Model Comparison

DeepSeek-V3.1 vs Jamba 1.5 Mini

DeepSeek-V3.1 significantly outperforms across most benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

2 benchmarks

DeepSeek-V3.1 outperforms in 2 benchmarks (GPQA, MMLU-Pro), while Jamba 1.5 Mini is better at 0 benchmarks.

DeepSeek-V3.1 significantly outperforms across most benchmarks.

Tue Apr 14 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Cost data unavailable.

Lowest available price from all providers
Tue Apr 14 2026 • llm-stats.com
DeepSeek
DeepSeek-V3.1
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
AI21 Labs
Jamba 1.5 Mini
Input tokens$0.20
Output tokens$0.40
Best providerAWS Bedrock
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

619.0B diff

DeepSeek-V3.1 has 619.0B more parameters than Jamba 1.5 Mini, making it 1190.4% larger.

DeepSeek
DeepSeek-V3.1
671.0Bparameters
AI21 Labs
Jamba 1.5 Mini
52.0Bparameters
671.0B
DeepSeek-V3.1
52.0B
Jamba 1.5 Mini

Context Window

Maximum input and output token capacity

Only Jamba 1.5 Mini specifies input context (256,144 tokens). Only Jamba 1.5 Mini specifies output context (256,144 tokens).

DeepSeek
DeepSeek-V3.1
Input- tokens
Output- tokens
AI21 Labs
Jamba 1.5 Mini
Input256,144 tokens
Output256,144 tokens
Tue Apr 14 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-V3.1 is licensed under MIT, while Jamba 1.5 Mini uses Jamba Open Model License.

License differences may affect how you can use these models in commercial or open-source projects.

DeepSeek-V3.1

MIT

Open weights

Jamba 1.5 Mini

Jamba Open Model License

Open weights

Release Timeline

When each model was launched

DeepSeek-V3.1 was released on 2025-01-10, while Jamba 1.5 Mini was released on 2024-08-22.

DeepSeek-V3.1 is 5 months newer than Jamba 1.5 Mini.

DeepSeek-V3.1

Jan 10, 2025

1.3 years ago

4mo newer
Jamba 1.5 Mini

Aug 22, 2024

1.6 years ago

Knowledge Cutoff

When training data ends

Jamba 1.5 Mini has a documented knowledge cutoff of 2024-03-05, while DeepSeek-V3.1's cutoff date is not specified.

We can confirm Jamba 1.5 Mini's training data extends to 2024-03-05, but cannot make a direct comparison without DeepSeek-V3.1's cutoff date.

DeepSeek-V3.1

—

Jamba 1.5 Mini

Mar 2024

Outputs Comparison

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Key Takeaways

DeepSeek
Higher GPQA score (74.9% vs 32.3%)
Higher MMLU-Pro score (83.7% vs 42.5%)
AI21 Labs
Larger context window (256,144 tokens)

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V3.1
AI21 Labs
Jamba 1.5 Mini

FAQ

Common questions about DeepSeek-V3.1 vs Jamba 1.5 Mini

DeepSeek-V3.1 significantly outperforms across most benchmarks. DeepSeek-V3.1 is made by DeepSeek and Jamba 1.5 Mini is made by AI21 Labs. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.
DeepSeek-V3.1 scores SimpleQA: 93.4%, MMLU-Redux: 91.8%, MMLU-Pro: 83.7%, GPQA: 74.9%, CodeForces: 69.7%. Jamba 1.5 Mini scores ARC-C: 85.7%, GSM8k: 75.8%, MMLU: 69.7%, TruthfulQA: 54.1%, Arena Hard: 46.1%.
DeepSeek-V3.1 supports an unknown number of tokens and Jamba 1.5 Mini supports 256K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include licensing (MIT vs Jamba Open Model License). See the full comparison above for benchmark-by-benchmark results.
DeepSeek-V3.1 is developed by DeepSeek and Jamba 1.5 Mini is developed by AI21 Labs.