Kimi K2 Thinking vs Grok Build 0.1
Moonshot AIxAIboth via one key, provider price + 3%
Kimi K2 Thinking (Moonshot AI, released Nov 2025) and Grok Build 0.1 (xAI, released May 2026) are both mandatory-reasoning models aimed at agentic and coding workflows, with near-identical context windows — 262,144 tokens versus 256,000. The real differences are price, modality, and how much performance data you actually get to evaluate the model against. Both run on OpenKey under one API key with a flat 3% fee on provider list pricing.
Spec vs spec
| Spec | Kimi K2 Thinking | Grok Build 0.1 |
|---|---|---|
| Context window | 262K | 256K |
| Max output | 100K | — |
| Input modalities | text | text, image |
| Output modalities | text | text |
| Released | Nov 6, 2025 | May 20, 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%
x-ai/grok-build-0.1
Input · 1M tokens
$1.00 + 3%$1.03
Output · 1M tokens
$2.00 + 3%$2.06
Cache read · 1M tokens
$0.200 + 3%$0.206
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%
x-ai/grok-build-0.1
$14.42
$14.00 provider + 3%
Pricing math
Kimi K2 Thinking's provider price is $0.60/M input and $2.50/M output; on OpenKey that's $0.60 × 1.03 = $0.618/M input and $2.50 × 1.03 = $2.575/M output. Grok Build 0.1 lists at $1.00/M input and $2.00/M output, which becomes $1.03/M input and $2.06/M output on OpenKey. Kimi's input tokens are 0.6x the price of Grok's (the input price ratio). For a 10M-input/2M-output workload, Kimi K2 Thinking costs $11.00 total against Grok Build 0.1's $14.00 — a $3.00 gap on one run that compounds fast at scale. Kimi also offers cache-read pricing at $0.15/M provider-side, undercutting Grok's $0.20/M cache-read rate, so repeated-context workloads widen the gap further.
Benchmarks and agentic performance
Kimi K2 Thinking has real numbers to point to: an Artificial Analysis intelligence index of 17.3, a coding index of 21, and an agentic index of 1.8, plus a Design Arena website-building Elo of 1156 (48.8% win rate, rank 70 among models tested). Grok Build 0.1 has no published benchmarks in this dataset — no Design Arena entry, no Artificial Analysis scores. That's not proof it's weaker, but it means you're evaluating it on description alone ('xAI's fast coding model... optimized for interactive coding') versus Kimi's measured track record. If benchmark transparency matters to your decision, Kimi K2 Thinking is the only one of the two with data to check.
Modality and context
Grok Build 0.1 accepts text and image input; Kimi K2 Thinking is text-only in and out. If your coding agent needs to read screenshots, diagrams, or UI mockups, Grok Build 0.1 is the only option here. Context windows are close enough not to matter: 262,144 tokens for Kimi versus 256,000 for Grok, a context ratio of 1.02. Neither model publishes a max-output cap that changes this calculus meaningfully — Kimi caps completions at 100,352 tokens, Grok doesn't specify one in this data.
Tooling and parameters
Both models support tool calling, structured outputs, and reasoning traces (`include_reasoning`, `reasoning`), so either fits an agent framework without adapter work. Kimi K2 Thinking supports a few extra knobs — `frequency_penalty` overlap aside, it adds `repetition_penalty` and `top_k`, giving you finer control over sampling behavior in long agentic loops. Grok Build 0.1's parameter list is slightly shorter but covers the essentials: `tool_choice`, `tools`, `response_format`, `seed`. Neither difference is disqualifying; it's a tie-breaker if you're already deep into prompt-level tuning.
Which model for which job
| Use case | Pick | Why |
|---|---|---|
| Budget-sensitive agentic pipelines | Kimi K2 Thinking | $11.00 vs $14.00 on a 10M-in/2M-out workload |
| Coding agent that reads screenshots or diagrams | Grok Build 0.1 | only model of the two with image input |
| Evaluating on published benchmarks before committing | Kimi K2 Thinking | has Design Arena Elo 1156 and Artificial Analysis scores; Grok Build 0.1 has none listed |
| High-frequency cached-context workloads | Kimi K2 Thinking | cache-read at $0.15/M vs Grok's $0.20/M |
| Fine-grained sampling control (top_k, repetition_penalty) | Kimi K2 Thinking | supports both parameters; Grok Build 0.1's list omits them |
Questions
- Which model is cheaper for a typical agentic workload?
- Kimi K2 Thinking, by $3.00 on a 10M-input/2M-output run: $11.00 total versus Grok Build 0.1's $14.00. The gap comes mostly from input pricing — Kimi's $0.618/M on OpenKey versus Grok's $1.03/M.
- Does either model handle images?
- Only Grok Build 0.1. It takes text and image input and returns text. Kimi K2 Thinking is text-to-text only, so if your workflow needs visual input, Grok Build 0.1 is the only choice between these two.
- Which has a bigger context window?
- Kimi K2 Thinking, slightly: 262,144 tokens versus Grok Build 0.1's 256,000, a ratio of 1.02. Practically that's not a meaningful difference for most document or codebase sizes.
- Is there benchmark data to compare the two directly?
- Only for Kimi K2 Thinking — it has a Design Arena website Elo of 1156 (rank 70, 48.8% win rate) and Artificial Analysis scores (17.3 intelligence index, 21 coding index, 1.8 agentic index). Grok Build 0.1 has no benchmarks in this dataset to compare against.