Kimi K2.5 vs Phi-3 Mini 4k
Kimi K2.5 (2026) and Phi-3 Mini 4k (2024) are agentic coding models from Moonshot AI and Microsoft Research. Kimi K2.5 ships a 256K-token context window, while Phi-3 Mini 4k ships a 4K-token context window. On MMLU PRO, Kimi K2.5 leads by 41.4 pts. On pricing, Phi-3 Mini 4k costs $0.05/1M input tokens versus $0.38/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Phi-3 Mini 4k is ~665% cheaper at $0.05/1M; pay for Kimi K2.5 only for coding workflow support.
Specs
| Released | 2026-03-15 | 2024-04-23 |
| Context window | 256K | 4K |
| Parameters | 1T (MoE, 384 experts) | 3.8B |
| Architecture | mixture of experts | decoder only |
| License | MIT | Open Source |
| Knowledge cutoff | - | - |
Pricing and availability
| Kimi K2.5 | Phi-3 Mini 4k | |
|---|---|---|
| Input price | $0.38/1M tokens | $0.05/1M tokens |
| Output price | $1.72/1M tokens | $0.25/1M tokens |
| Providers |
Capabilities
| Kimi K2.5 | Phi-3 Mini 4k | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
| Benchmark | Kimi K2.5 | Phi-3 Mini 4k |
|---|---|---|
| MMLU PRO | 87.1 | 45.7 |
| Google-Proof Q&A | 87.9 | 40.9 |
| Instruction-Following Evaluation | 93.9 | 45.0 |
Deep dive
On shared benchmark coverage, MMLU PRO has Kimi K2.5 at 87.1 and Phi-3 Mini 4k at 45.7, with Kimi K2.5 ahead by 41.4 points; Google-Proof Q&A has Kimi K2.5 at 87.9 and Phi-3 Mini 4k at 40.9, with Kimi K2.5 ahead by 47.0 points; Instruction-Following Evaluation has Kimi K2.5 at 93.9 and Phi-3 Mini 4k at 45.0, with Kimi K2.5 ahead by 48.9 points. The largest visible gap is 48.9 points on Instruction-Following Evaluation, 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 function calling: Kimi K2.5 and structured outputs: Kimi K2.5. Both models share the core language-model surface, 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 Phi-3 Mini 4k lists $0.05/1M input and $0.25/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Phi-3 Mini 4k lower by about $0.67 per million blended tokens. Availability is 7 providers versus 4, so concentration risk also matters.
Choose Kimi K2.5 when coding workflow support, larger context windows, and broader provider choice are central to the workload. Choose Phi-3 Mini 4k when provider fit and lower input-token cost 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, Kimi K2.5 or Phi-3 Mini 4k?
Kimi K2.5 supports 256K tokens, while Phi-3 Mini 4k supports 4K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Which is cheaper, Kimi K2.5 or Phi-3 Mini 4k?
Phi-3 Mini 4k is cheaper on tracked token pricing. Kimi K2.5 costs $0.38/1M input and $1.72/1M output tokens. Phi-3 Mini 4k costs $0.05/1M input and $0.25/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Kimi K2.5 or Phi-3 Mini 4k open source?
Kimi K2.5 is listed under MIT. Phi-3 Mini 4k is listed under Open Source. 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 function calling, Kimi K2.5 or Phi-3 Mini 4k?
Kimi K2.5 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.
Which is better for structured outputs, Kimi K2.5 or Phi-3 Mini 4k?
Kimi K2.5 has the clearer documented structured outputs signal in this comparison. If structured outputs 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 Phi-3 Mini 4k?
Kimi K2.5 is available on Fireworks AI, OpenRouter, Together AI, Fireworks AI, and NVIDIA NIM. Phi-3 Mini 4k is available on Microsoft Foundry, NVIDIA NIM, Baseten API, and Replicate API. 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.