Kimi K2vsGPT-5.3-Codex
Kimi K2 | GPT-5.3-Codex | |
|---|---|---|
| Specifications | ||
ParametersA rough measure of how big the model is. More parameters usually means more capable and more expensive to run, though it is a poor guide on its own — a smaller, newer model often beats a larger, older one. | 1T | — |
Context windowHow much text the model can hold in mind at once — your question, any documents you attach, the conversation so far, and its own reply. Go past it and the earliest part falls out of view. | 128k | — |
| API pricingUSD per 1M tokens · lower wins | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | — | $1.75 |
Output priceWhat you pay for the text the model writes back. It is normally the dearer half: producing an answer costs more than reading one. | — | $14.00 |
Cached input priceA reduced rate for text you send over and over. If every request starts with the same instructions or the same document, the provider keeps a copy ready and charges less to read it again. | — | $0.175 |
Cheapest inputLowest input rate across third-party providers, excluding the lab itself. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | $0.57Novita | $1.75Azure |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | $2.30Novita | $14.00Azure |
| Benchmarks | ||
BullshitBench v2Nonsense detection — Given a confidently-worded but nonsensical prompt, does the AI spot that it makes no sense and push back — instead of playing along and inventing an answer? The score is how often it clearly called out the nonsense. Higher is better. | 10% | 24% |
SWE-Bench VerifiedCoding — Real coding tasks pulled from open-source projects — the AI has to find and fix actual bugs. A human-checked version of the original SWE-Bench. Higher is better. | 65.8% | 85% |
| BenchmarksPublished by one model only | ||
Next.js EvalsNext.js coding — Vercel's open eval of how well AI coding agents build and migrate real Next.js apps — measured as the share of tasks the agent completes successfully. Higher is better. | — | 81% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 75.1% | — |
| Overview | ||
| Company | Moonshot AI | OpenAI |
| Release date | Jul 11 2025 | Feb 5 2026 |
| Access | Open Weight | Proprietary |
Other comparisons
Frequently asked questions
GPT-5.3-Codex leads Kimi K2 on 2 of the 2 benchmarks they both report (BullshitBench v2, SWE-Bench Verified). Only GPT-5.3-Codex has a verified first-party API price: $1.75 per million input tokens and $14.00 per million output tokens. No pay-as-you-go API rate is tracked for Kimi K2. Kimi K2 shipped 209 days before GPT-5.3-Codex, so benchmark comparisons should account for the intervening progress.
Kimi K2 is open weight, while GPT-5.3-Codex is proprietary.
On BullshitBench v2, GPT-5.3-Codex leads at 24% vs Kimi K2 at 10%. On SWE-Bench Verified, GPT-5.3-Codex leads at 85% vs Kimi K2 at 65.8%.
Kimi K2 was released by Moonshot AI on Jul 11 2025.
GPT-5.3-Codex was released by OpenAI on Feb 5 2026.
GPT-5.3-Codex leads on SWE-Bench Verified — Kimi K2 65.8% vs GPT-5.3-Codex 85%.
Only GPT-5.3-Codex has a verified first-party API price: $1.75 per million input tokens and $14.00 per million output tokens. No pay-as-you-go API rate is tracked for Kimi K2. Rates are pay-as-you-go API prices verified on August 18, 2026.
Kimi K2 is an open weight model released by Moonshot AI. GPT-5.3-Codex is a proprietary model released by OpenAI.