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GPT-4.1 nano vs Jamba 1.5 Large
Comprehensive side-by-side LLM comparison
GPT-4.1 nano leads with 6.1% higher average benchmark score. GPT-4.1 nano offers 568.3K more tokens in context window than Jamba 1.5 Large. GPT-4.1 nano is $9.50 cheaper per million tokens. GPT-4.1 nano supports multimodal inputs. Overall, GPT-4.1 nano is the stronger choice for coding tasks.
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OpenAI
GPT-4.1 Nano was developed as the smallest and most efficient variant in the GPT-4.1 family, designed for applications requiring minimal latency and resource usage. Built to enable AI capabilities on edge devices and resource-constrained environments, it distills GPT-4.1 capabilities into an ultra-compact form factor.
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AI21 Labs
Jamba 1.5 Large was developed by AI21 Labs using a hybrid architecture combining transformer and state space models, designed to provide efficient long-context understanding. Built to handle extended documents and conversations with computational efficiency, it represents AI21's innovation in efficient large-scale model design.
7 months newer
Jamba 1.5 Large
AI21 Labs
2024-08-22
GPT-4.1 nano
OpenAI
2025-04-14
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Pricing Comparison
Cost per million tokens (USD)
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GPT-4.1 nano
Input:$0.10
Output:$0.40($9.50 cheaper)
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Jamba 1.5 Large
Input:$2.00
Output:$8.00
Performance Metrics
Context window and performance specifications
Average performance across 2 common benchmarks
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GPT-4.1 nano
Average Score:65.2%(+6.1%)
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Jamba 1.5 Large
Average Score:59.1%
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Knowledge Cutoff
Training data recency comparison
Jamba 1.5 Large
2024-03-05
GPT-4.1 nano
2024-05-31
More recent knowledge cutoff means awareness of newer technologies and frameworks
Provider Availability & Performance
Available providers and their performance metrics
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GPT-4.1 nano
1 providers
OpenAI
Throughput: 200 tok/s
Latency: 2ms
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Jamba 1.5 Large
2 providers
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GPT-4.1 nano
Avg Score:65.2%(+6.1%)
Providers:1
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Jamba 1.5 Large
Avg Score:59.1%
Providers:2