Tencent Hy3 vs DeepSeek V4: Chinese Coding Models Compared

Two Chinese models. Both excellent at code. Very different architectures, different strengths, and different deployment stories. Tencent Hy3 (released July 6, 2026) brings a 295B MoE with only 21B active parameters. DeepSeek V4 Pro is a dense model with a longer track record and larger community.

This comparison helps you decide which one fits your coding workflow.

Specs at a Glance

Spec	Tencent Hy3	DeepSeek V4 Pro
Architecture	MoE (192 experts, top-8)	Dense
Total parameters	295B	~670B (estimated)
Active parameters	21B	Full model
Context window	256K	128K
SWE-bench Verified	74.4%	~72%
Coding score	47.37	~47 (Pro variant)
License	Apache 2.0	Open (custom)
API pricing (OpenRouter)	~$0.14/M input	~$0.27/M input
Hugging Face weights	Yes (tencent/Hy3)	Yes
Release date	July 6, 2026 (full)	May 2026

Coding Performance

Both models score similarly on coding benchmarks. Hy3 at 47.37 coding score is on par with DeepSeek-V4-Pro. On SWE-bench Verified, Hy3’s 74.4% slightly edges out DeepSeek V4’s approximately 72%.

In practical coding, both handle:

• Multi-file refactoring
• Bug diagnosis and fixes
• Test generation
• Code explanation
• Architecture suggestions

Where they differ:

Hy3 advantages in coding:

• Better performance on very long code contexts (256K vs 128K window)
• Slightly more concise output
• Faster inference due to smaller active parameter count
• Better at Chinese-language codebases and documentation

DeepSeek V4 advantages in coding:

• More consistent across diverse languages (wider training distribution)
• Better at explaining complex algorithms in English
• More established tooling ecosystem
• Stronger on math-heavy code (algorithms, scientific computing)

For standard web development, backend services, and typical enterprise code, the difference is marginal. Choose based on other factors.

Architecture: MoE vs Dense

This is the fundamental technical difference and it affects everything.

Hy3 (MoE): 192 experts with top-8 routing means only 21B parameters activate per token. Benefits:

• Lower inference compute (faster, cheaper per token)
• Specialized experts for different domains
• Better parameter efficiency (more knowledge per active parameter)
• Easier to serve at scale

DeepSeek V4 (Dense): Every parameter participates in every token. Benefits:

• More consistent behavior across all inputs
• No routing errors (all knowledge is always available)
• Simpler to fine-tune (no expert balancing concerns)
• Better understood architecture

The practical impact: Hy3 is cheaper and faster to run. DeepSeek V4 is more predictable in behavior. For API access, this translates to Hy3’s $0.14/M vs DeepSeek’s $0.27/M on OpenRouter.

For teams running locally, the MoE architecture means Hy3 needs less compute per request but more total memory (all 295B parameters must be in memory even though only 21B activate). DeepSeek V4’s full model is larger but you always use everything you load.

See our guides on running DeepSeek V4 locally and running Hy3 locally for hardware specifics.

Context Window: 256K vs 128K

Hy3 offers double the context of DeepSeek V4 (256K vs 128K tokens). For coding tasks, this matters when:

• Working with large monorepos where you need many files in context
• Analyzing long codebases for architecture decisions
• Processing full test suites alongside implementation
• Handling complex multi-file refactoring

For typical single-file edits and focused tasks, both windows are more than adequate. The difference shows up in large-scale work.

Licensing

Hy3: Apache 2.0. Clean, simple, unrestricted. Use it anywhere, for anything, commercially, with no geographic limitations. This is a deliberate departure from Tencent’s previous more restrictive releases.

DeepSeek V4: Open weights with a custom license. Still permissive for most use cases, but check the specific terms for your deployment scenario. Some conditions may apply depending on usage scale.

For companies building commercial products, Hy3’s Apache 2.0 removes legal ambiguity entirely. For research and personal use, both licenses work fine.

