Ember-1 vs Qwen3 VL 30B A3B Thinking
Ember-1 leads the LLM Stats Score 43.1 to 18.2. Qwen3 VL 30B A3B Thinking is 15.1x cheaper per token.
Fireworks AI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
Ember-1 leads the overall LLM Stats Score 43.1 to 18.2, ranking #51 overall.
On price, Qwen3 VL 30B A3B Thinking is roughly 15.1x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Ember-1 also accepts a larger context window (1,040,000 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 Ember-1
- overall performance matters — it scores 43.1 and ranks #51 on LLM Stats
- your work emphasizes reasoning and agents — it leads those capability indexes
- you process long inputs — it offers a 1,040,000 token context window
- you want the most recent training data — it shipped Sep 2026
Choose Qwen3 VL 30B A3B Thinking
- cost matters — it's about 15.1x cheaper per token
- you need open weights you can self-host or fine-tune
At a glance
The differences that matter most.
Individual benchmarks
4 reported for Ember-1 · 50 for Qwen3 VL 30B A3B Thinking
Ember-1 and Qwen3 VL 30B A3B Thinkingdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, Ember-1 ($3.00/1M tokens) is 15.0x more expensive than Qwen3 VL 30B A3B Thinking ($0.20/1M tokens).
For output processing, Ember-1 ($15.00/1M tokens) is 15.2x more expensive than Qwen3 VL 30B A3B Thinking ($0.99/1M tokens).
In conclusion, Ember-1 is more expensive than Qwen3 VL 30B A3B Thinking.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Ember-1 has 2749.0B more parameters than Qwen3 VL 30B A3B Thinking, making it 8867.7% larger.
Context Window
Maximum input and output token capacity
Ember-1 accepts 1,040,000 input tokens compared to Qwen3 VL 30B A3B Thinking's 131,072 tokens. Only Qwen3 VL 30B A3B Thinking specifies output context (32,768 tokens).
Input capabilities
Documented input modalities across available providers
Qwen3 VL 30B A3B Thinking supports multimodal inputs, whereas Ember-1 does not.
Qwen3 VL 30B A3B Thinking can handle both text and other forms of data like images, making it suitable for multimodal applications.
Ember-1
Qwen3 VL 30B A3B Thinking
License
Usage and distribution terms
Ember-1 is licensed under a proprietary license, while Qwen3 VL 30B A3B Thinking uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
Proprietary
Closed source
Apache 2.0
Open weights
Release Timeline
When each model was launched
Ember-1 was released on 2026-09-23, while Qwen3 VL 30B A3B Thinking was released on 2025-09-22.
Ember-1 is 12 months newer than Qwen3 VL 30B A3B Thinking.
Sep 23, 2026
2 weeks ago
1.0yr newerSep 22, 2025
1.0 years ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
Ember-1 is available from Fireworks. Qwen3 VL 30B A3B Thinking is available from Novita, DeepInfra.
Ember-1
Qwen3 VL 30B A3B Thinking
Outputs Comparison
Judge for yourself.
Run your own prompts against Ember-1 and Qwen3 VL 30B A3B Thinking side-by-side, then vote on the output you prefer.
FAQ
Common questions about Ember-1 vs Qwen3 VL 30B A3B Thinking.