| Signal | Kimi K2 Thinking | Delta | Phi 4 Mini Instruct |
|---|---|---|---|
Capabilities | 67 | +33 | |
Benchmarks | 51 | -14 | |
Pricing | 98 | -2 | |
Context window size | 86 | +5 | |
Recency | 99 | +4 | |
Output Capacity | 90 | +5 | |
| Overall Result | 4 wins | of 6 | 2 wins |
Score History
53.3
current score
Kimi K2 Thinking
right now
52.7
current score
Moonshot AI
Microsoft
Phi 4 Mini Instruct saves you $159.50/month
That's $1914.00/year compared to Kimi K2 Thinking at your current usage level of 100K calls/month.
| Metric | Kimi K2 Thinking | Phi 4 Mini Instruct | Winner |
|---|---|---|---|
| Overall Score | 53 | 53 | Kimi K2 Thinking |
| Rank | #165 | #167 | Kimi K2 Thinking |
| Quality Rank | #165 | #167 | Kimi K2 Thinking |
| Adoption Rank | #165 | #167 | Kimi K2 Thinking |
| Parameters | -- | -- | -- |
| Context Window | 262K | 128K | Kimi K2 Thinking |
| Pricing | $0.60/$2.50/M | $0.08/$0.35/M | -- |
| Signal Scores | |||
| Capabilities | 67 | 33 | Kimi K2 Thinking |
| Benchmarks | 51 | 65 | Phi 4 Mini Instruct |
| Pricing | 98 | 100 | Phi 4 Mini Instruct |
| Context window size | 86 | 81 | Kimi K2 Thinking |
| Recency | 99 | 96 | Kimi K2 Thinking |
| Output Capacity | 90 | 85 | Kimi K2 Thinking |
Our score (0-100) is driven by benchmark performance (90%) from Arena Elo ratings, MMLU, GPQA, HumanEval, SWE-bench, and 15+ standardized evaluations. Capabilities and context window serve as tiebreakers (10%). Learn more about our methodology.
Scores 53/100 (rank #165), placing it in the top 43% of all 290 models tracked.
Scores 53/100 (rank #167), placing it in the top 43% of all 290 models tracked.
With only a 1-point gap, these models are in the same performance tier. The practical difference in output quality is minimal - your choice should depend on pricing, latency requirements, and specific feature needs.
Phi 4 Mini Instruct offers 86% better value per quality point. At 1M tokens/day, you'd spend $6.45/month with Phi 4 Mini Instruct vs $46.50/month with Kimi K2 Thinking - a $40.05 monthly difference.
Both models have comparable response speeds. For most applications, the latency difference is negligible.
When latency matters most: Interactive chatbots, IDE code completion, real-time translation, and user-facing applications where response time directly impacts experience. For batch processing, background summarization, or offline analysis, latency is less critical.
Code generation & review
Based on overall model capabilities and architecture for coding tasks like generating functions, debugging, and refactoring
Customer support chatbot
Suitable for user-facing chat with competitive response times. Phi 4 Mini Instruct also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (262K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.35/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (53/100) correlates with better nuance, coherence, and style in long-form content
Kimi K2 Thinking and Phi 4 Mini Instruct are extremely close in overall performance (only 0.5999999999999943 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
Kimi K2 Thinking
Marginally better benchmark scores; both are excellent
Best for Cost
Phi 4 Mini Instruct
86% lower pricing; better value at scale
Best for Reliability
Kimi K2 Thinking
Higher uptime and faster response speeds
Best for Prototyping
Kimi K2 Thinking
Stronger community support and better developer experience
Best for Production
Kimi K2 Thinking
Wider enterprise adoption and proven at scale
by Moonshot AI
| Capability | Kimi K2 Thinking | Phi 4 Mini Instruct |
|---|---|---|
| Vision (Image Input) | ||
| Function Callingdiffers | ||
| Streaming | ||
| JSON Mode | ||
| Reasoningdiffers | ||
| Web Search | ||
| Image Output |
Moonshot AI
Microsoft
Phi 4 Mini Instruct saves you $3.52/month
That's 86% cheaper than Kimi K2 Thinking at 1,000 tokens/request and 100 requests/day.
Assumes 60% input / 40% output token ratio per request. Actual costs may vary based on your usage pattern.
| Parameter | Kimi K2 Thinking | Phi 4 Mini Instruct |
|---|---|---|
| Context Window | 262K | 128K |
| Max Output Tokens | 262,144 | 128,000 |
| Open Source | Yes | Yes |
| Created | Nov 6, 2025 | Oct 17, 2025 |