Compare AI models
| Specifications | ||
ParametersThe learned values that shape how a model responds. B means billion and T means trillion. For mixture-of-experts models, the total count is larger than the number active for each token. Parameter count alone does not measure quality. | 16B | — |
Context windowHow much information the model can work with in one request, measured in tokens — pieces of words or other data. This includes your question, attachments, conversation and reply. Limits can vary by model variant and provider. | 128k | 128k |
| Overview | ||
| Company | Moonshot AI | OpenAI |
| Release date | Apr 10 2025 | Feb 12 2026 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
Kimi-VLvsClaude Haiku 5.5GPT-5.3-Codex-SparkvsClaude Haiku 5.5Kimi-VLvsGemini 4 ArgonGPT-5.3-Codex-SparkvsGemini 4 ArgonKimi-VLvsMuse Spark OpenGPT-5.3-Codex-SparkvsMuse Spark OpenKimi-VLvsGrok 4.7GPT-5.3-Codex-SparkvsGrok 4.7Kimi-VLvsDeepSeek-V4.1-Flash-1017GPT-5.3-Codex-SparkvsDeepSeek-V4.1-Flash-1017Kimi-VLvsMistral Large 4GPT-5.3-Codex-SparkvsMistral Large 4Frequently asked questions
Kimi-VL and GPT-5.3-Codex-Spark don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Kimi-VL shipped 308 days before GPT-5.3-Codex-Spark, so benchmark comparisons should account for the intervening progress.
Context windows are 128k (Kimi-VL) vs 128k (GPT-5.3-Codex-Spark). Kimi-VL is open weight, while GPT-5.3-Codex-Spark is closed.
Direct benchmark comparisons are unavailable — Kimi-VL and GPT-5.3-Codex-Spark don't publish scores on any of the same benchmarks.