Kimi K3 vs Tencent Hy3: Open-Weight Chinese Giants Head to Head
Two Chinese open-weight models now compete at the global frontier, but they win on completely different benchmarks. Kimi K3 scores 88.3% on Terminal-Bench 2.1 (rank #2 globally). Tencent Hy3 scores 74.4% on SWE-Verified, leading that benchmark.
These models are not interchangeable. They excel at different things, cost wildly different amounts, and suit different workflows. Here is how to choose between them.
Benchmark Comparison
| Benchmark | Kimi K3 | Tencent Hy3 |
|---|---|---|
| Terminal-Bench 2.1 | 88.3% (#2) | Lower |
| SWE-Verified | Strong | 74.4% (leader) |
| DeepSWE | 67.5% | Lower |
| Frontend Code Arena | #1 | Not ranked high |
| Intelligence Index | 57.1 (#3) | Lower |
| Context Window | 1M tokens | Large |
| Vision | Native | Limited |
K3 dominates on Terminal-Bench, general intelligence, and frontend code. Hy3 leads on SWE-Verified. These benchmarks measure different things, so understanding what each tests is critical.
Terminal-Bench 2.1 measures real-world terminal task completion: executing commands, reading output, iterating on solutions, managing systems. K3 excels here because its architecture handles multi-step interactive workflows well.
SWE-Verified measures the ability to fix real GitHub issues from popular open-source projects. Hy3’s 74.4% score means it resolves nearly three-quarters of real bug reports successfully. This requires understanding existing codebases, identifying root causes, and writing minimal, correct patches.
The Pricing Gap
| Kimi K3 | Tencent Hy3 | |
|---|---|---|
| Input | $3/M tokens | $0.14/M tokens |
| Output | $15/M tokens | $0.14/M tokens |
| Cache | $0.30/M (90%+ hit rate) | N/A |
This is not a subtle difference. Hy3 is over 100x cheaper on output. Even with K3’s cache bringing effective input costs down to ~$0.57/M, Hy3 remains dramatically cheaper.
For budget comparisons across all models, see our AI API pricing comparison.
The question is whether K3’s performance premium justifies 100x the output cost. The answer depends entirely on your workload and how much your time is worth.
Where K3 Wins Clearly
Terminal and CLI Workflows
K3 at 88.3% Terminal-Bench handles interactive terminal sessions, build systems, deployment pipelines, and system administration at a level Hy3 cannot match. If your work involves heavy terminal interaction, K3 is the better choice by a large margin.
Frontend Development
Being #1 on Frontend Code Arena means K3 produces the best frontend code of any model available. React components, CSS architecture, accessibility patterns, responsive design. K3 handles all of it better than Hy3.
Complex Reasoning
The Intelligence Index gap (57.1 for K3 vs Hy3’s lower score) means K3 handles ambiguous problems, architectural decisions, and multi-domain reasoning better. For tasks that require creativity and broad knowledge synthesis, K3 has a significant advantage.
Vision-Based Workflows
K3 has native vision support. You can share screenshots of UI bugs, architecture diagrams, mockups, and visual documentation. Hy3 has limited visual capabilities. If your workflow involves visual input, K3 is the only choice.
Very Long Context
K3’s 1M token window with aggressive caching means you can load massive codebases affordably. The cache at $0.30/M makes long-context usage economically viable over extended sessions.
Where Hy3 Wins Clearly
Bug Fixing in Existing Codebases
Hy3’s 74.4% SWE-Verified score means it excels specifically at understanding existing code and writing correct patches. If your primary task is fixing bugs in established projects, Hy3 is purpose-built for this.
Cost-Sensitive Workloads
At $0.14/M for both input and output, Hy3 is accessible to teams where K3’s pricing is prohibitive. A task that costs $1 with K3 costs less than $0.01 with Hy3. For high-volume automated workflows (running the model on thousands of issues, for example), Hy3’s economics are unbeatable.
Batch Processing
If you need to process thousands of code files, generate tests for an entire codebase, or run automated code review across a large repository, Hy3’s pricing makes this feasible. The same workload on K3 would be 100x more expensive on output.
