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GPT-5.4 Pro vs Kimi K2.5

GPT-5.4 Pro (2026) and Kimi K2.5 (2026) are agentic coding models from OpenAI and Moonshot AI. GPT-5.4 Pro ships a 1.1M-token context window, while Kimi K2.5 ships a 256K-token context window. On pricing, Kimi K2.5 costs $0.44/1M input tokens versus $30/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing.

Kimi K2.5 is ~6718% cheaper at $0.44/1M; pay for GPT-5.4 Pro only for coding workflow support.

Specs

Specification
Released2026-03-012026-03-15
Context window1.1M256K
Parameters1T (MoE, 384 experts)
Architecturedecoder onlymixture of experts
LicenseProprietaryMIT
Knowledge cutoff2025-08-

Pricing and availability

Pricing attributeGPT-5.4 ProKimi K2.5
Input price$30/1M tokens$0.44/1M tokens
Output price$180/1M tokens$2/1M tokens
Providers

Capabilities

CapabilityGPT-5.4 ProKimi K2.5
VisionYesNo
MultimodalYesNo
ReasoningYesNo
Function callingYesYes
Tool useYesNo
Structured outputsYesYes
Code executionYesNo

Benchmarks

No shared benchmark rows are currently sourced for this pair.

Deep dive

The capability footprint differs most on vision: GPT-5.4 Pro, multimodal input: GPT-5.4 Pro, reasoning mode: GPT-5.4 Pro, tool use: GPT-5.4 Pro, and code execution: GPT-5.4 Pro. Both models share function calling and 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, GPT-5.4 Pro lists $30/1M input and $180/1M output tokens, while Kimi K2.5 lists $0.44/1M input and $2/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Kimi K2.5 lower by about $74.09 per million blended tokens. Availability is 2 providers versus 7, so concentration risk also matters.

Choose GPT-5.4 Pro when coding workflow support and larger context windows 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. This keeps the decision grounded in measurable tradeoffs instead of brand-level assumptions.

FAQ

Which has a larger context window, GPT-5.4 Pro or Kimi K2.5?

GPT-5.4 Pro supports 1.1M 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, GPT-5.4 Pro or Kimi K2.5?

Kimi K2.5 is cheaper on tracked token pricing. GPT-5.4 Pro costs $30/1M input and $180/1M output tokens. Kimi K2.5 costs $0.44/1M input and $2/1M output tokens. Provider discounts or batch pricing can still change the final bill.

Is GPT-5.4 Pro or Kimi K2.5 open source?

GPT-5.4 Pro 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 vision, GPT-5.4 Pro or Kimi K2.5?

GPT-5.4 Pro has the clearer documented vision signal in this comparison. If vision is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.

Which is better for multimodal input, GPT-5.4 Pro or Kimi K2.5?

GPT-5.4 Pro 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.

Where can I run GPT-5.4 Pro and Kimi K2.5?

GPT-5.4 Pro is available on OpenRouter and OpenAI 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.

Continue comparing

Last reviewed: 2026-05-11. Data sourced from public model cards and provider documentation.