phi-4 vs Kimi K2 Instruct
At a Glance
| Compare | phi-4Microsoft | Kimi K2 InstructMoonshot AI |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.070Deepinfra ↗ · Sep 21, 2026 | $0.57Openrouter ↗ · Aug 29, 2026 |
| Output priceFrom · USD / 1M tokens | $0.14Deepinfra ↗ · Sep 21, 2026 | $2.30Openrouter ↗ · Aug 29, 2026 |
| Context windowMaximum documented tokens | 16K | 131K |
| Model facts checked | Aug 28, 2026View model evidence → | Aug 28, 2026View model evidence → |
Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →
Available Benchmarks
| Benchmark | phi-4 | Kimi-K2-Instruct |
|---|---|---|
| Berkeley Function Calling Leaderboardv4-ede5081a24bc · overall_accuracy · leader | 28.7949% of row best · percent · Phi-4 (Prompt) | 59.06100% of row best · percent · Moonshotai-Kimi-K2-Instruct (FC) |
| Overall ResultCounted from the protocol-matched rows above | 0 benchmark winsNo overall winner | 1 benchmark winNo overall winner |
Third-party benchmark Only like-for-like primary-publisher results are shown; raw scores, relative scores, configuration, and token spend remain visible.
Side-by-Side Facts
| Field | phi-4 | Kimi-K2-Instruct |
|---|---|---|
| Developer | Microsoft | Moonshot AI |
| Family | Phi 4 | Kimi K2 Instruct |
| Model | phi-4 | Kimi-K2-Instruct |
| Version | phi-4 | Kimi-K2-Instruct |
| Lifecycle | active | active |
| Released | 2024-12-12 | 2025-07-11 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text |
| Output modalities | Text | Text |
| Context window | 16K | 131K |
| Total parameters | 14.7B | 1T |
| Active parameters | Unknown | 32B |
| License | mit | other |
| Open weights | Yes | Yes |
| API available | Yes | Yes |
| Self-hostable | Yes | Yes |
| Provider access | Deepinfra (Standard), Hugging Face (Standard), Openrouter (Standard) | Hugging Face (Standard), Openrouter (Standard) |
| Capabilities | chat, generation | chat, generation, tools |
phi-4 Capabilities
Kimi K2 Instruct Capabilities
Primary Evidence
Sources and Freshness
Questions
phi-4 vs Kimi K2 Instruct FAQs
Is phi-4 or Kimi K2 Instruct better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both phi-4 and Kimi K2 Instruct, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, phi-4 or Kimi K2 Instruct?+
phi-4 is $0.070 and Kimi K2 Instruct is $0.57 per million tokens, so phi-4 is cheaper on this metric. phi-4 is $0.14 and Kimi K2 Instruct is $2.30 per million tokens, so phi-4 is cheaper on this metric.
Which has a larger context window, phi-4 or Kimi K2 Instruct?+
Kimi K2 Instruct has the larger sourced context window. phi-4 supports 16K and Kimi K2 Instruct supports 131K.
Which performs better in benchmarks, phi-4 or Kimi K2 Instruct?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can phi-4 or Kimi K2 Instruct be self-hosted?+
Both models have the same recorded self-hosting status: supported. phi-4 is open weight; Kimi K2 Instruct is open weight.
Can phi-4 and Kimi K2 Instruct understand images?+
phi-4 is not documented with image input; Kimi K2 Instruct is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, phi-4 or Kimi K2 Instruct?+
Neither has a larger sourced maximum output. phi-4 is — and Kimi K2 Instruct is —.
Do phi-4 and Kimi K2 Instruct support reasoning and tool use?+
phi-4: none of these features are definitively sourced. Kimi K2 Instruct: tool calling. Feature support does not establish relative quality.
Which is available from more inference providers, phi-4 or Kimi K2 Instruct?+
phi-4 has 3 sourced provider routes; Kimi K2 Instruct has 2, so phi-4 has broader tracked availability.
Which offers better value, phi-4 or Kimi K2 Instruct?+
There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.