April 2026 has been an extraordinary month for Chinese open-source AI. Within 10 days, three of China’s top AI labs released flagship models that rival the best closed-source alternatives: DeepSeek V4 (April 24), Kimi K2.6 (April 20), and GLM-5.1 (March 27 / open-sourced April 8). Each takes a fundamentally different approach to solving the hardest problems in AI.
If you are a developer or team trying to choose between these three models — or simply want to understand where Chinese AI stands globally — this comparison breaks down their architecture, benchmarks, pricing, strengths, and ideal use cases.
Quick Comparison Table
| Feature | DeepSeek V4-Pro | Kimi K2.6 | GLM-5.1 |
|---|---|---|---|
| Company | DeepSeek (深度求索) | Moonshot AI (月之暗面) | Zhipu AI (智谱) |
| Release Date | April 24, 2026 | April 20, 2026 | March 27, 2026 |
| Total Parameters | ~1.6T (MoE) | ~1.1T (MoE) | ~744B (MoE) |
| Active Parameters | 49B | Not disclosed | 40-44B |
| Experts / Active | 128 / 4 | Not disclosed | 256 / 8 |
| Context Window | 1M tokens | 262K tokens | 200K tokens |
| Max Output | 384K tokens | Not disclosed | 128K tokens |
| Open Source | MIT | Yes (Hugging Face) | MIT |
| Training Hardware | NVIDIA + Huawei Ascend | NVIDIA | 100K Huawei Ascend 910B only |
| Multimodal | Text only (preview) | Text + Vision | Text only |
Benchmark Showdown
These three models compete across coding, reasoning, and agent benchmarks. Here is how they stack up against each other and against top closed-source models:
Coding Benchmarks (SWE-Bench Series)
| Benchmark | DeepSeek V4-Pro | Kimi K2.6 | GLM-5.1 | GPT-5.4 | Claude Opus 4.6 |
|---|---|---|---|---|---|
| SWE-Bench Pro | ~58% | 58.6% | 58.4% | 57.7% | 53.4% |
| SWE-Bench Verified | ~80% | 80.2% | 77.8% | — | 80.8% |
| SWE-Bench Multilingual | — | 76.7% | — | — | 77.8% |
| Terminal-Bench 2.0 | — | 66.7% | 63.5% | 65.4% | 65.4% |
| LiveCodeBench (v6) | ~72.5% | 89.6% | 52.0% | — | 88.8% |
All three Chinese models beat GPT-5.4 and Claude Opus 4.6 on SWE-Bench Pro — a remarkable achievement. Kimi K2.6 leads narrowly at 58.6%, followed by GLM-5.1 at 58.4% and DeepSeek V4-Pro close behind. However, Claude Opus 4.6 retains its edge on SWE-Bench Verified.
Reasoning and Knowledge
| Benchmark | DeepSeek V4-Pro | Kimi K2.6 | GLM-5.1 | GPT-5.4 | Gemini 3.1 Pro |
|---|---|---|---|---|---|
| AIME 2026 | ~96% | 96.4% | 95.3% | 99.2% | 98.3% |
| GPQA Diamond | ~72.8% | 90.5% | 86.0% | 92.8% | 94.3% |
| HLE w/ tools | — | 54.0% | 52.3% | 52.1% | 51.4% |
| HLE (pure reasoning) | — | 34.7% | — | 39.8% | 44.4% |
| DeepSearchQA (F1) | — | 92.5% | — | 78.6% | 81.9% |
KimK2.6 dominates the HLE (Humanity’s Last Exam) with tools benchmark and DeepSearchQA, showing superior agent-powered reasoning. GLM-5.1 has the highest AIME score among Chinese models at 95.3%. Pure reasoning without tools remains a gap for all three compared to Gemini 3.1 Pro.
Pricing Comparison: Who Is Cheapest?
Pricing is where these models diverge most dramatically:
| Model | Input ($/1M tokens) | Output ($/1M tokens) | Notes |
|---|---|---|---|
| DeepSeek V4-Flash | $0.14 | $0.28 | Cache hit: $0.03 input |
| DeepSeek V4-Pro | $1.74 | $3.48 | Cache hit: $0.14 input |
| GLM-5.1 | $1.40 | $4.40 | Subscription: $3-15/month |
| Kimi K2.6 | ~$1.50 (est.) | ~$5.00 (est.) | API + 30% top-up promo |
| Claude Opus 4.7 | $5.00 | $25.00 | Reference |
| GPT-5.5 | $5.00 | $30.00 | Reference |
DeepSeek V4-Flash is the undisputed price champion — at $0.28 per million output tokens, it costs roughly 1% of Claude Opus 4.7. For budget-conscious teams processing large volumes, the Flash tier is almost too cheap to ignore.
GLM-5.1 offers a compelling middle ground with its subscription plans ($3/month Lite, $15/month Pro), while Kimi K2.6 sits at a similar price point with aggressive promotional pricing.
Architecture: Three Different Paths to the Same Goal
DeepSeek V4: Memory-Compute Separation
DeepSeek V4 introduces the most architecturally radical innovation: Engram conditional memory. Inspired by the brain’s hippocampus, Engram provides an O(1) external knowledge lookup system that feeds factual information directly into the transformer backbone. This “memory-compute separation” means the model doesn’t need to memorize everything in its weights — it can look up facts instantly.
Combined with mHC (Manifold-Constrained Hyper-Connections) for training stability at 1.6T parameters and DSA (DeepSeek Sparse Attention) for efficient 1M context processing, V4 represents a paradigm shift from “scale up” to “architect smarter.”
