How fast is N tokens/s? Open source visualization tools let you personally experience the speed of AI model inference
Developer Mike Veerman released the tokenspeed open source tool, which allows users to intuitively experience the real speed difference of 5-800 tok/s through visual simulation. The tool supports four modes: code/text/inference/Agent, which reveals the huge difference in perception of the same tok/s value under different output types, and has direct reference value for AI tool selection and local model deployment decisions.
Core conclusion
Numbers such as "47 tok/s" and "180 tok/s" that are common in LLM benchmark reports are just an abstract concept for most users. Recently, developer Mike Veerman released an open source interactive tool tokenspeed, which simulates different token rates in four modes (code/text/inference/Agent), allowing users to intuitively feel what it means to go from 5 tok/s to 800 tok/s.
Key findings: The perceived speed of the same tok/s value varies greatly under different content types - code consumes more tokens than text, and inference mode consumes 2-3 times more tokens than pure code. This has a direct impact on AI coding tool selection and local model deployment decisions.
Key Points
- Event: tokenspeed visualization tool released (Hacker News community rating 318)
- Access address: mikeveerman.github.io/tokenspeed/
- Core Function: 4 modes to simulate different LLM output rates, preset 9 speeds (5-800 tok/s)
- Affected objects: Developers who use AI coding tools, local model deployers, AI tool selection decision makers
Background: Why we need to "feel" token speed
When LLM industry reviews are published, they are always accompanied by token/s numbers. But "30 tok/s" is just an abstract number to most people - unless you watch tokens flow across the screen at that speed.
Just like the upgrade from 1200 bps dial-up to 28.8k - the difference in numbers is far less intuitive than the actual experience. tokenspeed fills this gap: allowing you to accurately experience the real "feel" of different speeds in three typical output modes (code, text, reasoning).
The tool received a score of 318 on Hacker News, with community comments stating: **Most people get the benchmark number and don’t know what it means in their actual usage scenarios. ** This is exactly what tokenspeed exists for.
Key Impact
| Dimensions | Change | What it means to us | Recommended actions |
|---|---|---|---|
| Local model selection | 5-30 tok/s range is intuitive and perceptible | Local deployment (such as LM Studio) 30 tok/s is not enough for code fluency | Prioritize the use of cloud models (60-200 tok/s) to handle coding tasks |
| Cloud API selection | 60 vs 200 vs 800 tok/s huge difference | Claude/GPT's 60 tok/s feels much slower than text in code mode | When evaluating, you should specify "code mode tok/s" rather than general tok/s |
| Agent workflow design | Inference mode consumes 2-3x tokens | A long inference chain may double the Agent response time | Set a reasonable thinking budget for the inference model |
| Cost vs experience balance | 800 tok/s (Cerebras level) is far faster than human reading speed | High-speed models are significantly improved in code generation scenarios, but wasteful in reading scenarios | Choose models of different speed levels according to task types |
Adaptation suggestions
Tool selection: token speed should be included in the evaluation criteria
When you're choosing between Claude Code and DeepSeek V4, tok/s is a key and underrated metric. A simple decision-making framework:
- 5-30 tok/s (local model) → suitable for text summary and simple chat; not suitable for coding
- 60-100 tok/s (Claude, GPT-4 level) → suitable for medium complexity encoding; requires patience to wait for long output
- 200+ tok/s (Groq, Cerebras level) → Suitable for high-paced coding Agent workflow
Use tokenspeed for actual testing
Test your working scenario with tokenspeed's code mode:
- Open tokenspeed
- Switch to
c(code mode) to observe the code flow with syntax highlighting - Switch to
a(Agent mode) to observe the alternating rhythm of tool calling + code generation - Switch between 5 and 10 (native model), then jump to preset 6 (60 tok/s Claude level) to feel the difference
Related extended information
- Modal Token Timing Simulator (similar tool, focusing on UI rendering comparison)
- GitHub
Tool entry (trigger tool floating card)
Tools involved in this article: Claude Code, OpenAI, ChatGPT, Gemini, DeepSeek, LM Studio, n8n, Hermes Agent
Internal link guidance
- Want to learn about local model deployment? Watch: How to run local AI models on M4 Mac with LM Studio: A complete 30-minute tutorial
- Real case: He used Claude Code + AWS to build AI SaaS, and the token speed directly affects his productivity: He used Claude Code + AWS to build AI SaaS, and his monthly income was $12,000 for 3 months
- Want to know how to use n8n to build automated processes with different models? Watch: How to build an AI content automated distribution system with n8n + ChatGPT: a complete 30-minute tutorial
Monetization angle
How can you make money from this trend?
WayToClawEarn focuses on verified earn playbooks—not just news. Start from these cases.
n8n + OpenAI affiliate site
Automate content and affiliate monetization
Claude + n8n automation agency
Charge monthly for agent workflow builds