Grok Code Fast 1 vs Kimi K2 Thinking
Grok Code Fast 1 (2025) and Kimi K2 Thinking (2025) are agentic coding models from xAI and Moonshot AI. Grok Code Fast 1 ships a 262K-token context window, while Kimi K2 Thinking ships a 256K-token context window. On pricing, Grok Code Fast 1 costs $0.2/1M input tokens versus $0.6/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Grok Code Fast 1 is ~200% cheaper at $0.2/1M; pay for Kimi K2 Thinking only for reasoning depth.
Specs
| Released | 2025-08-27 | 2025-01-01 |
| Context window | 262K | 256K |
| Parameters | 314B | — |
| Architecture | mixture of experts | decoder only |
| License | Proprietary | Proprietary |
| Knowledge cutoff | - | - |
Pricing and availability
| Grok Code Fast 1 | Kimi K2 Thinking | |
|---|---|---|
| Input price | $0.2/1M tokens | $0.6/1M tokens |
| Output price | $1.5/1M tokens | $2.5/1M tokens |
| Providers |
Capabilities
| Grok Code Fast 1 | Kimi K2 Thinking | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on reasoning mode: Kimi K2 Thinking, function calling: Grok Code Fast 1, and tool use: Grok Code Fast 1. Both models share structured outputs, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.
For cost, Grok Code Fast 1 lists $0.2/1M input and $1.5/1M output tokens, while Kimi K2 Thinking lists $0.6/1M input and $2.5/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Grok Code Fast 1 lower by about $0.58 per million blended tokens. Availability is 1 providers versus 5, so concentration risk also matters.
Choose Grok Code Fast 1 when coding workflow support, larger context windows, and lower input-token cost are central to the workload. Choose Kimi K2 Thinking when reasoning depth and broader provider choice are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship. This keeps the decision grounded in measurable tradeoffs instead of brand-level assumptions.
FAQ
Which has a larger context window, Grok Code Fast 1 or Kimi K2 Thinking?
Grok Code Fast 1 supports 262K tokens, while Kimi K2 Thinking supports 256K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Which is cheaper, Grok Code Fast 1 or Kimi K2 Thinking?
Grok Code Fast 1 is cheaper on tracked token pricing. Grok Code Fast 1 costs $0.2/1M input and $1.5/1M output tokens. Kimi K2 Thinking costs $0.6/1M input and $2.5/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Grok Code Fast 1 or Kimi K2 Thinking open source?
Grok Code Fast 1 is listed under Proprietary. Kimi K2 Thinking is listed under Proprietary. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.
Which is better for reasoning mode, Grok Code Fast 1 or Kimi K2 Thinking?
Kimi K2 Thinking has the clearer documented reasoning mode signal in this comparison. If reasoning mode is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for function calling, Grok Code Fast 1 or Kimi K2 Thinking?
Grok Code Fast 1 has the clearer documented function calling signal in this comparison. If function calling is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run Grok Code Fast 1 and Kimi K2 Thinking?
Grok Code Fast 1 is available on OpenRouter. Kimi K2 Thinking is available on Fireworks AI, GCP Vertex AI, NVIDIA NIM, AWS Bedrock, and OpenRouter. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-04-27. Data sourced from public model cards and provider documentation.