Model Comparison
Jamba 1.5 Large vs Qwen3 VL 4B InstructWhich is better in 2026?
Both models are evenly matched across the benchmarks. Qwen3 VL 4B Instruct is 15.6x cheaper per token.
Verdict: Jamba 1.5 Large vs Qwen3 VL 4B Instruct — which is better?
Jamba 1.5 Large (by AI21 Labs) and Qwen3 VL 4B Instruct (by Alibaba Cloud / Qwen Team) 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.
Jamba 1.5 Large outperforms in 1 benchmarks (MMLU), while Qwen3 VL 4B Instruct is better at 1 benchmark (MMLU-Pro). Both models are evenly matched across the benchmarks.
On price, Qwen3 VL 4B Instruct is roughly 15.6x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Qwen3 VL 4B Instruct also accepts a larger context window (262,144 input tokens), making it the stronger choice for long documents and large codebases.
Choose Jamba 1.5 Large if…
- you want predictable pricing at $2.00/M input and $8.00/M output
Choose Qwen3 VL 4B Instruct if…
- cost matters — it's about 15.6x cheaper per token
- you process long inputs — it offers a 262,144 token context window
- you want the most recent training data — it shipped Sep 2025
Performance Benchmarks
Comparative analysis across standard metrics
Jamba 1.5 Large outperforms in 1 benchmarks (MMLU), while Qwen3 VL 4B Instruct is better at 1 benchmark (MMLU-Pro).
Both models are evenly matched across the benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, Jamba 1.5 Large ($2.00/1M tokens) is 20.0x more expensive than Qwen3 VL 4B Instruct ($0.10/1M tokens).
For output processing, Jamba 1.5 Large ($8.00/1M tokens) is 13.3x more expensive than Qwen3 VL 4B Instruct ($0.60/1M tokens).
In conclusion, Jamba 1.5 Large is more expensive than Qwen3 VL 4B Instruct.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Jamba 1.5 Large has 394.0B more parameters than Qwen3 VL 4B Instruct, making it 9850.0% larger.
Context Window
Maximum input and output token capacity
Qwen3 VL 4B Instruct accepts 262,144 input tokens compared to Jamba 1.5 Large's 256,000 tokens. Qwen3 VL 4B Instruct can generate longer responses up to 262,144 tokens, while Jamba 1.5 Large is limited to 256,000 tokens.
Input Capabilities
Supported data types and modalities
Qwen3 VL 4B Instruct supports multimodal inputs, whereas Jamba 1.5 Large does not.
Qwen3 VL 4B Instruct can handle both text and other forms of data like images, making it suitable for multimodal applications.
Jamba 1.5 Large
Qwen3 VL 4B Instruct
License
Usage and distribution terms
Jamba 1.5 Large is licensed under Jamba Open Model License, while Qwen3 VL 4B Instruct uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
Jamba Open Model License
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
Jamba 1.5 Large was released on 2024-08-22, while Qwen3 VL 4B Instruct was released on 2025-09-22.
Qwen3 VL 4B Instruct is 13 months newer than Jamba 1.5 Large.
Aug 22, 2024
1.8 years ago
Sep 22, 2025
8 months ago
1.1yr newerKnowledge Cutoff
When training data ends
Jamba 1.5 Large has a documented knowledge cutoff of 2024-03-05, while Qwen3 VL 4B Instruct'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 Qwen3 VL 4B Instruct's cutoff date.
Mar 2024
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Provider Availability
Jamba 1.5 Large is available from Bedrock, Google. Qwen3 VL 4B Instruct is available from DeepInfra.
Jamba 1.5 Large
Qwen3 VL 4B Instruct
Outputs Comparison
Key Takeaways
Jamba 1.5 Large
View detailsAI21 Labs
Qwen3 VL 4B Instruct
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
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FAQ
Common questions about Jamba 1.5 Large vs Qwen3 VL 4B Instruct.