DeepSeek-V2.5 vs Ember-1
Ember-1 leads the LLM Stats Score 43.1 to 8.1. DeepSeek-V2.5 is 34.3x cheaper per token.
DeepSeek · Fireworks AI · Updated for 2026
Which is better?
Ember-1 leads the overall LLM Stats Score 43.1 to 8.1, 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, DeepSeek-V2.5 is roughly 34.3x 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 DeepSeek-V2.5
- cost matters — it's about 34.3x cheaper per token
- you need open weights you can self-host or fine-tune
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
At a glance
The differences that matter most.
Individual benchmarks
15 reported for DeepSeek-V2.5 · 4 for Ember-1
DeepSeek-V2.5 outperforms in 0 benchmarks, while Ember-1 is better at 1 benchmark (SWE-Bench Verified).
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, DeepSeek-V2.5 ($0.14/1M tokens) is 21.4x cheaper than Ember-1 ($3.00/1M tokens).
For output processing, DeepSeek-V2.5 ($0.28/1M tokens) is 53.6x cheaper than Ember-1 ($15.00/1M tokens).
In conclusion, Ember-1 is more expensive than DeepSeek-V2.5.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Ember-1 has 2544.0B more parameters than DeepSeek-V2.5, making it 1078.0% larger.
Context Window
Maximum input and output token capacity
Ember-1 accepts 1,040,000 input tokens compared to DeepSeek-V2.5's 8,192 tokens. Only DeepSeek-V2.5 specifies output context (8,192 tokens).
License
Usage and distribution terms
DeepSeek-V2.5 is licensed under deepseek, while Ember-1 uses a proprietary license.
License differences may affect how you can use these models in commercial or open-source projects.
deepseek
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
DeepSeek-V2.5 was released on 2024-05-08, while Ember-1 was released on 2026-09-23.
Ember-1 is 29 months newer than DeepSeek-V2.5.
May 8, 2024
2.4 years ago
Sep 23, 2026
2 weeks ago
2.4yr newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
DeepSeek-V2.5 is available from DeepSeek, DeepInfra, Hyperbolic. Ember-1 is available from Fireworks.
DeepSeek-V2.5
Ember-1
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V2.5 and Ember-1 side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V2.5 vs Ember-1.