Core Thesis
In early May 2026, a notable paradigm shift emerged in the Chinese AI model community: from “competing on reasoning length” to “competing on token efficiency.”
While closed-source giants keep stacking inference performance with increasingly long chains-of-thought, Ant Group’s open-source Ling-2.6-1T has played an entirely different card — a “fast thinking” execution mode: less talk, more work. This is not a slogan but an architectural-level differentiation.
What Exactly Is Ling-2.6-1T’s “Fast Thinking”
Ling-2.6-1T is a MoE model with approximately 1 trillion total parameters, activating only 63 billion (63B) per inference. Compared to American models of similar parameter scale, its core differentiator is not capability ceiling but execution path efficiency.
The typical behavior pattern of closed-source models: when facing an agent task, they perform extensive internal reasoning (potentially generating tens of thousands of reasoning tokens) before outputting results. It’s like asking a programmer to write a 5,000-word design document before writing code — useful, but expensive.
Ling-2.6-1T’s design philosophy flips this around:
If 10 tokens can solve it, never use 100.
The core advantage of this “fast thinking” mode shines brightest in agent scenarios:
| Scenario | Typical Closed-Source Token Usage | Ling-2.6-1T Token Usage |
|---|---|---|
| Code Bug Fix | 5,000-20,000 | 1,500-5,000 |
| Multi-Step Agent Orchestration | 30,000-100,000 | 8,000-25,000 |
| Simple Tool Call | 2,000-8,000 | 500-2,000 |
One developer summarized it perfectly after testing: “Closed-source models perform thinking; Ling just does the work.”
Xiaomi MiMo-V2.5-Pro: Same Philosophy, Different Entry Point
Xiaomi’s open-source MiMo-V2.5-Pro (1T parameters, Code Agent specialized) follows a similar route. Its core selling point is 1M context window + extreme token efficiency, with benchmark results directly targeting DeepSeek V4 Pro and Kimi K2.6.
What makes MiMo-V2.5-Pro unique is its token compression optimization for code scenarios:
- In code completion scenarios, pre-trained code structure understanding reduces massive redundant context repetition
- In multi-turn coding conversations, code AST awareness compresses historical conversation token overhead
- MIT license + commercial use support means enterprises can deploy directly without licensing concerns
DeepSeek’s Token Efficiency Legacy
This route can actually be traced back to DeepSeek. DeepSeek V4’s MoE architecture (~1T parameters / ~37B activated) was itself a token efficiency revolution — achieving maximum capability output with minimum activation parameters.
Since then, Chinese models have followed suit:
| Model | Total Parameters | Activated Parameters | Activation Rate | Core Strategy |
|---|---|---|---|---|
| DeepSeek V4 | ~1T | ~37B | ~3.7% | Extreme MoE routing |
| Ling-2.6-Flash | 104B | 7.4B | ~7.1% | Lightweight agent |
| Ling-2.6-1T | ~1T | ~63B | ~6.3% | Fast thinking execution |
| MiMo-V2.5-Pro | ~1T | ~80B | ~8% | Code scenario optimization |
In contrast, the design philosophy of mainstream American models leans toward “using more tokens to exchange for higher quality output” — which is indeed advantageous for creative writing and complex reasoning scenarios, but becomes a cost black hole in high-frequency agent scenarios.
Why Token Efficiency Is Becoming a Core Competitiveness
Three real-world factors are driving this trend:
1. Exponential Token Consumption in Agent Scenarios
A typical agent workflow (plan → execute → check → correct → complete) may involve 5-10 rounds of model calls. If each round generates a large amount of reasoning tokens, total costs can easily exceed the budget by 10x.
One developer did the math: running a moderately complex coding agent task with a certain closed-source model, daily token consumption could exceed $50; switching to a token-efficiency-optimized Chinese model brought the same task cost down to $3-5.
2. Subscription Model Cost Ceiling
Currently, domestic models’ Coding Plan Max (~¥80/month or $80/month) can already support 800 million tokens per month for heavy agent usage. But if you’re using a model with high token consumption, 800 million tokens might only cover a few hundred complex agent tasks; with a token-efficient model, the same budget can handle thousands of tasks.
3. Edge Deployment Needs
With the popularization of local inference tools like Ollama, more developers want to run large models on consumer-grade hardware. Token-efficient models mean:
- Lower VRAM requirements
- Faster inference speed
- Better fit for Jetson, RTX, and other edge devices
Does This Mean “Reasoning Length” Doesn’t Matter Anymore?
No. This is a question of scenario segmentation.
- Complex reasoning, scientific research, long-form writing: Longer reasoning chains still have value
- Agent orchestration, code generation, tool calling: Token efficiency is the more critical metric
The current strategy of Chinese models is to first dominate the efficiency advantage in agent scenarios, then gradually extend upward to more complex reasoning tasks. This is a pragmatic route — first build a user base in high-frequency, low-cost scenarios, then gradually improve capability ceilings.
Industry Impact: The Moat May Be Shifting
A developer’s comment on social media hit the nail on the head:
“While everyone is competing on parameters, reasoning scores, and longer contexts, only it goes the opposite direction, pushing token efficiency to the extreme. The moat is crumbling.”
The background of this statement: closed-source models’ “moat” is largely built on high inference costs — because they require significant compute to support verbose reasoning processes. Once open-source models can deliver comparable capabilities in key scenarios at 1/10th the cost, this moat starts to leak.
Selection Recommendations
| Scenario | Recommended Strategy |
|---|---|
| Heavy agent workflows | Ling-2.6-1T or MiMo-V2.5-Pro, lowest token cost |
| Daily coding assistance | Ling-2.6-Flash (7.4B activated, ultra-lightweight) |
| Complex reasoning tasks | DeepSeek V4 Pro or Kimi K2.6, better reasoning depth |
| Local deployment | Quantized versions on Ollama, Ling-2.6-Flash INT4 requires only ~4GB VRAM |
Summary
Chinese models in 2026 are forging a different path from their American counterparts: not competing on parameter scale or reasoning length, but using extreme token efficiency to build competitive advantages in agent scenarios.
This is not a compromise — it’s a more pragmatic technology route choice. In most practical application scenarios, users don’t need “thinking AI,” they need “AI that works efficiently.”
Whether this route ultimately succeeds depends on one core question: when token efficiency is high enough, can “fast thinking” model output quality approach “slow thinking” models?
Based on current benchmark data (Ling-2.6-1T SWE-Bench Verified 67, MiMo-V2.5-Pro targeting DeepSeek V4 Pro), the answer is very close. And the cost difference behind that may be decisive.