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DeepSeek V4 Pro vs Hy3

How do these models stack up? Below is an expert side-by-side comparison of specifications, context window capacity, live pricing per million tokens, and standardized benchmark scores for DeepSeek V4 Pro and Hy3.

DeepSeek

DeepSeek V4 Pro

DeepSeek V4 Pro is a large-scale Mixture-of-Experts model from DeepSeek with 1.6T total parameters and 49B activated parameters, supporting a 1M-token context window. It is designed for advanced reasoning, coding,...

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Tencent

Hy3

Hy3 is a 295B-parameter Mixture-of-Experts model from Tencent (21B active, 192 experts with top-8 routing) built for reasoning, agentic workflows, and real-world production use. It supports a configurable reasoning effort:...

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Technical Specifications

SpecificationDeepSeek V4 ProHy3
ProviderDeepSeekTencent
Context Window1,048,576 tokens262,144 tokens
Agent Suitability94/100N/A
Time to First Token (TTFT)280 msN/A
Deployment Modelmanaged apimanaged api
Production Stabilitystablestable
API AvailableYesYes
Released Date2026-04-242026-07-06

API Pricing Comparison

Input Price per Million Tokens

DeepSeek V4 Pro

$0.43

Hy3

$0.20

Output Price per Million Tokens

DeepSeek V4 Pro

$0.87

Hy3

$0.80

Want to test both models live?

Run side-by-side prompt prompts in our dynamic Sandbox. Check execution speeds, latency metrics, and compute actual costs in real-time.

Benchmark Performance Metrics

Scores show the raw performance percentages verified across key evaluation suites. Higher bars indicate superior accuracy and capability in that domain.

MMLUGeneral knowledge & multi-task understanding
8850.0%vsN/A
DeepSeek V4 Pro
Hy3
HumanEvalPython coding & logic synthesis
8900.0%vsN/A
DeepSeek V4 Pro
Hy3
MATHComplex mathematical problem solving
7460.0%vsN/A
DeepSeek V4 Pro
Hy3
GPQAGraduate-level expert reasoning
4900.0%vsN/A
DeepSeek V4 Pro
Hy3
HellaSwagCommonsense reasoning and inference
8750.0%vsN/A
DeepSeek V4 Pro
Hy3
MT-BenchMulti-turn conversation flow quality
918.0%vsN/A
DeepSeek V4 Pro
Hy3

DeepSeek V4 Pro Quirks & Gotchas

  • โ–ธMoE architecture โ€” cold-start latency on first request, use keep-alive
  • โ–ธBest cost-performance ratio of any frontier model โ€” strong tool calling for agentic use

Hy3 Quirks & Gotchas

No developer gotchas reported.