Multi-model Collaboration is an advanced Q&A mode on the GUID.AI chat page. After it is enabled, the system calls multiple participating models in parallel for the same question, lets each model answer separately, and then uses a summary model to aggregate the results into a more complete conclusion that is better suited for decision-making.
It is especially suitable for the following scenarios:
- Complex question verification
- Multi-solution comparison
- High-value content creation
- Professional cross-validation
- Tasks that need to balance quality, speed, and cost
Compared with normal single-model chat, multi-model collaboration provides broader perspectives, higher fault tolerance, and more stable conclusions. Note that it usually also brings higher consumption and longer response time.
1. What Is Multi-model Collaboration?
In normal chat, a question is usually answered by only one model.
In multi-model collaboration, one question goes through the following process:
- You select 2 to 5 participating models
- The system sends the same question to these models at the same time
- Each model independently outputs its own answer
- After all participating models finish answering, the system calls 1 summary model
- The summary model summarizes, compares, and refines the answers, then outputs the final collaborative conclusion
Simple explanation:
- Participating models are responsible for "thinking separately"
- Summary model is responsible for "integrating everything"
This mechanism significantly reduces the risk that a single model misses key information, misunderstands the question, or produces a one-sided answer.
2. Where Do I Enter It?
- Enter the platform Chat page.
- In the toolbar above the input box at the bottom of the page, find the
Multi-model Collaborationbutton. - Click it to open the Multi-model Collaboration configuration panel.
Before enabling, the button usually appears in a normal state. After enabling, it displays Multi-model Collaboration ยท Enabled and shows the current collaboration status near the toolbar.
3. Complete Usage Steps
Step 1: Select Participating Models
In the Participating Models area of the configuration panel, check the models you want to participate in answering.
Recommended rules in the current system:
- Select at least 2
- Select at most 5
Selection suggestions:
- For more stable comprehensive judgment: select 3 to 4 models with different styles
- To control cost: select 2 core models
- For in-depth comparison: mix reasoning, knowledge-oriented, and speed-oriented models
Common pairing ideas:
- Reasoning + general-purpose: suitable for solution analysis, code ideas, and complex Q&A
- General-purpose + fast: suitable for high-frequency business Q&A
- Mixed vendors: suitable for cross-validation and reducing single-model bias
Tip: The more participating models you choose, the more comprehensive the conclusion usually is, but consumption also increases accordingly.
Step 2: Select the Summary Model
In the Summary Model dropdown, select one model responsible for integrating the final result.
The summary model's job is not to answer the question again, but to:
- Summarize the views of participating models
- Identify consensus and differences
- Filter duplicate content
- Provide the final recommendation or conclusion
Prefer models with:
- Strong reasoning ability
- Good long-text integration ability
- High stability
If the participating models already include a high-quality model, you can usually let one of them also serve as the summary model.
Step 3: Set the Scheduling Strategy
The current system supports the following scheduling strategies:
| Scheduling Strategy | Suitable Scenarios | Characteristics |
|---|---|---|
Speed First |
Pursuing faster results | More suitable for daily efficiency-oriented Q&A |
Price First |
Wanting to control consumption | More suitable for batch use or cost-sensitive scenarios |
Success Rate First |
Pursuing stable completion | More suitable for important tasks, formal output, and complex questions |
How to choose:
- Daily office work and general consultation: use
Speed First - High-frequency use and team cost control: use
Price First - Formal proposals, important analysis, and customer delivery: use
Success Rate First
Step 4: Configure Web Search
You can configure the Web Search switch in the collaboration panel.
Recommended to enable for:
- Latest information
- Public information references
- Supplementing time-sensitive content
Recommended to disable for:
- Pure internal knowledge organization
- Fixed copy generation
- Tasks more sensitive to cost and speed
If web search is also enabled on the chat page itself, it usually participates in the overall collaboration process as well.