Community and Ecosystem

DeepSeek V4 has a head start here. Released before Hy3, it has:

• More fine-tuned variants available
• More community guides and tutorials
• Better representation in tool integrations (some IDEs and coding tools added DeepSeek support first)
• Larger community on Discord, GitHub, forums
• More established in the open-source coding model landscape

Hy3 is newer but backed by Tencent’s resources. Expect the ecosystem to catch up quickly given:

• Apache 2.0 encourages community contributions
• Tencent is actively promoting adoption
• The model’s efficiency makes it attractive for tool builders
• Production validation across 50+ Tencent products gives confidence

Inference Cost

For API access through providers like OpenRouter:

• Hy3: ~$0.14/M input tokens
• DeepSeek V4 Pro: ~$0.27/M input tokens

Hy3 is roughly half the price. Over significant usage, this adds up. For the full pricing picture across models, see our AI API pricing comparison.

For self-hosted inference, the cost comparison is more nuanced:

• Hy3 needs more memory (295B total params to load) but less compute per token
• DeepSeek V4 needs less memory relative to compute but uses all parameters

Total cost of ownership depends on your inference pattern. High-throughput serving favors Hy3’s efficiency. Low-volume use cases may favor DeepSeek V4’s simpler deployment.

Hybrid Thinking (Hy3) vs Standard Inference (DeepSeek V4)

Hy3 features “hybrid fast-and-slow thinking” built into its architecture. The model automatically decides when to reason deeply and when to respond quickly based on input complexity. Complex coding problems get more reasoning steps. Simple completions get fast responses.

DeepSeek V4 offers a more uniform inference approach. Reasoning models in the DeepSeek family exist (DeepSeek-R1 lineage), but V4 Pro does not have the same adaptive reasoning built in.

For coding tasks that vary in complexity (some quick completions, some deep debugging), Hy3’s adaptive approach can be more efficient. You get fast responses for simple tasks without configuring anything.

Real-World Recommendation

Choose Tencent Hy3 if:

• Cost efficiency is paramount (half the API price)
• You need longer context (256K vs 128K)
• You are building commercial products (Apache 2.0 clarity)
• You work with Chinese-language codebases
• You want the fastest responses (21B active params)
• You value adaptive reasoning without configuration

Choose DeepSeek V4 if:

• You need a more established ecosystem and community
• Consistency across all tasks matters more than peak efficiency
• You work primarily with English-language code and documentation
• You prefer a model with more fine-tuned variants available
• You do math-heavy or algorithmic work
• You already have DeepSeek integrated in your workflow

For most developers: If you are starting fresh and cost matters, Hy3 is the better default choice as of July 2026. The performance is equivalent, the price is lower, the context is longer, and the license is cleaner. DeepSeek V4 remains excellent but Hy3 offers more for less.

For comparisons with Western models, see how Chinese models stack up against Claude Opus 4.8 and Claude Sonnet 5.

The Bigger Picture

Both Hy3 and DeepSeek V4 demonstrate that the Chinese AI ecosystem is producing world-class coding models. They compete not just with each other but with closed Western models costing 10-20x more per token.

The choice between them is increasingly about ecosystem preference and deployment specifics rather than capability gaps. Both are excellent. The market benefits from having both.

FAQ

Which model is better for coding?

They are extremely close. Hy3 edges slightly ahead on SWE-bench Verified (74.4% vs ~72%) and costs less. DeepSeek V4 is more consistent and has better English documentation output. For most coding tasks, the difference is negligible.

Can I fine-tune both models?

Yes. Both have open weights on Hugging Face. Hy3’s MoE architecture makes fine-tuning more complex (you need to handle expert routing), while DeepSeek V4’s dense architecture is more straightforward to fine-tune with standard tools.

Which is easier to run locally?

DeepSeek V4 is simpler to set up but needs more compute per token. Hy3 needs more total memory (295B params to load) but less compute per inference step. For the hardware details, see our guides on running models locally and VRAM requirements.

Do both support Chinese and English equally well?

Both handle English code fluently. Hy3 has a slight edge on Chinese-language content (given Tencent’s internal Chinese-language products). DeepSeek V4 has marginally better English natural language output. For code itself (which is mostly English keywords regardless), both perform equally.

Are there concerns about using Chinese AI models?

For open-weight models running on your own infrastructure, supply chain concerns are minimal since you can inspect the model. For API access, standard API trust considerations apply. The Apache 2.0 license has no hidden conditions.