Model Comparison

DeepSeek-V3 vs Jamba 1.5 LargeWhich is better in 2026?

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

Verdict: DeepSeek-V3 vs Jamba 1.5 Large — which is better?

DeepSeek-V3 (by DeepSeek) and Jamba 1.5 Large (by AI21 Labs) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.

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.

On price, DeepSeek-V3 is roughly 7.3x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

Jamba 1.5 Large also accepts a larger context window (256,000 input tokens), making it the stronger choice for long documents and large codebases.

Choose DeepSeek-V3 if…

  • you want the strongest raw capability — it leads on 3 of 3 shared benchmarks
  • cost matters — it's about 7.3x cheaper per token
  • you want the most recent training data — it shipped Dec 2024

Choose Jamba 1.5 Large if…

  • you process long inputs — it offers a 256,000 token context window

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.

Fri Jul 03 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
Fri Jul 03 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
Fri Jul 03 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

1.5 years ago

4mo newer
Jamba 1.5 Large

Aug 22, 2024

1.9 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

Notice missing or incorrect data?Start an Issue discussion→

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

Interactive Arena

Judge for yourself.

Run your own prompts against DeepSeek-V3 and Jamba 1.5 Large side-by-side, then vote on the output you prefer.

DeepSeek-V3
✓ Preferred
Jamba 1.5 Large
Open in Playground
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.

Which is better, DeepSeek-V3 or 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.

How does DeepSeek-V3 compare to Jamba 1.5 Large in benchmarks?

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%.

Is DeepSeek-V3 cheaper than Jamba 1.5 Large?

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.

What are the context window sizes for DeepSeek-V3 and Jamba 1.5 Large?

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.

What are the main differences between DeepSeek-V3 and Jamba 1.5 Large?

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.

Who makes DeepSeek-V3 and Jamba 1.5 Large?

DeepSeek-V3 is developed by DeepSeek and Jamba 1.5 Large is developed by AI21 Labs.

Related comparisons