Kimi K2.5 vs o4-mini
Kimi K2.5 (2026) and o4-mini (2025) are agentic coding models from Moonshot AI and OpenAI. Kimi K2.5 ships a 256K-token context window, while o4-mini ships a not-yet-sourced context window. On MMLU PRO, Kimi K2.5 leads by 3.9 pts. On pricing, Kimi K2.5 costs $0.38/1M input tokens versus $0.5/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Kimi K2.5 is safer overall; choose o4-mini when coding workflow support matters.
Specs
| Released | 2026-03-15 | 2025-04-16 |
| Context window | 256K | — |
| Parameters | 1T (MoE, 384 experts) | — |
| Architecture | mixture of experts | decoder only |
| License | MIT | Proprietary |
| Knowledge cutoff | - | 2025-08 |
Pricing and availability
| Kimi K2.5 | o4-mini | |
|---|---|---|
| Input price | $0.38/1M tokens | $0.5/1M tokens |
| Output price | $1.72/1M tokens | $2/1M tokens |
| Providers |
Capabilities
| Kimi K2.5 | o4-mini | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
| Benchmark | Kimi K2.5 | o4-mini |
|---|---|---|
| MMLU PRO | 87.1 | 83.2 |
| MultiChallenge | 61.4 | 44.9 |
| BFCL | 68.3 | 53.2 |
| Massive Multi-discipline Multimodal Understanding | 84.3 | 81.6 |
Deep dive
On shared benchmark coverage, MMLU PRO has Kimi K2.5 at 87.1 and o4-mini at 83.2, with Kimi K2.5 ahead by 3.9 points; MultiChallenge has Kimi K2.5 at 61.4 and o4-mini at 44.9, with Kimi K2.5 ahead by 16.5 points; BFCL has Kimi K2.5 at 68.3 and o4-mini at 53.2, with Kimi K2.5 ahead by 15.1 points. The largest visible gap is 16.5 points on MultiChallenge, 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 vision: o4-mini, multimodal input: o4-mini, reasoning mode: o4-mini, tool use: o4-mini, and code execution: o4-mini. 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, Kimi K2.5 lists $0.38/1M input and $1.72/1M output tokens, while o4-mini lists $0.5/1M input and $2/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Kimi K2.5 lower by about $0.17 per million blended tokens. Availability is 7 providers versus 4, so concentration risk also matters.
Choose Kimi K2.5 when coding workflow support, lower input-token cost, and broader provider choice are central to the workload. Choose o4-mini when coding workflow support are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.
FAQ
Which is cheaper, Kimi K2.5 or o4-mini?
Kimi K2.5 is cheaper on tracked token pricing. Kimi K2.5 costs $0.38/1M input and $1.72/1M output tokens. o4-mini costs $0.5/1M input and $2/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Kimi K2.5 or o4-mini open source?
Kimi K2.5 is listed under MIT. o4-mini 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 vision, Kimi K2.5 or o4-mini?
o4-mini 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, Kimi K2.5 or o4-mini?
o4-mini 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, Kimi K2.5 or o4-mini?
o4-mini 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 Kimi K2.5 and o4-mini?
Kimi K2.5 is available on Fireworks AI, OpenRouter, Together AI, Fireworks AI, and NVIDIA NIM. o4-mini is available on OpenAI API, OpenRouter, OpenAI Batch API, and Replicate API. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-04-27. Data sourced from public model cards and provider documentation.