Ember-1 vs Kimi K2-Thinking-0905
Ember-1 leads the LLM Stats Score 43.1 to 35.7. Kimi K2-Thinking-0905 is 7.0x cheaper per token.
Fireworks AI · Moonshot AI · Updated for 2026
Which is better?
Ember-1 leads the overall LLM Stats Score 43.1 to 35.7, 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, Kimi K2-Thinking-0905 is roughly 7.0x 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 coding and agents — 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 Kimi K2-Thinking-0905
- cost matters — it's about 7.0x 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 · 21 for Kimi K2-Thinking-0905
Ember-1 outperforms in 1 benchmarks (SWE-Bench Verified), while Kimi K2-Thinking-0905 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 6.4x more expensive than Kimi K2-Thinking-0905 ($0.47/1M tokens).
For output processing, Ember-1 ($15.00/1M tokens) is 7.5x more expensive than Kimi K2-Thinking-0905 ($2.00/1M tokens).
In conclusion, Ember-1 is more expensive than Kimi K2-Thinking-0905.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Ember-1 has 1780.0B more parameters than Kimi K2-Thinking-0905, making it 178.0% larger.
Context Window
Maximum input and output token capacity
Ember-1 accepts 1,040,000 input tokens compared to Kimi K2-Thinking-0905's 262,144 tokens. Only Kimi K2-Thinking-0905 specifies output context (262,144 tokens).
License
Usage and distribution terms
Ember-1 is licensed under a proprietary license, while Kimi K2-Thinking-0905 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 Kimi K2-Thinking-0905 was released on 2025-09-05.
Ember-1 is 13 months newer than Kimi K2-Thinking-0905.
Sep 23, 2026
2 weeks ago
1.0yr newerSep 5, 2025
1.1 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. Kimi K2-Thinking-0905 is available from DeepInfra, Novita, Fireworks.
Ember-1
Kimi K2-Thinking-0905
Outputs Comparison
Judge for yourself.
Run your own prompts against Ember-1 and Kimi K2-Thinking-0905 side-by-side, then vote on the output you prefer.
FAQ
Common questions about Ember-1 vs Kimi K2-Thinking-0905.