Jamba 1.5 Large vs Qwen3.8 Max
Qwen3.8 Max leads the LLM Stats Score 51.9 to 0.9. Qwen3.8 Max is 1.4x cheaper per token.
AI21 Labs · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
Qwen3.8 Max leads the overall LLM Stats Score 51.9 to 0.9, ranking #11 overall.
In the 1 individual benchmarks reported for both models, Qwen3.8 Max wins 1; this is a narrower head-to-head signal than the composite indexes.
On price, Qwen3.8 Max is roughly 1.4x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose Jamba 1.5 Large
- you want predictable pricing at $2.00/M input and $8.00/M output
Choose Qwen3.8 Max
- overall performance matters — it scores 51.9 and ranks #11 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 1 of 1 exact shared results
- cost matters — it's about 1.4x cheaper per token
- you want the most recent training data — it shipped Aug 2026
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
8 reported for Jamba 1.5 Large · 42 for Qwen3.8 Max
Jamba 1.5 Large outperforms in 0 benchmarks, while Qwen3.8 Max is better at 1 benchmark (GPQA).
Qwen3.8 Max significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, Jamba 1.5 Large ($2.00/1M tokens) is 1.2x more expensive than Qwen3.8 Max ($1.65/1M tokens).
For output processing, Jamba 1.5 Large ($8.00/1M tokens) is 1.6x more expensive than Qwen3.8 Max ($4.95/1M tokens).
In conclusion, Jamba 1.5 Large is more expensive than Qwen3.8 Max.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Qwen3.8 Max has 2002.0B more parameters than Jamba 1.5 Large, making it 503.0% larger.
Context Window
Maximum input and output token capacity
Both models have the same input context window of 256,000 tokens. Both models can generate responses up to 256,000 tokens.
Input capabilities
Documented input modalities across available providers
Qwen3.8 Max supports multimodal inputs, whereas Jamba 1.5 Large does not.
Qwen3.8 Max can handle both text and other forms of data like images, making it suitable for multimodal applications.
Jamba 1.5 Large
Qwen3.8 Max
License
Usage and distribution terms
Jamba 1.5 Large is licensed under Jamba Open Model License, while Qwen3.8 Max uses Qwen3.8-Max License.
License differences may affect how you can use these models in commercial or open-source projects.
Jamba Open Model License
Open weights
Qwen3.8-Max License
Open weights
Release Timeline
When each model was launched
Jamba 1.5 Large was released on 2024-08-22, while Qwen3.8 Max was released on 2026-08-02.
Qwen3.8 Max is 24 months newer than Jamba 1.5 Large.
Aug 22, 2024
2.1 years ago
Aug 2, 2026
1 months ago
1.9yr newerKnowledge Cutoff
When training data ends
Jamba 1.5 Large has a documented knowledge cutoff of 2024-03-05, while Qwen3.8 Max'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 Qwen3.8 Max's cutoff date.
Mar 2024
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Provider Availability
Jamba 1.5 Large is available from Bedrock, Google. Qwen3.8 Max is available from DeepInfra, Fireworks, Novita, Together.
Jamba 1.5 Large
Qwen3.8 Max
Outputs Comparison
Judge for yourself.
Run your own prompts against Jamba 1.5 Large and Qwen3.8 Max side-by-side, then vote on the output you prefer.
FAQ
Common questions about Jamba 1.5 Large vs Qwen3.8 Max.