Kimi K2 0905 vs Parse
Comparing Kimi K2 0905 and Parse across benchmarks, pricing, and capabilities.
Moonshot AI · Cohere · Updated for 2026
Which is better?
Kimi K2 0905 and Parse trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.
Kimi K2 0905 also accepts a larger context window (262,144 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 Kimi K2 0905
- you process long inputs — it offers a 262,144 token context window
Choose Parse
- you want the most recent training data — it shipped Aug 2026
At a glance
The differences that matter most.
Individual benchmarks
6 reported for Kimi K2 0905 · 1 for Parse
Kimi K2 0905 and Parsedon'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
Model Size
Parameter count comparison
Kimi K2 0905 has 997.7B more parameters than Parse, making it 43378.3% larger.
Context Window
Maximum input and output token capacity
Kimi K2 0905 accepts 262,144 input tokens compared to Parse's 8,192 tokens. Only Kimi K2 0905 specifies output context (262,144 tokens).
Input capabilities
Documented input modalities across available providers
Parse supports multimodal inputs, whereas Kimi K2 0905 does not.
Parse can handle both text and other forms of data like images, making it suitable for multimodal applications.
Kimi K2 0905
Parse
License
Usage and distribution terms
Both models are licensed under proprietary licenses.
Both models have usage restrictions defined by their respective organizations.
Proprietary
Closed source
Proprietary
Closed source
Release Timeline
When each model was launched
Kimi K2 0905 was released on 2025-09-05, while Parse was released on 2026-08-27.
Parse is 12 months newer than Kimi K2 0905.
Sep 5, 2025
12 months ago
Aug 27, 2026
1 weeks ago
11mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
Kimi K2 0905 is available from Novita. Parse is available from Azure, Cohere.
Kimi K2 0905
Parse
Outputs Comparison
Judge for yourself.
Run your own prompts against Kimi K2 0905 and Parse side-by-side, then vote on the output you prefer.
FAQ
Common questions about Kimi K2 0905 vs Parse.