Model Comparison

Gemini 2.0 Flash Thinking vs Jamba 1.5 Large

Gemini 2.0 Flash Thinking significantly outperforms across most benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

1 benchmarks

Gemini 2.0 Flash Thinking outperforms in 1 benchmarks (GPQA), while Jamba 1.5 Large is better at 0 benchmarks.

Gemini 2.0 Flash Thinking significantly outperforms across most benchmarks.

Sat May 02 2026 • llm-stats.com

Arena Performance

Human preference votes

Context Window

Maximum input and output token capacity

Only Jamba 1.5 Large specifies input context (256,000 tokens). Only Jamba 1.5 Large specifies output context (256,000 tokens).

Google
Gemini 2.0 Flash Thinking
Input- tokens
Output- tokens
AI21 Labs
Jamba 1.5 Large
Input256,000 tokens
Output256,000 tokens
Sat May 02 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Gemini 2.0 Flash Thinking supports multimodal inputs, whereas Jamba 1.5 Large does not.

Gemini 2.0 Flash Thinking can handle both text and other forms of data like images, making it suitable for multimodal applications.

Gemini 2.0 Flash Thinking

Text
Images
Audio
Video

Jamba 1.5 Large

Text
Images
Audio
Video

License

Usage and distribution terms

Gemini 2.0 Flash Thinking is licensed under a proprietary license, while Jamba 1.5 Large uses Jamba Open Model License.

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

Gemini 2.0 Flash Thinking

Proprietary

Closed source

Jamba 1.5 Large

Jamba Open Model License

Open weights

Release Timeline

When each model was launched

Gemini 2.0 Flash Thinking was released on 2025-01-21, while Jamba 1.5 Large was released on 2024-08-22.

Gemini 2.0 Flash Thinking is 5 months newer than Jamba 1.5 Large.

Gemini 2.0 Flash Thinking

Jan 21, 2025

1.3 years ago

5mo newer
Jamba 1.5 Large

Aug 22, 2024

1.7 years ago

Knowledge Cutoff

When training data ends

Gemini 2.0 Flash Thinking has a knowledge cutoff of 2024-08-01, while Jamba 1.5 Large has a cutoff of 2024-03-05.

Gemini 2.0 Flash Thinking has more recent training data (up to 2024-08-01), making it potentially better informed about events through that date compared to Jamba 1.5 Large (2024-03-05).

Gemini 2.0 Flash Thinking

Aug 2024

5 mo newer
Jamba 1.5 Large

Mar 2024

Outputs Comparison

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Key Takeaways

Google
Supports multimodal inputs
Higher GPQA score (74.2% vs 36.9%)
AI21 Labs
Larger context window (256,000 tokens)
Has open weights

Detailed Comparison

AI Model Comparison Table
Feature
Google
Gemini 2.0 Flash Thinking
AI21 Labs
Jamba 1.5 Large

FAQ

Common questions about Gemini 2.0 Flash Thinking vs Jamba 1.5 Large.

Which is better, Gemini 2.0 Flash Thinking or Jamba 1.5 Large?

Gemini 2.0 Flash Thinking significantly outperforms across most benchmarks. Gemini 2.0 Flash Thinking is made by Google and Jamba 1.5 Large is made by AI21 Labs. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does Gemini 2.0 Flash Thinking compare to Jamba 1.5 Large in benchmarks?

Gemini 2.0 Flash Thinking scores MMMU: 75.4%, GPQA: 74.2%, AIME 2024: 73.3%. Jamba 1.5 Large scores ARC-C: 93.0%, GSM8k: 87.0%, MMLU: 81.2%, Arena Hard: 65.4%, TruthfulQA: 58.3%.

What are the context window sizes for Gemini 2.0 Flash Thinking and Jamba 1.5 Large?

Gemini 2.0 Flash Thinking supports an unknown number of tokens and Jamba 1.5 Large supports 256K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between Gemini 2.0 Flash Thinking and Jamba 1.5 Large?

Key differences include multimodal support (yes vs no), licensing (Proprietary vs Jamba Open Model License). See the full comparison above for benchmark-by-benchmark results.

Who makes Gemini 2.0 Flash Thinking and Jamba 1.5 Large?

Gemini 2.0 Flash Thinking is developed by Google and Jamba 1.5 Large is developed by AI21 Labs.