Kimi K2 Thinking vs GPT-5.2-Codex
Moonshot AIOpenAIboth via one key, provider price + 3%
Kimi K2 Thinking (Moonshot AI, released Nov 6, 2025) and GPT-5.2-Codex (OpenAI, released Jan 14, 2026) both run mandatory reasoning, but they're built for different jobs. Kimi K2 Thinking is a trillion-parameter MoE reasoning model priced for volume. GPT-5.2-Codex is OpenAI's coding-agent model with image input and a bigger context window. The price gap is real — input tokens on GPT-5.2-Codex cost roughly 3x more, output tokens nearly 5.6x more. Both are available on OpenKey with one key and a flat 3% fee on top of provider list price.
Spec vs spec
| Spec | Kimi K2 Thinking | GPT-5.2-Codex |
|---|---|---|
| Context window | 262K | 400K |
| Max output | 100K | 128K |
| Input modalities | text | text, image |
| Output modalities | text | text |
| Released | Nov 6, 2025 | Jan 14, 2026 |
| Reasoning | always on | always on |
Pricing
Per 1M tokens. Provider price plus the flat 3% fee — the sum is what you pay.
moonshotai/kimi-k2-thinking
Input · 1M tokens
$0.600 + 3%$0.618
Output · 1M tokens
$2.50 + 3%$2.58
Cache read · 1M tokens
$0.150 + 3%$0.154
FEE — FLAT, EVERY MODEL3%
openai/gpt-5.2-codex
Input · 1M tokens
$1.75 + 3%$1.80
Output · 1M tokens
$14.00 + 3%$14.42
Cache read · 1M tokens
$0.175 + 3%$0.180
FEE — FLAT, EVERY MODEL3%
One workload, priced on both
10M input + 2M output tokens at each model's price, flat 3% fee included.
moonshotai/kimi-k2-thinkingCheaper
$11.33
$11.00 provider + 3%
openai/gpt-5.2-codex
$46.87
$45.50 provider + 3%
Pricing math on a real workload
Provider list price for Kimi K2 Thinking is $0.60/M input, $2.50/M output, with cache reads at $0.15/M. On OpenKey that's $0.60 x 1.03 = $0.618/M input and $2.50 x 1.03 = $2.575/M output. GPT-5.2-Codex lists at $1.75/M input and $14.00/M output, which becomes $1.75 x 1.03 = $1.8025/M input and $14.00 x 1.03 = $14.42/M output on OpenKey.
Run the numbers on a 10M-input / 2M-output workload: Kimi K2 Thinking costs $11.00 total, GPT-5.2-Codex costs $45.50 — GPT-5.2-Codex is about 4.1x more expensive for that mix. The input price ratio alone is 0.34 (Kimi K2 Thinking's input price is 34% of GPT-5.2-Codex's). If you're running large batch reasoning jobs, that gap compounds fast.
Coding and agent benchmarks
GPT-5.2-Codex has design-arena agent scores across five categories: Godot game dev (elo 1187, rank 12), Android native (elo 1176, rank 15), mobile apps (elo 1172, rank 24), web apps (elo 1125, rank 22), and full-stack (elo 1060, rank 27). That's a model built and measured specifically for agentic coding tasks.
Kimi K2 Thinking's only reported design-arena score is website generation (elo 1156, rank 70, 48.8% win rate) — a single-category, mid-pack result. It also reports artificial_analysis scores: intelligence index 17.3, coding index 21, agentic index 1.8. Those numbers exist for Kimi K2 Thinking but not for GPT-5.2-Codex in this data, so they aren't directly comparable across models — but on the metric both models share (design-arena agent categories), GPT-5.2-Codex's rank-12-to-27 spread beats Kimi's rank-70 website score.
Context and output limits
GPT-5.2-Codex has a 400,000-token context window and a 128,000-token max completion. Kimi K2 Thinking has a 262,144-token context window and a 100,352-token max completion — the context ratio is 0.66, meaning Kimi's window is about two-thirds the size of GPT-5.2-Codex's. For most single-session coding or reasoning tasks 262K tokens is plenty, but if you're feeding entire large codebases or long document chains in one pass, the extra 138K tokens on GPT-5.2-Codex gives more headroom before you need to chunk input.
Modality and reasoning controls
Kimi K2 Thinking is text-only, both input and output. GPT-5.2-Codex accepts text and image input (output stays text), which matters if your workflow includes screenshots, diagrams, or UI mockups as part of the coding task. Both models make reasoning mandatory, but GPT-5.2-Codex exposes four effort levels — xhigh, high, medium (default), low — giving you a dial on reasoning depth versus latency and cost. Kimi K2 Thinking's reasoning is mandatory with no exposed effort tiers in this data. Kimi K2 Thinking also supports a wider parameter set overall, including top_k, repetition_penalty, and logprobs, which GPT-5.2-Codex's list doesn't include.
Which model for which job
| Use case | Pick | Why |
|---|---|---|
| High-volume reasoning pipelines | Kimi K2 Thinking | $11.00 vs $45.50 on a 10M-in/2M-out workload |
| Coding agents (mobile, web, full-stack) | GPT-5.2-Codex | design-arena agent ranks 12-27 across five categories vs Kimi's single rank-70 score |
| Tasks requiring image input (screenshots, mockups) | GPT-5.2-Codex | only model of the two with image input modality |
| Long-context ingestion beyond 262K tokens | GPT-5.2-Codex | 400,000-token context vs 262,144 for Kimi K2 Thinking |
| Budget-constrained batch processing | Kimi K2 Thinking | input price ratio of 0.34 means output tokens cost far less at scale |
| Tunable reasoning depth per request | GPT-5.2-Codex | four supported reasoning efforts (xhigh, high, medium, low) vs none exposed for Kimi |
Questions
- How much more does GPT-5.2-Codex cost than Kimi K2 Thinking?
- On a 10M-input/2M-output workload, GPT-5.2-Codex costs $45.50 versus $11.00 for Kimi K2 Thinking on OpenKey — about 4.1x more. The gap comes from both input ($1.8025/M vs $0.618/M) and output ($14.42/M vs $2.575/M) pricing after the 3% fee.
- Which model has the bigger context window?
- GPT-5.2-Codex, at 400,000 tokens versus Kimi K2 Thinking's 262,144 tokens. The context ratio is 0.66, meaning Kimi's window is roughly two-thirds the size. GPT-5.2-Codex also has a higher max completion at 128,000 tokens versus 100,352.
- Does either model accept image input?
- GPT-5.2-Codex does — its input modalities are text and image, while output stays text-only. Kimi K2 Thinking is text-to-text only, with no image input or output support in either direction.
- Which model performs better on coding-agent benchmarks?
- GPT-5.2-Codex has design-arena agent scores across five categories, ranking 12th in Godot game dev (elo 1187) and 15th in Android native (elo 1176). Kimi K2 Thinking's only comparable score is website generation at rank 70 (elo 1156), a much weaker single-category result.