Open-Source Contributions
For automated PR generation, issue triage, or code review bots that run continuously, Hy3’s cost makes it practical to deploy at scale. K3 would bankrupt most budgets at this volume.
Real-World Task Comparison
Let me map specific developer tasks to model strengths:
| Task | Better Model | Why |
|---|---|---|
| Debugging a production issue via terminal | K3 | Terminal-Bench 88.3% |
| Fixing a bug in a GitHub repo | Hy3 | SWE-Verified 74.4% |
| Building a new React component | K3 | Frontend Code Arena #1 |
| Automated code review for 100 PRs | Hy3 | Cost: $0.14/M vs $15/M |
| Architectural design discussion | K3 | Intelligence Index 57.1 |
| Writing patches for open-source issues | Hy3 | SWE-Verified leader |
| Deploying and configuring infrastructure | K3 | Terminal-Bench leader |
| Bulk test generation | Hy3 | 100x cheaper |
| Visual UI debugging with screenshots | K3 | Native vision |
Using Both Models Together
The smartest approach for many teams is using both:
- K3 for interactive development: When you are actively building, debugging in terminal, designing architecture, or creating frontend components.
- Hy3 for automated bulk operations: Code review bots, test generation, batch fixes, automated refactoring across a codebase.
Both are available on OpenRouter, making it straightforward to route requests to the appropriate model based on task type.
For setup details on using K3 with coding tools, see our K3 Aider and Claude Code guide.
The Chinese AI Ecosystem Context
K3 and Hy3 represent the two peaks of Chinese AI for coding, with DeepSeek V4 Pro as a solid third option.
- Moonshot AI (K3): Frontier performance, global top 3, premium pricing
- Tencent (Hy3): SWE-Verified leader, ultra-budget pricing, batch-oriented
- DeepSeek (V4 Pro): Middle ground, large community, accessible
Together, these three models cover nearly every coding use case at various price points. The Chinese AI ecosystem is no longer a single-lab story.
For the complete picture, read our best Chinese AI models 2026 guide and best open-source coding models guide.
Migration and Integration
From Hy3 to K3
If you are upgrading from Hy3 to K3 for higher-quality interactive development:
- Switch model ID to
moonshotai/kimi-k3on OpenRouter - Restructure prompts for K3’s cache optimization (consistent prefixes)
- Budget for significantly higher costs (plan for $0.50-1.00 per meaningful interaction vs pennies with Hy3)
- Take advantage of K3’s vision by including visual context
From K3 to Hy3
If you are adding Hy3 for batch/automated workflows alongside K3:
- Identify tasks that run at high volume and do not need frontier intelligence
- Set up routing logic to send these to Hy3
- Accept slightly lower success rates in exchange for 100x cost reduction
- Monitor quality and escalate failures to K3
FAQ
Which is the better coding model overall?
Depends on what “coding” means to you. K3 wins on interactive terminal work (88.3% T-Bench), frontend code (#1), and general intelligence (57.1). Hy3 wins on fixing bugs in existing codebases (74.4% SWE-Verified) and is 100x cheaper. For most developers doing daily interactive development, K3 is better. For automated code maintenance at scale, Hy3 is better.
Can Hy3 replace K3 if budget is tight?
For some tasks, yes. Hy3 at $0.14/M handles straightforward coding tasks well. But on complex terminal workflows and frontend development, the quality gap is large. You will spend more time correcting Hy3’s mistakes, which may cost more in developer time than K3’s higher API bill.
Is K3’s cache advantage enough to close the pricing gap?
No. Even with 90% cache hits bringing K3’s effective input to ~$0.57/M, Hy3 at $0.14/M is still cheaper on input and dramatically cheaper on output ($15/M vs $0.14/M). The cache helps K3’s economics but cannot match Hy3’s ultra-budget pricing.
Are both models available globally?
Both are available through OpenRouter. K3 is accessible as moonshotai/kimi-k3. Check OpenRouter for current Hy3 availability in your region. Neither has the geographic restrictions that Muse Spark 1.1 has (US-only on OpenRouter).
Should I use both models in the same project?
Yes, this is often the optimal approach. Use K3 for complex interactive tasks where quality directly impacts your productivity, and Hy3 for high-volume automated tasks where the 100x cost savings matter more than marginal quality differences.