA-Omniscience Index (higher is better) measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer. Scores range from -100 to 100, where 0 means as many correct as incorrect answers, and negative scores mean more incorrect than correct. · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Bars intentionally show many models at once so the page feels like a benchmark report rather than a dashboard.
Artificial Analysis Intelligence Index by Open Weights / Proprietary
Artificial Analysis Intelligence Index v4.1 incorporates 9 evaluations: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Artificial Analysis Intelligence Index
Artificial Analysis Intelligence Index v4.1 incorporates 9 evaluations: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Artificial Analysis Openness Index: Score
Openness Index assesses model openness on a 0 to 100 normalized scale (higher is more open) · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Bars intentionally show many models at once so the page feels like a benchmark report rather than a dashboard.
Intelligence
Artificial Analysis Intelligence Index · Higher is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Output Speed
Output tokens per second · Higher is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Speed should be read together with latency and cost; fast output does not always mean better user-perceived performance.
Speed
Output tokens per second · Higher is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Speed should be read together with latency and cost; fast output does not always mean better user-perceived performance.
End-to-End Response Time
Seconds to output 500 tokens, including reasoning model 'thinking' time · Lower is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Bars intentionally show many models at once so the page feels like a benchmark report rather than a dashboard.
Latency: Time To First Answer Token
Seconds to first answer token received · Accounts for reasoning model 'thinking' time · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Latency includes thinking / reasoning time when applicable. Lower is better for interactive use cases.
Context Window
Context window: tokens limit · Higher is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Larger context windows enable complex multi-turn tasks but may increase latency and cost for long inputs.
Cost per Intelligence Index Task
Weighted average cost (USD) per Artificial Analysis Intelligence Index task, segmented by token type. Lower is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Ethen Gateway should route away from this model when the task is simple, repetitive, or likely to generate many output tokens.
Cost per Task
Weighted average cost (USD) per Intelligence Index task · Lower is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Ethen Gateway should route away from this model when the task is simple, repetitive, or likely to generate many output tokens.
Cost to Run Artificial Analysis Intelligence Index
Cost (USD) to run all evaluations in the Artificial Analysis Intelligence Index · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Ethen Gateway should route away from this model when the task is simple, repetitive, or likely to generate many output tokens.
Pricing: Cache Hit, Input, and Output
Price (USD per M Tokens) · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Bars intentionally show many models at once so the page feels like a benchmark report rather than a dashboard.
Time per Intelligence Index Task
Weighted average decode time (minutes) per task; excludes TTFT and overhead time · Lower is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Model Size: Total and Active Parameters
Comparison between total model parameters and parameters active during inference · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Larger models generally perform better but cost more to serve. Active parameter count matters for inference efficiency.
Methodology
Methodology & Provenance
This page is rendered from the normalized profile and page JSON for Jamba 1.7 Large.
Benchmark values are preserved as normalized; only layout, disclosure ordering, and typography are adjusted for readability.
Frequently Asked Questions
Model FAQs & Technical Disclosures
Jamba 1.7 Large was released on July 7, 2025.
Jamba 1.7 Large was created by AI21 Labs.
Jamba 1.7 Large scores 5 (estimated) on the Artificial Analysis Intelligence Index, placing it at the lower end among other open weight non-reasoning models of similar size (median: 17).
Jamba 1.7 Large generates output at 58.3 tokens per second (based on AI21 Labs's API), which is above average compared to other open weight non-reasoning models of similar size (median: 58.3 t/s).
Jamba 1.7 Large has a time to first token (TTFT) of 1.57s (based on AI21 Labs's API), which is very competitive compared to other open weight non-reasoning models of similar size (median: 2.33s).
Jamba 1.7 Large costs $2.00 per 1M input tokens (at the higher end, median: $0.60) and $8.00 per 1M output tokens (at the higher end, median: $2.40), based on AI21 Labs's API.
Jamba 1.7 Large costs $2.00 per 1M input tokens and $8.00 per 1M output tokens (based on AI21 Labs's API). For a blended rate (7:2:1 cache hit/input/output ratio), this is $2.60 per 1M tokens. Pricing may vary by provider.
No, Jamba 1.7 Large is not a reasoning model. It provides direct responses without extended chain-of-thought reasoning.
Jamba 1.7 Large supports text input.
Jamba 1.7 Large supports text output.
No, Jamba 1.7 Large does not support image input. It can only process text.
No, Jamba 1.7 Large is not multimodal. It only supports text input.
Jamba 1.7 Large has a context window of 260k tokens. This determines how much text and conversation history the model can process in a single request.
Yes, Jamba 1.7 Large is open weights. The model weights are publicly available and can be downloaded for self-hosting.
Jamba 1.7 Large has 398 billion parameters (94 billion active).
Jamba 1.7 Large is a Mixture of Experts (MoE) model with 398 billion total parameters, but only 94 billion active parameters are used during inference.
Jamba 1.7 Large is released under the Jamba Open Model License Agreement license. This license allows commercial use.
Jamba 1.7 Large achieves a score of 5 on the Artificial Analysis Intelligence Index. This composite benchmark evaluates models across reasoning, knowledge, mathematics, and coding.
Jamba 1.7 Large has a knowledge cutoff of August 2024. The model's training data includes information up to this date.
Yes, Jamba 1.7 Large is available via API through 1 provider.
Jamba 1.7 Large is available through 1 API provider.