Ember-1 vs GLM-4.7
Ember-1 leads the LLM Stats Score 43.1 to 34.2. GLM-4.7 is 8.1x cheaper per token.
Fireworks AI · Zhipu AI · Updated for 2026
Which is better?
Ember-1 leads the overall LLM Stats Score 43.1 to 34.2, 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, GLM-4.7 is roughly 8.1x 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 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 GLM-4.7
- cost matters — it's about 8.1x 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 · 13 for GLM-4.7
Ember-1 outperforms in 1 benchmarks (SWE-Bench Verified), while GLM-4.7 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 7.5x more expensive than GLM-4.7 ($0.40/1M tokens).
For output processing, Ember-1 ($15.00/1M tokens) is 8.6x more expensive than GLM-4.7 ($1.75/1M tokens).
In conclusion, Ember-1 is more expensive than GLM-4.7.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Ember-1 has 2422.0B more parameters than GLM-4.7, making it 676.5% larger.
Context Window
Maximum input and output token capacity
Ember-1 accepts 1,040,000 input tokens compared to GLM-4.7's 202,752 tokens. Only GLM-4.7 specifies output context (202,752 tokens).
Input capabilities
Documented input modalities across available providers
GLM-4.7 supports multimodal inputs, whereas Ember-1 does not.
GLM-4.7 can handle both text and other forms of data like images, making it suitable for multimodal applications.
Ember-1
GLM-4.7
License
Usage and distribution terms
Ember-1 is licensed under a proprietary license, while GLM-4.7 uses MIT.
License differences may affect how you can use these models in commercial or open-source projects.
Proprietary
Closed source
MIT
Open weights
Release Timeline
When each model was launched
Ember-1 was released on 2026-09-23, while GLM-4.7 was released on 2025-12-22.
Ember-1 is 9 months newer than GLM-4.7.
Sep 23, 2026
2 weeks ago
9mo newerDec 22, 2025
9 months 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. GLM-4.7 is available from DeepInfra, Fireworks, Novita.
Ember-1
GLM-4.7
Outputs Comparison
Judge for yourself.
Run your own prompts against Ember-1 and GLM-4.7 side-by-side, then vote on the output you prefer.
FAQ
Common questions about Ember-1 vs GLM-4.7.