Kimi K2.6: Agent Swarm Architecture
Kimi K2.6’s standout feature is its Agent Swarm capability. It can orchestrate up to 300 sub-agents running in parallel, executing approximately 4,000 collaborative steps. This “scale out, not up” approach means complex tasks are dynamically decomposed and distributed across specialized agents that work concurrently.
K2.6 is the only model among the three with integrated vision capabilities, allowing it to generate professional web applications with design-quality UIs by combining code with image and video generation tools. It also features a “Skill” system — users can create reusable capabilities from uploaded documents.
GLM-5.1: Pure Huawei Ascend, Long-Horizon Focus
GLM-5.1 is the only model trained entirely on Huawei Ascend 910B chips — 100,000 of them, with zero NVIDIA GPUs. Its core innovation is long-horizon task execution: the ability to work autonomously for 8+ hours on complex engineering tasks.
Using its Slime asynchronous reinforcement learning framework, GLM-5.1 achieves “run longer, perform better” behavior — unlike most models that degrade over long sessions, GLM-5.1 improves through sustained iteration. The model demonstrated 655 rounds of autonomous optimization on a vector database (6x throughput improvement) and built a complete Linux desktop environment from scratch.
Strengths and Weaknesses
DeepSeek V4
- Biggest strength: 1M token context window (5x longer than competitors), Engram anti-hallucination system, lowest pricing
- Biggest weakness: No multimodal support, preview version occasionally unstable, Pro tier throughput limited
- Best for: Processing massive documents, building RAG systems, cost-sensitive development, long-context coding tasks
Kimi K2.6
- Biggest strength: Agent Swarm (300 parallel sub-agents), vision integration, web/app design capabilities, longest continuous coding sessions (13+ hours), leading DeepSearchQA scores
- Biggest weakness: Higher pricing than DeepSeek, API raised 58% from K2.5, pure reasoning trails Gemini 3.1 Pro by a wide margin
- Best for: Multi-agent orchestration, complex research workflows, full-stack web development with design, long-running autonomous tasks
GLM-5.1
- Biggest strength: SWE-Bench Pro global #1, 8+ hour autonomous execution, fully Huawei-trained (no NVIDIA dependency), most affordable per-token coding model
- Biggest weakness: 200K context (smallest of the three), no multimodal, hallucination risk increases in very long sessions, self-reported benchmarks need independent verification
- Best for: Software engineering tasks, autonomous coding agents, enterprises requiring domestic chip deployment, long-running optimization tasks
Which Model Should You Choose?
| Your Priority | Choose | Why |
|---|---|---|
| Lowest cost, largest context | DeepSeek V4-Flash | $0.28/M output, 1M tokens — nothing else comes close |
| Multi-agent orchestration | Kimi K2.6 | 300 sub-agents, 4,000 steps, Claw Groups platform |
| SWE-Bench / bug fixing | GLM-5.1 | Highest SWE-Bench Pro score globally |
| Vision + code | Kimi K2.6 | Only one with integrated vision capabilities |
| 8+ hour autonomous tasks | GLM-5.1 | Only open-source model verified for 8-hour continuous work |
| Anti-hallucination | DeepSeek V4 | Engram memory module achieves 97% NIAH accuracy |
| Full-stack app building | Kimi K2.6 | Frontend + backend + design in one agent workflow |
| NVIDIA-free deployment | GLM-5.1 | Trained and optimized for Huawei Ascend ecosystem |
| Math & data analysis | DeepSeek V4-Pro | MATH-500 ~96.1, approaches GPT-5 |
The Bigger Picture: Chinese Open-Source AI in April 2026
These three releases collectively signal a turning point. A year ago, the gap between open-source and closed-source models was measured in years. Today, Chinese open-source models match or exceed GPT-5.4 and Claude Opus 4.6 on specific benchmarks — coding, agent tasks, and deep search — at a fraction of the cost.
Each model chose a different path to differentiation:
- DeepSeek bet on architectural innovation (Engram, mHC, DSA) and extreme cost efficiency
- Kimi bet on multi-agent orchestration and vision-language integration
- GLM bet on long-horizon autonomous execution and domestic hardware independence
All three are MIT-licensed, all three are available on Hugging Face, and all three offer APIs at prices that make Claude and GPT look expensive by comparison.
For developers, this means more choice, lower costs, and less dependence on any single provider. The era of “one model dominates everything” is over. The question is no longer whether open-source can compete — it is which open-source model fits your specific use case best.
Frequently Asked Questions
Can I run these models locally?
DeepSeek V4-Pro requires approximately 4x A100 80GB. GLM-5.1 needs at least 2x H100 80GB. Kimi K2.6’s full weights are similarly demanding. For individual developers, the API is the practical choice. DeepSeek V4-Flash is the most feasible for local deployment due to its smaller size (284B total, 13B active).
Which model has the best Chinese language support?
All three are Chinese models with excellent Chinese language capabilities. DeepSeek and GLM tend to be slightly stronger in Chinese technical writing, while Kimi excels in Chinese-language research and content creation workflows.
How do these compare to GPT-5.5?
GPT-5.5 (released April 24, same day as DeepSeek V4) remains stronger in multimodal tasks, general reasoning (especially pure math), and ecosystem integration. However, for coding and agent tasks, all three Chinese models are competitive — and at far lower prices. We covered this in our GPT-5.5 vs DeepSeek V4 comparison.
Which one should I use for building AI agents?
For multi-agent orchestration, Kimi K2.6’s 300-agent Swarm is unmatched. For long-running single-agent coding tasks, GLM-5.1’s 8-hour endurance wins. For agents that need massive context (reading entire codebases or legal contracts), DeepSeek V4’s 1M window is the clear choice. Many teams are already combining them: DeepSeek for context, Kimi for orchestration, GLM for sustained execution.