Skip to main content
← Back to Models
⚖️

Jamba 1.5 LargevsGPT-o3

AI21 Labs vs OpenAI — Side-by-side model comparison

Jamba 1.5 Large leads 2/5 categories

Head-to-Head Comparison

MetricJamba 1.5 LargeGPT-o3
Provider
Arena Rank
#2
Context Window
256K
200K
Input Pricing
$2.00/1M tokens
$2.00/1M tokens
Output Pricing
$8.00/1M tokens
$8.00/1M tokens
Parameters
398B (94B active)
Undisclosed
Open Source
Yes
No
Best For
Long documents, enterprise RAG, analysis
Advanced reasoning, agentic tasks, research
Release Date
Aug 22, 2024
Apr 16, 2025

Jamba 1.5 Large

Jamba 1.5 Large, developed by AI21 Labs, is a hybrid model combining the Mamba state-space architecture with traditional Transformer layers, featuring 398 billion total parameters (94 billion active) and a 256K token context window. The novel SSM-Transformer design enables efficient processing of very long sequences while maintaining strong performance on reasoning and generation tasks. The architecture offers better throughput than pure Transformer models at equivalent quality, reducing inference costs for long-context workloads. Priced at $2.00 per million input tokens and $8.00 per million output tokens. As an open-source model, it can be self-hosted for enterprise deployments. Jamba 1.5 Large demonstrates that architectural diversity beyond the dominant Transformer paradigm can yield practical advantages, particularly for applications requiring processing of lengthy legal, scientific, or financial documents.

View AI21 Labs profile →

GPT-o3

GPT-o3 is OpenAI's most advanced reasoning model, succeeding o1 as the frontier of deliberative AI. It uses an enhanced chain-of-thought approach where the model spends more compute time 'thinking' before responding, dramatically improving performance on complex STEM, mathematical, and logical reasoning tasks. With a 200K token context window and the ability to use tools during reasoning, o3 represents a significant leap in AI problem-solving capabilities. It achieved state-of-the-art results on the ARC-AGI benchmark, demonstrating near-human performance on novel reasoning challenges. The model is particularly strong at multi-step mathematical proofs, complex code debugging, and scientific analysis where careful step-by-step reasoning is essential. Originally priced at a premium, an 80% price reduction in June 2025 made o3 accessible to a much broader range of developers and applications.

View OpenAI profile →

Key Differences: Jamba 1.5 Large vs GPT-o3

1

Jamba 1.5 Large supports a larger context window (256K), allowing it to process longer documents in a single request.

2

Jamba 1.5 Large is open-source (free to self-host and fine-tune) while GPT-o3 is proprietary (API-only access).

J

When to use Jamba 1.5 Large

  • +You need to process long documents (256K context)
  • +You need to self-host or fine-tune the model
  • +Your use case involves long documents, enterprise rag, analysis
View full Jamba 1.5 Large specs →
G

When to use GPT-o3

  • +You prefer a managed API without infrastructure overhead
  • +Your use case involves advanced reasoning, agentic tasks, research
View full GPT-o3 specs →

Cost Analysis

Both models have similar pricing. For a typical enterprise workload processing 100M tokens per month:

Jamba 1.5 Large monthly cost

$500

100M tokens/mo (50/50 in/out)

GPT-o3 monthly cost

$500

100M tokens/mo (50/50 in/out)

The Verdict

Jamba 1.5 Large wins our head-to-head comparison with 2 out of 5 category wins. It's the stronger choice for long documents, enterprise rag, analysis, though GPT-o3 holds an edge in advanced reasoning, agentic tasks, research.

Last compared: April 2026 · Data sourced from public benchmarks and official pricing pages

Frequently Asked Questions

Which is better, Jamba 1.5 Large or GPT-o3?
In our head-to-head comparison, Jamba 1.5 Large leads in 2 out of 5 categories (arena rank, context window, input pricing, output pricing, and parameters). Jamba 1.5 Large excels at long documents, enterprise rag, analysis, while GPT-o3 is better suited for advanced reasoning, agentic tasks, research. The best choice depends on your specific requirements, budget, and use case.
How does Jamba 1.5 Large pricing compare to GPT-o3?
Jamba 1.5 Large charges $2.00 per 1M input tokens and $8.00 per 1M output tokens. GPT-o3 charges $2.00 per 1M input tokens and $8.00 per 1M output tokens. For high-volume production workloads, the pricing difference can significantly impact total cost of ownership.
What is the context window difference between Jamba 1.5 Large and GPT-o3?
Jamba 1.5 Large supports a 256K token context window, while GPT-o3 supports 200K tokens. Jamba 1.5 Large can process longer documents, codebases, and conversations in a single request. Context window size matters most for tasks involving long documents, large codebases, or extended conversations.
Can I use Jamba 1.5 Large or GPT-o3 for free?
Jamba 1.5 Large is a paid API model starting at $2.00 per 1M input tokens. GPT-o3 is a paid API model starting at $2.00 per 1M input tokens. Open-source models can be self-hosted for free but require your own GPU infrastructure.
Which model has better benchmarks, Jamba 1.5 Large or GPT-o3?
Jamba 1.5 Large's arena rank is not yet available, while GPT-o3 holds rank #2. Note that benchmarks don't capture every use case — we recommend testing both models on your specific tasks.
Is Jamba 1.5 Large or GPT-o3 better for coding?
Jamba 1.5 Large's primary strength is long documents, enterprise rag, analysis. GPT-o3's primary strength is advanced reasoning, agentic tasks, research. For coding specifically, arena rank and code-specific benchmarks are the best indicators of performance.