Jamba 1.5 Large vs Qwen2.5 7B Instruct
Jamba 1.5 Large and Qwen2.5 7B Instruct are closely matched at 0.9 and 2.5 on the LLM Stats Score. Qwen2.5 7B Instruct is 11.7x cheaper per token.
AI21 Labs · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
Jamba 1.5 Large and Qwen2.5 7B Instruct are closely matched on the overall LLM Stats Score at 0.9 and 2.5.
The models split the 4 individual benchmarks reported for both models evenly.
On price, Qwen2.5 7B Instruct is roughly 11.7x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Jamba 1.5 Large also accepts a larger context window (256,000 input tokens), making it the stronger choice for long documents and large codebases.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose Jamba 1.5 Large
- you process long inputs — it offers a 256,000 token context window
Choose Qwen2.5 7B Instruct
- cost matters — it's about 11.7x cheaper per token
- you want the most recent training data — it shipped Sep 2024
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 · 14 for Qwen2.5 7B Instruct
Jamba 1.5 Large outperforms in 2 benchmarks (Arena Hard, GPQA), while Qwen2.5 7B Instruct is better at 2 benchmarks (GSM8k, MMLU-Pro).
Both models are evenly matched across the 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 6.7x more expensive than Qwen2.5 7B Instruct ($0.30/1M tokens).
For output processing, Jamba 1.5 Large ($8.00/1M tokens) is 26.7x more expensive than Qwen2.5 7B Instruct ($0.30/1M tokens).
In conclusion, Jamba 1.5 Large is more expensive than Qwen2.5 7B Instruct.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Jamba 1.5 Large has 390.4B more parameters than Qwen2.5 7B Instruct, making it 5130.0% larger.
Context Window
Maximum input and output token capacity
Jamba 1.5 Large accepts 256,000 input tokens compared to Qwen2.5 7B Instruct's 131,072 tokens. Jamba 1.5 Large can generate longer responses up to 256,000 tokens, while Qwen2.5 7B Instruct is limited to 8,192 tokens.
License
Usage and distribution terms
Jamba 1.5 Large is licensed under Jamba Open Model License, while Qwen2.5 7B 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 Qwen2.5 7B Instruct was released on 2024-09-19.
Qwen2.5 7B Instruct is 1 month newer than Jamba 1.5 Large.
Aug 22, 2024
2.1 years ago
Sep 19, 2024
2.0 years ago
4w newerKnowledge Cutoff
When training data ends
Jamba 1.5 Large has a documented knowledge cutoff of 2024-03-05, while Qwen2.5 7B 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 Qwen2.5 7B Instruct's cutoff date.
Mar 2024
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Provider Availability
Jamba 1.5 Large is available from Bedrock, Google. Qwen2.5 7B Instruct is available from Together.
Jamba 1.5 Large
Qwen2.5 7B Instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against Jamba 1.5 Large and Qwen2.5 7B Instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about Jamba 1.5 Large vs Qwen2.5 7B Instruct.