Model Comparison

Comprehensive side-by-side analysis of model capabilities and performance

Jamba 1.5 Large

AI21 Labs

Jamba 1.5 Large is a language model developed by AI21 Labs. It achieves strong performance with an average score of 65.5% across 8 benchmarks. It excels particularly in ARC-C (93.0%), GSM8k (87.0%), MMLU (81.2%). It supports a 512K token context window for handling large documents. The model is available through 2 API providers. Released in 2024, it represents AI21 Labs's latest advancement in AI technology.

Meta

Llama 4 Maverick

Meta

Llama 4 Maverick is a multimodal language model developed by Meta. It achieves strong performance with an average score of 71.8% across 13 benchmarks. It excels particularly in DocVQA (94.4%), MGSM (92.3%), ChartQA (90.0%). The model shows particular specialization in vision tasks with an average performance of 75.8%. With a 2.0M token context window, it can handle extensive documents and complex multi-turn conversations. The model is available through 7 API providers. As a multimodal model, it can process and understand text, images, and other input formats seamlessly. Released in 2025, it represents Meta's latest advancement in AI technology.

Jamba 1.5 Large

AI21 Labs

2024-08-22

Meta

Llama 4 Maverick

Meta

2025-04-05

7 months newer

Pricing Comparison

Cost per million tokens (USD)

Jamba 1.5 Large

Input:$2.00
Output:$8.00
Meta

Llama 4 Maverick

$9.23 cheaper
Input:$0.17
Output:$0.60

Performance Metrics

Context window and performance specifications

Jamba 1.5 Large

Max Context:512.0K
Parameters:398.0B
Meta

Llama 4 Maverick

Larger context
Max Context:2.0M
Parameters:400.0B

Average performance across 3 common benchmarks

Jamba 1.5 Large

Average Score:57.2%
Meta

Llama 4 Maverick

+21.4%
Average Score:78.6%

Performance comparison across key benchmark categories

Jamba 1.5 Large

math
+11.3%
87.0%
general
57.1%
Meta

Llama 4 Maverick

math
75.7%
general
+14.4%
71.5%
Benchmark Scores - Detailed View
Side-by-side comparison of all benchmark scores
Knowledge Cutoff
Training data recency comparison

Jamba 1.5 Large

2024-03-05

More recent knowledge cutoff means awareness of newer technologies and frameworks

Provider Availability & Performance

Available providers and their performance metrics

Jamba 1.5 Large

2 providers

Google

Throughput: 42 tok/s
Latency: 0.3ms

Bedrock

Throughput: 100 tok/s
Latency: 0.5ms
Meta

Llama 4 Maverick

7 providers

Sambanova

Throughput: 638.7 tok/s
Latency: 2.04ms

Together

Throughput: 97.93 tok/s
Latency: 0.2ms

DeepInfra

Throughput: 83.59 tok/s
Latency: 0.38ms

Fireworks

Throughput: 63.03 tok/s
Latency: 0.62ms

Groq

Throughput: 307.3 tok/s
Latency: 0.27ms

Novita

Throughput: 69.42 tok/s
Latency: 0.62ms

Lambda

Throughput: 93.69 tok/s
Latency: 0.65ms

Jamba 1.5 Large

Avg Score:57.2%
Providers:2
Meta

Llama 4 Maverick

+21.4%
Avg Score:78.6%
Providers:7