| Signal | Kimi K2.5 | Delta | Phi 4 |
|---|---|---|---|
Capabilities | 83 | +50 | |
Benchmarks | 57 | -8 | |
Pricing | 98 | -2 | |
Context window size | 77 | +17 | |
Recency | 100 | +59 | |
Output Capacity | 90 | +20 | |
| Overall Result | 4 wins | of 6 | 2 wins |
Score History
58.6
current score
Phi 4
right now
59.9
current score
Moonshot AI
Microsoft
Phi 4 saves you $121.50/month
That's $1458.00/year compared to Kimi K2.5 at your current usage level of 100K calls/month.
| Metric | Kimi K2.5 | Phi 4 | Winner |
|---|---|---|---|
| Overall Score | 59 | 60 | Phi 4 |
| Rank | #151 | #149 | Phi 4 |
| Quality Rank | #151 | #149 | Phi 4 |
| Adoption Rank | #151 | #149 | Phi 4 |
| Parameters | -- | -- | -- |
| Context Window | 262K | 16K | Kimi K2.5 |
| Pricing | $0.40/$1.90/M | $0.07/$0.14/M | -- |
| Signal Scores | |||
| Capabilities | 83 | 33 | Kimi K2.5 |
| Benchmarks | 57 | 65 | Phi 4 |
| Pricing | 98 | 100 | Phi 4 |
| Context window size | 77 | 60 | Kimi K2.5 |
| Recency | 100 | 41 | Kimi K2.5 |
| Output Capacity | 90 | 70 | Kimi K2.5 |
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 59/100 (rank #151), placing it in the top 48% of all 290 models tracked.
Scores 60/100 (rank #149), placing it in the top 49% 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 offers 91% better value per quality point. At 1M tokens/day, you'd spend $3.08/month with Phi 4 vs $34.50/month with Kimi K2.5 - a $31.43 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 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 ($0.14/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (60/100) correlates with better nuance, coherence, and style in long-form content
Image understanding & OCR
Supports vision input - can analyze screenshots, diagrams, photos, and scanned documents directly
Kimi K2.5 and Phi 4 are extremely close in overall performance (only 1.2999999999999972 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
Kimi K2.5
Marginally better benchmark scores; both are excellent
Best for Cost
Phi 4
91% lower pricing; better value at scale
Best for Reliability
Kimi K2.5
Higher uptime and faster response speeds
Best for Prototyping
Kimi K2.5
Stronger community support and better developer experience
Best for Production
Kimi K2.5
Wider enterprise adoption and proven at scale
by Moonshot AI
| Capability | Kimi K2.5 | Phi 4 |
|---|---|---|
| Vision (Image Input)differs | ||
| Function Callingdiffers | ||
| Streaming | ||
| JSON Mode | ||
| Reasoningdiffers | ||
| Web Search | ||
| Image Output |
Moonshot AI
Microsoft
Phi 4 saves you $2.71/month
That's 90% cheaper than Kimi K2.5 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 K2.5 | Phi 4 |
|---|---|---|
| Context Window | 262K | 16K |
| Max Output Tokens | 262,144 | 16,384 |
| Open Source | Yes | Yes |
| Created | Jan 27, 2026 | Jan 10, 2025 |