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. | 48B | — |
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. | 1M | 128k |
| Overview | ||
| Company | Moonshot AI | OpenAI |
| Release date | Oct 30 2025 | Feb 12 2026 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
Kimi LinearvsClaude Haiku 5.5GPT-5.3-Codex-SparkvsClaude Haiku 5.5Kimi LinearvsGemini 4 ArgonGPT-5.3-Codex-SparkvsGemini 4 ArgonKimi LinearvsMuse Spark OpenGPT-5.3-Codex-SparkvsMuse Spark OpenKimi LinearvsGrok 4.7GPT-5.3-Codex-SparkvsGrok 4.7Kimi LinearvsDeepSeek-V4.1-Flash-1017GPT-5.3-Codex-SparkvsDeepSeek-V4.1-Flash-1017Kimi LinearvsMistral Large 4GPT-5.3-Codex-SparkvsMistral Large 4Frequently asked questions
Kimi Linear 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 Linear shipped 105 days before GPT-5.3-Codex-Spark, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Kimi Linear) vs 128k (GPT-5.3-Codex-Spark). Kimi Linear is open weight, while GPT-5.3-Codex-Spark is closed.
Direct benchmark comparisons are unavailable — Kimi Linear and GPT-5.3-Codex-Spark don't publish scores on any of the same benchmarks.