Ask themin parallel.Fuseto the top.
Beam fans one prompt across N frontier models, then synthesizes the strongest answer from all responses. Consensus = confidence. Disagreement = dig deeper.
Refined in two years of real-world usage
v1
Apr 2024
v2
Oct 2025, Live
v3
Q3 2026
One model is a guess.
N models is smarter.
Maybe right.
Maybe hallucinating.
You ship the answer, hope for the best. No second opinion. No way to spot drift, bias, or fabrication until production.
reliability
GPT
?
confidence: WHO KNOWS
Consensus visible
at a glance.
Same prompt, multiple frontier models, in parallel. Where they agree, ship it. Where they diverge, dig deeper. You decide.
reliability
GPT
96%
CLAUDE
92%
GEMINI
88%
merged → confidence: HIGH
No model wins everything
Every model has a superpower.
Pick one model and you inherit its blind spots. Beam fields the whole roster, so the best model for coding, the best for images, and the best for research all answer the same prompt.
Start assembling the team. Examples:
GEMINI
Vision & images
Best-in-class visual understanding and image analysis, with broad, balanced world knowledge.
GPT
Deep research
Scholarly depth and comprehensive, thorough analysis when the question runs deep.
CLAUDE
Code & reasoning
Superior coding, logical precision, and clean technical writing.
“All models have their own winning specialties, but with Big-AGI you have them all. You don’t have to choose.”
Fred L. · Parent & Maker
One prompt, tuned N ways
Same question.
Different everything else.
A beam is not just a second model. Change the mind, the expert, or the settings on each one. Then select any reply, branch a fresh chat from it, or merge them all.
change models
Same question,
different neurons.
Send it to GPT, Claude, and Gemini at once. Or run the same model five times for five independent draws. Each beam is its own thread of thought.
change personas
Same question,
different expert.
Put a persona in each chair: a senior engineer, a skeptical scientist, a sharp editor. Same question, a different expert answering. Personas keep their own memory across sessions.
change parametersUpcoming
Same question,
different settings.
Tune each beam on its own: thinking budget, reasoning effort, web search on or off, context window. One click for Max mode (every model at its ceiling) or Search mode (live web on every beam).
01
Ask
N models, one prompt, simultaneously.
Pick from any frontier model: GPT, Claude, Gemini, Grok, DeepSeek, your local GLM or Gemma. Hit Ctrl + Enter and the prompt fans out to all of them at once.
02
Check
Agreement = confidence, divergence = signal.
They answer blind: different companies, different training data, no model sees the others. When they converge on the same answer, that consensus is hard to fake. When they diverge, the disagreement is a flag: a place to dig deeper.
03
Merge
Fuse the strongest answer.
Merge then runs a meta-pass: every response is fed to a model of your choice, which fuses them into one final answer that combines the best of each. A menu of merge programs ships today, and you stay in the driver’s seat.
The merge programs
Beam wide, then fuse down. Pick how the synthesis runs, per prompt.
Fuse
Auto-selects the strongest parts of every answer and writes the one you keep.
Guided
A checklist of the distinct ideas found across the answers. Tick what matters, Beam merges along your picks. Principal component analysis, applied to language.
Compare
A structured table of where the answers agree, differ, and win. Built for decisions.
Custom
Your own merge prompt, with editable separators. The meta-pass is fully yours.
Upcoming
Council
Coming to Beam, inspired by Andrej Karpathy’s LLM Council: every model reads and ranks the others’ answers, the scores are tallied, and the highest-rated response wins. An LLM council that grades itself, with no single judge deciding. Also known as an LM Council, a council of models, or LLM-as-a-judge applied across vendors.
In development · best-of-N by consensus, not a single grader · tell us if you want it
Iterate to the answer
The best answer is
a few passes in.
Iterate to perfection. Edit earlier messages, fold the strongest lines from each reply back into your query, and re-run the ensemble. Every pass lands tighter, and your history keeps every version to branch from.
1
edit any message
Rewind to any earlier turn, edit it, and re-run from there.
2
harvest across beams
Feed the strongest lines from each reply back into a sharper prompt.
3
beam again
Each pass lands tighter than the last -> the answer LOCKS IN.
Voices · verbatim
What people do
with Beam.
I.
Especially with beam, when there's consensus across gpt, Claude, Gemini it means gold. When there's discrepancies, that's where it indicates deeper dive with more details needed
Parent & Maker · Award-winning result, 50 teams
II.
Your work is literally saving lives per my use
ER Physician · 1yr+ daily
III.
Big-AGI feels like it has the most carefully thought through design. And it's open source!
AI Researcher · GitHub Sponsor
Straight answers
Should a third model do the merge? Does it favor its own answer?
Sometimes, but less than you would expect. In practice, models tend to agree with each other on scoring more often than they favor themselves, and the merged answer usually beats any single reply. Using a third, independent model as the merger is a solid default: it is not a guarantee against bias, it is simply a second (and third) opinion instead of one. You can always inspect every response Beam collected and override the merge yourself.
Asked by a user · answered by the founder
Why different model families instead of one model sampled N times?
Sampling one model five times gives you five draws from the same distribution, with the same blind spots. Beaming across families (GPT, Claude, Gemini, and friends) diversifies the failure modes, so a hallucination rarely survives the vote. The research points the same way: ensembles of agents outperform single models, and Beam pushes the idea further by ensembling across vendors instead of copies. The wisdom of crowds, applied to frontier models.
Grounded in the ensemble literature, battle-tested in Beam since 2024
Is running N models N times the cost?
Roughly, yes, and on the prompts that matter it earns its keep: a few extra cents to catch a wrong answer before it ships is cheap insurance. You also stay in control. Run two beams instead of six, use fast or local models for the wide pass and a frontier model only for the merge, or reserve Beam for the high-stakes questions and single-model chat for everything else. Beam runs on your own API keys, so you pay providers directly at cost, with no markup from us.
On cost and control
Stop trusting one model.
Start running them all.
Beam ships inside Big-AGI: open source, BYO API keys, zero lock-in. Ensemble reasoning for engineers, researchers, and operators who can't afford to ship a hallucination.
Native support for every major model, days after release
Your keys, your data, your conversations
Personas that remember across sessions
The original parallel multi-model interface
Sign in · BYO API keys · Zero lock-in
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