Ember-1 vs Qwen3-235B-A22B-Instruct-2507
Ember-1 leads the LLM Stats Score 43.1 to 24.1. Qwen3-235B-A22B-Instruct-2507 is 29.3x 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 24.1, ranking #51 overall.
In the 1 individual benchmarks reported for both models, Ember-1 wins 1; this is a narrower head-to-head signal than the composite indexes.
On price, Qwen3-235B-A22B-Instruct-2507 is roughly 29.3x 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 coding — it leads those capability indexes
- you value its reported benchmark strengths — it wins 1 of 1 exact shared results
- 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 Qwen3-235B-A22B-Instruct-2507
- cost matters — it's about 29.3x cheaper per token
- you need open weights you can self-host or fine-tune
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
4 reported for Ember-1 · 25 for Qwen3-235B-A22B-Instruct-2507
Ember-1 outperforms in 1 benchmarks (Tau2 Airline), while Qwen3-235B-A22B-Instruct-2507 is better at 0 benchmarks.
Ember-1 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, Ember-1 ($3.00/1M tokens) is 33.3x more expensive than Qwen3-235B-A22B-Instruct-2507 ($0.09/1M tokens).
For output processing, Ember-1 ($15.00/1M tokens) is 27.3x more expensive than Qwen3-235B-A22B-Instruct-2507 ($0.55/1M tokens).
In conclusion, Ember-1 is more expensive than Qwen3-235B-A22B-Instruct-2507.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Ember-1 has 2545.0B more parameters than Qwen3-235B-A22B-Instruct-2507, making it 1083.0% larger.
Context Window
Maximum input and output token capacity
Ember-1 accepts 1,040,000 input tokens compared to Qwen3-235B-A22B-Instruct-2507's 262,144 tokens. Only Qwen3-235B-A22B-Instruct-2507 specifies output context (262,144 tokens).
License
Usage and distribution terms
Ember-1 is licensed under a proprietary license, while Qwen3-235B-A22B-Instruct-2507 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 Qwen3-235B-A22B-Instruct-2507 was released on 2025-07-22.
Ember-1 is 14 months newer than Qwen3-235B-A22B-Instruct-2507.
Sep 23, 2026
2 weeks ago
1.2yr newerJul 22, 2025
1.2 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. Qwen3-235B-A22B-Instruct-2507 is available from DeepInfra, Fireworks, Novita.
Ember-1
Qwen3-235B-A22B-Instruct-2507
Outputs Comparison
Judge for yourself.
Run your own prompts against Ember-1 and Qwen3-235B-A22B-Instruct-2507 side-by-side, then vote on the output you prefer.
FAQ
Common questions about Ember-1 vs Qwen3-235B-A22B-Instruct-2507.