Step 5: Send the Question and View the Results
After collaboration is enabled, enter your question in the chat input box and send it. The system automatically performs the following process:
- Parallel answering: multiple participating models start answering at the same time
- Streaming display: each model's output is displayed in real time as an independent message card
- Automatic aggregation: after participating models finish, the summary model outputs the comprehensive result
You will usually see two types of messages:
- Original answers from each participating model
- One final collaboration summary message
4. Recommended Question Style
To make multi-model collaboration truly valuable, we recommend asking questions as clearly as possible:
1. Clear Goal
Do not simply ask "What should I do?" Instead, try to clearly state:
- What your goal is
- What the background is
- Who the result is for
- What output format you want
Example:
Please analyze the plan for adding multi-model collaboration capability to an existing system from the perspectives of product, technical implementation, and cost control, and provide a recommended solution with risk notes.
2. Specify Comparison Dimensions
If you want model outputs to be more comparable, you can directly specify dimensions in the question, such as:
- Accuracy
- Cost
- Implementation complexity
- User experience
- Delivery timeline
3. Specify Output Format
For example, require:
- Table
- Bullet-point summary
- Risk list
- Execution steps
- Final recommendation
This makes it easier for the summary model to produce structured results.
5. Which Scenarios Are Best Suited?
| Scenario | Recommendation Level | Reason |
|---|---|---|
| Technical solution design | โ Strongly recommended | Suitable for multi-perspective analysis of architecture, implementation path, risks, and cost |
| Business decision comparison | โ Strongly recommended | Suitable for comparing pros and cons of multiple options and forming comprehensive recommendations |
| High-value copywriting or proposals | โ Recommended | Multiple models can provide different expression ideas, then the summary model polishes them uniformly |
| Fact-checking and professional Q&A | โ Recommended | Helps cross-validate and reduce single-model bias |
| Casual chat | โ Not recommended | Higher cost than single-model chat and not cost-effective |
| Scenarios that strongly require second-level response | โ Not recommended | Requires waiting for multiple models and summary flow, so the response is slower |
6. How to Combine Models More Effectively?
Combination 1: Stable
- Participating models: 2 to 3
- Summary model: 1 strong reasoning model
- Scheduling strategy:
Success Rate First - Suitable for: formal replies, customer proposals, important reports
Combination 2: Efficient
- Participating models: 2
- Summary model: 1 general-purpose model
- Scheduling strategy:
Speed First - Suitable for: daily office work, quick comparison, lightweight analysis
Combination 3: Cost-Controlled
- Participating models: 2
- Summary model: 1 economical or general-purpose model
- Scheduling strategy:
Price First - Suitable for: high-frequency use, budget-sensitive teams
Combination 4: Deep Research
- Participating models: 4 to 5 models from different vendors
- Summary model: 1 strong integration model
- Scheduling strategy:
Success Rate First - Web search: recommended to enable
- Suitable for: industry research, competitor analysis, complex decisions
7. Relationship with Other Chat Capabilities
Multi-model collaboration is not an isolated feature. It can work with other capabilities on the chat page:
| Capability | Can Be Combined | Description |
|---|---|---|
Web Search |
Yes | Suitable for questions that need time-sensitive information |
Deep Thinking |
Yes | Suitable for more complex and rigorous reasoning tasks |
Role |
Can be combined based on page capabilities | Makes answers better fit a position or tone requirement |
Preset Templates |
Can be combined based on page capabilities | Suitable for fixed-format tasks such as summarization, analysis, and writing |
Recommended order:
- Clarify the task goal first
- Select participating models and a summary model
- Enable web search or deep thinking as needed
- Send the question last
8. How Is Consumption Calculated?
This is the most important point to understand before using multi-model collaboration.
Multi-model collaboration is not "one request, multiple answers"; it is the accumulation of multiple model calls:
- Each participating model is billed independently
- The summary model is also billed independently
- Enabling web search, deep thinking, or longer output may further increase total consumption
Example:
- Select
3 participating models - Select
1 summary model
Then one question is actually equivalent to at least 4 model calls.
Therefore, we recommend:
- Use single-model mode first for simple questions
- Enable collaboration for important questions
- Reduce the number of participating models when controlling cost
- Before batch use, test the best configuration with a small number of samples