Model Comparison

DeepSeek-V3 vs Jamba 1.5 Large

DeepSeek-V3 significantly outperforms across most benchmarks. DeepSeek-V3 is 7.3x cheaper per token.

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Performance Benchmarks

Comparative analysis across standard metrics

3 benchmarks

DeepSeek-V3 outperforms in 3 benchmarks (GPQA, MMLU, MMLU-Pro), while Jamba 1.5 Large is better at 0 benchmarks.

DeepSeek-V3 significantly outperforms across most benchmarks.

Sat Apr 04 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

DeepSeek-V3 costs less

For input processing, DeepSeek-V3 ($0.27/1M tokens) is 7.4x cheaper than Jamba 1.5 Large ($2.00/1M tokens).

For output processing, DeepSeek-V3 ($1.10/1M tokens) is 7.3x cheaper than Jamba 1.5 Large ($8.00/1M tokens).

In conclusion, Jamba 1.5 Large is more expensive than DeepSeek-V3.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers
Sat Apr 04 2026 • llm-stats.com
DeepSeek
DeepSeek-V3
Input tokens$0.27
Output tokens$1.10
Best providerDeepSeek
AI21 Labs
Jamba 1.5 Large
Input tokens$2.00
Output tokens$8.00
Best providerAWS Bedrock
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

273.0B diff

DeepSeek-V3 has 273.0B more parameters than Jamba 1.5 Large, making it 68.6% larger.

DeepSeek
DeepSeek-V3
671.0Bparameters
AI21 Labs
Jamba 1.5 Large
398.0Bparameters
671.0B
DeepSeek-V3
398.0B
Jamba 1.5 Large

Context Window

Maximum input and output token capacity

Jamba 1.5 Large accepts 256,000 input tokens compared to DeepSeek-V3's 131,072 tokens. Jamba 1.5 Large can generate longer responses up to 256,000 tokens, while DeepSeek-V3 is limited to 131,072 tokens.

DeepSeek
DeepSeek-V3
Input131,072 tokens
Output131,072 tokens
AI21 Labs
Jamba 1.5 Large
Input256,000 tokens
Output256,000 tokens
Sat Apr 04 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-V3 is licensed under MIT + Model License (Commercial use allowed), 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.

DeepSeek-V3

MIT + Model License (Commercial use allowed)

Open weights

Jamba 1.5 Large

Jamba Open Model License

Open weights

Release Timeline

When each model was launched

DeepSeek-V3 was released on 2024-12-25, while Jamba 1.5 Large was released on 2024-08-22.

DeepSeek-V3 is 4 months newer than Jamba 1.5 Large.

DeepSeek-V3

Dec 25, 2024

1.3 years ago

4mo newer
Jamba 1.5 Large

Aug 22, 2024

1.6 years ago

Knowledge Cutoff

When training data ends

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

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

DeepSeek-V3

—

Jamba 1.5 Large

Mar 2024

Provider Availability

DeepSeek-V3 is available from DeepSeek. Jamba 1.5 Large is available from Bedrock, Google.

DeepSeek-V3

deepseek logo
DeepSeek
Input Price:Input: $0.27/1MOutput Price:Output: $1.10/1M

Jamba 1.5 Large

bedrock logo
AWS Bedrock
Input Price:Input: $2.00/1MOutput Price:Output: $8.00/1M
google logo
Google
Input Price:Input: $2.00/1MOutput Price:Output: $8.00/1M
* Prices shown are per million tokens

Outputs Comparison

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Key Takeaways

DeepSeek
Less expensive input tokens
Less expensive output tokens
Higher GPQA score (59.1% vs 36.9%)
Higher MMLU score (88.5% vs 81.2%)
Higher MMLU-Pro score (75.9% vs 53.5%)
AI21 Labs
Larger context window (256,000 tokens)

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V3
AI21 Labs
Jamba 1.5 Large

FAQ

Common questions about DeepSeek-V3 vs Jamba 1.5 Large

DeepSeek-V3 significantly outperforms across most benchmarks. DeepSeek-V3 is made by DeepSeek 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.
DeepSeek-V3 scores DROP: 91.6%, CLUEWSC: 90.9%, MATH-500: 90.2%, MMLU-Redux: 89.1%, MMLU: 88.5%. Jamba 1.5 Large scores ARC-C: 93.0%, GSM8k: 87.0%, MMLU: 81.2%, Arena Hard: 65.4%, TruthfulQA: 58.3%.
DeepSeek-V3 is 7.4x cheaper for input tokens. DeepSeek-V3 costs $0.27/M input and $1.10/M output via deepseek. Jamba 1.5 Large costs $2.00/M input and $8.00/M output via bedrock.
DeepSeek-V3 supports 131K 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.
Key differences include context window (131K vs 256K), input pricing ($0.27 vs $2.00/M), licensing (MIT + Model License (Commercial use allowed) vs Jamba Open Model License). See the full comparison above for benchmark-by-benchmark results.
DeepSeek-V3 is developed by DeepSeek and Jamba 1.5 Large is developed by AI21 Labs.