Skip to content

Hy4 vs Mi:dm K 3.0 Pro

Hy4

Tencent

40#159
vs
Signal-by-Signal Comparison
SignalHy4DeltaMi:dm K 3.0 Pro
Capabilities
83
+33
50
Pricing
99
0
99
Context window size
96
+10
86
Recency
100
--
100
Output Capacity
77
-9
86
Overall Result
2 wins
of 5
2 wins
It's a tie - both models win 2 signals each

Score History

Score History (2 data points)
Hy4Mi:dm K 3.0 Pro
Hy4

40

current score

Leader

Tied

right now

Mi:dm K 3.0 Pro

40

current score

LMMarketCap.com
Interactive Price Comparison
100Kcalls/month
1,000tokens (~1,333 chars)
500tokens (~667 chars)

Hy4

Tencent

Per request$0.001042
Daily$3.47
Monthly$104.22
Annual$1250.70

Mi:dm K 3.0 Pro

kt

Best Value
Per request$0.000600
Daily$2.00
Monthly$60.00
Annual$720.00

Mi:dm K 3.0 Pro saves you $44.23/month

That's $530.70/year compared to Hy4 at your current usage level of 100K calls/month.

42% cheaper
Choose Mi:dm K 3.0 Pro for cost optimization

Hy4 pricing:
Input:$0.42/M tokens
Output:$1.25/M tokens
Mi:dm K 3.0 Pro pricing:
Input:$0.20/M tokens
Output:$0.80/M tokens
Tie
Hy4

Tencent

40

Composite Score

Tie
Mi:dm K 3.0 Pro

kt

40

Composite Score

Signal-by-Signal Comparison
MetricHy4Mi:dm K 3.0 ProWinner
Overall Score
40
40
--
Rank#159#157
Mi:dm K 3.0 Pro
Quality Rank#159#157
Mi:dm K 3.0 Pro
Adoption Rank#159#157
Mi:dm K 3.0 Pro
Parameters------
Context Window1049K262K
Hy4
Pricing$0.42/$1.25/M$0.20/$0.80/M--
Signal Scores
Capabilities
83
50
Hy4
Pricing
99
99
Mi:dm K 3.0 Pro
Context window size
96
86
Hy4
Recency
100
100
Hy4
Output Capacity
77
86
Mi:dm K 3.0 Pro
Benchmark Interpretation

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.

Hy4Entry Level

Scores 40/100 (rank #159), placing it in the top 46% of all 290 models tracked.

Raw Quality0/100
Cost Efficiency0/100
Speed0/100
Mi:dm K 3.0 ProEntry Level

Scores 40/100 (rank #157), placing it in the top 46% of all 290 models tracked.

Raw Quality0/100
Cost Efficiency0/100
Speed0/100

With only a 0-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.

When to Use Each Model

Choose Hy4 when you need:

  • Processing long documents or large codebases (1049K token context)
  • Multimodal workflows that require image understanding
  • Agentic applications using tool/function calling
  • Step-by-step reasoning and chain-of-thought problem solving
  • Self-hosted deployments where you need full control over the model

Choose Mi:dm K 3.0 Pro when you need:

  • High-volume production workloads where API costs must be minimized
  • Step-by-step reasoning and chain-of-thought problem solving
Cost-Performance Analysis
Hy4
Input cost$0.42/M tokens
Output cost$1.25/M tokens
Cost per quality point$0.042
Est. monthly (1M tokens/day)$25.01
Mi:dm K 3.0 ProBest Value
Input cost$0.20/M tokens
Output cost$0.80/M tokens
Cost per quality point$0.025
Est. monthly (1M tokens/day)$15.00

Mi:dm K 3.0 Pro offers 40% better value per quality point. At 1M tokens/day, you'd spend $15.00/month with Mi:dm K 3.0 Pro vs $25.01/month with Hy4 - a $10.01 monthly difference.

Latency & Speed
Hy4Faster
Speed score0/100
Mi:dm K 3.0 Pro
Speed score0/100

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.

Example Use Cases

Code generation & review

Based on overall model capabilities and architecture for coding tasks like generating functions, debugging, and refactoring

Hy4

Customer support chatbot

Suitable for user-facing chat with competitive response times. Mi:dm K 3.0 Pro also offers lower per-token costs for high-volume support

Hy4

Long document analysis

Larger context window (1049K tokens) can process longer documents, contracts, and research papers in a single pass

Hy4

Batch data extraction

Lower output pricing ($0.80/M) reduces costs when processing thousands of records daily

Mi:dm K 3.0 Pro

Creative writing & content

Higher overall composite score (40/100) correlates with better nuance, coherence, and style in long-form content

Hy4

Image understanding & OCR

Supports vision input - can analyze screenshots, diagrams, photos, and scanned documents directly

Hy4
Which Should You Choose?
Our recommendation:
Hy4

Hy4 and Mi:dm K 3.0 Pro are extremely close in overall performance (only 0 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.

Hy4
Recommended

by Tencent

  • Choose for Quality - Marginally better benchmark scores; both are excellent
  • Choose for Reliability - Higher uptime and faster response speeds
  • Choose for Prototyping - Stronger community support and better developer experience
  • Choose for Production - Wider enterprise adoption and proven at scale

by kt

  • Choose for Cost - 40% lower pricing; better value at scale
Capability Comparison
CapabilityHy4Mi:dm K 3.0 Pro
Vision (Image Input)differs
Function Callingdiffers
Streaming
JSON Mode
Reasoning
Web Search
Image Output
Monthly Cost Calculator
1,000tokens (600 in / 400 out)
100requests/day (3,000/month)

Hy4

Tencent

$2.25
estimated monthly cost

Mi:dm K 3.0 Pro

kt

Best Value
$1.32
estimated monthly cost

Mi:dm K 3.0 Pro saves you $0.9312/month

That's 41% cheaper than Hy4 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.

Parameters & Context
ParameterHy4Mi:dm K 3.0 Pro
Context Window1.0M262K
Max Output Tokens65,536235,615
Open SourceYesNo
CreatedNov 4, 2026Nov 5, 2026
Last updated: 15m ago

Related comparisons

Hy4 vs Mi:dm K 3.0 Pro (2026) | LM Market Cap