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Grok 4 vs Kimi K2.5

Grok 4 (2026) and Kimi K2.5 (2026) are agentic coding models from xAI and Moonshot AI. Grok 4 ships a 256k-token context window, while Kimi K2.5 ships a 256K-token context window. On MMLU PRO, Kimi K2.5 leads by a hair. On pricing, Kimi K2.5 costs $0.38/1M input tokens versus $3/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.

Kimi K2.5 is ~684% cheaper at $0.38/1M; pay for Grok 4 only for coding workflow support.

Specs

Released2026-03-012026-03-15
Context window256k256K
Parameters1T (MoE, 384 experts)
Architecturedecoder onlymixture of experts
LicenseProprietaryMIT
Knowledge cutoff--

Pricing and availability

Grok 4Kimi K2.5
Input price$3/1M tokens$0.38/1M tokens
Output price$15/1M tokens$1.72/1M tokens
Providers

Capabilities

Grok 4Kimi K2.5
Vision
Multimodal
Reasoning
Function calling
Tool use
Structured outputs
Code execution

Benchmarks

BenchmarkGrok 4Kimi K2.5
MMLU PRO87.087.1
τ-bench78.974.2

Deep dive

On shared benchmark coverage, MMLU PRO has Grok 4 at 87 and Kimi K2.5 at 87.1, with Kimi K2.5 ahead by 0.1 points; τ-bench has Grok 4 at 78.9 and Kimi K2.5 at 74.2, with Grok 4 ahead by 4.7 points. The largest visible gap is 4.7 points on τ-bench, which matters most when that benchmark mirrors your workload. Treat isolated benchmark wins as directional, because provider routing, prompt style, and tool access can move real application results.

The capability footprint differs most on multimodal input: Grok 4, reasoning mode: Grok 4, function calling: Kimi K2.5, and code execution: Grok 4. 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 4 lists $3/1M input and $15/1M output tokens, while Kimi K2.5 lists $0.38/1M input and $1.72/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Kimi K2.5 lower by about $5.82 per million blended tokens. Availability is 3 providers versus 7, so concentration risk also matters.

Choose Grok 4 when coding workflow support are central to the workload. Choose Kimi K2.5 when coding workflow support, lower input-token cost, 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.

FAQ

Which has a larger context window, Grok 4 or Kimi K2.5?

Grok 4 supports 256k tokens, while Kimi K2.5 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 4 or Kimi K2.5?

Kimi K2.5 is cheaper on tracked token pricing. Grok 4 costs $3/1M input and $15/1M output tokens. Kimi K2.5 costs $0.38/1M input and $1.72/1M output tokens. Provider discounts or batch pricing can still change the final bill.

Is Grok 4 or Kimi K2.5 open source?

Grok 4 is listed under Proprietary. Kimi K2.5 is listed under MIT. 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 multimodal input, Grok 4 or Kimi K2.5?

Grok 4 has the clearer documented multimodal input signal in this comparison. If multimodal input is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.

Which is better for reasoning mode, Grok 4 or Kimi K2.5?

Grok 4 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.

Where can I run Grok 4 and Kimi K2.5?

Grok 4 is available on Microsoft Foundry, OpenRouter, and Replicate API. Kimi K2.5 is available on Fireworks AI, OpenRouter, Together AI, Fireworks AI, and NVIDIA NIM. 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.