One model says LGTM. Three models find the race condition.
You already know the failure modes: plausible code that is subtly wrong, confidently hallucinated APIs, library advice from two versions ago. Deepest sends your question to several top models at once and shows you exactly where they disagree. Keep Copilot for autocomplete; bring the decisions and the gnarly bugs here.
200 free credits. No credit card required.
Watch three models read the same job queue
One check-then-set status flag, three top models, three different verdicts. The split is the review.
Simulated demo for illustration. Actual responses are more detailed and personalized.
Why one AI is not enough for engineering work
A single chatbot gives you one fluent answer with no way to tell the solid from the invented. Engineers named this failure mode before anyone else did.
Plausible code is not correct code
A model's worst output is not the code that fails to run; it is the code that runs, reads clean, and is wrong in a way you find in production. Hallucinated methods, deprecated flags, concurrency assumptions that hold right up until they do not. One fluent answer gives you no signal about which kind you got. Three independent answers do: the invented API is the one the other two models never mention.
Every model has a cutoff, and none of them acts like it
Ask one model about a fast-moving library and you get last year's API presented with today's confidence. Different models have different cutoffs and different training data, so cross-checking is the cheapest way to catch stale advice: when one model insists on an option the others say was removed two majors ago, you know exactly what to verify against the changelog.
A single model agrees with your framing
Bring an architecture decision to one chatbot and it will mostly polish the option you already prefer. That is not review, it is confirmation. Models that answer independently and then argue surface what a sycophantic single answer buries: the operational cost, the failure mode at scale, the boring alternative that is probably right.
How engineers use Deepest
Three ways to combine the models, mapped to how engineering work actually gets checked.
Compare · where the models agree and disagree
Compare: the reviewer that does not share your blind spots
Ask once and every model answers side by side, with an automatic breakdown of where they agree and where they split. Paste a diff and each model catches different bug classes: one reads the concurrency, one the error paths, one the API misuse. Agreement is not proof of correctness, but disagreement is a precise list of what to check before you merge.
GPT-5.6
Claude
Calls axios.retryConfig, which does not exist
Gemini
Compare
Two of three agree on the fix. The third invented an API.
Debate · models argue and converge
Debate: red-team the ADR before you commit to it
Debate makes the models argue against each other and converge over rounds. Feed it an architecture decision (queue choice, service split, build vs buy) and watch your preferred option take fire from positions you had not considered. The objections that survive are the ones your staff engineers would raise; the option left standing is the one worth writing up.
GPT-5.6 · opening
Claude · rebuttal
GPT-5.6 · concedes
Claude · holds
Debate
Convergence after two rounds: Postgres, with the tradeoffs on record.
Synthesis · one combined best answer
Synthesis: one answer built from the strongest parts
For a migration plan, an incident writeup, or a design doc first draft, you do not want three versions to reconcile; you want the best one. Synthesis has the models answer independently, then merges the strongest reasoning and cleanest structure into a single draft, so the doc arrives already cross-checked instead of reflecting one model's habits.
GPT-5.6
Claude
Gemini
Synthesis
One plan, assembled from the strongest sections of each answer.
What engineering work looks like with a model panel
Four places engineers put Deepest to work on day one. Each prompt is copyable as written.
Code review second opinions
Paste a diff and get independent reviews from several models. Each one catches different bug classes, and the disagreement report tells you which findings are solid and which are one model's imagination.
Try this prompt
Review this diff as a senior engineer who did not write it. List bugs, race conditions, and unhandled edge cases in order of severity, and say which ones you would block the merge on: [paste diff]
Architecture decision stress-tests
Bring the ADR before you commit, not after. Disagreeing models surface the operational costs, failure modes, and boring alternatives that a single agreeable model buries under your own framing.
Try this prompt
We are choosing between a Redis-backed queue and Postgres SELECT FOR UPDATE SKIP LOCKED for background jobs (about 50 jobs/sec, at-least-once delivery is fine). Argue for the option I am NOT leaning toward first, then give your real recommendation: [paste context]
Debugging when you are stuck
When you have stared at a bug for an hour, three independent hypotheses beat one. Models that have not seen each other's answers propose genuinely different causes, and the overlap tells you where to instrument first.
Try this prompt
This endpoint intermittently returns stale data, roughly 1 request in 50. Here is the handler, the cache layer, and what I have ruled out. Give me your top three hypotheses ranked by likelihood, and the cheapest experiment to falsify each: [paste code and notes]
Evaluating unfamiliar tech
A single model's take on a library is frozen at its training cutoff and biased toward whatever its data over-represented. Cross-checked answers catch deprecated advice and surface the sharp edges the README does not mention.
Try this prompt
We are considering Drizzle vs Prisma for a Next.js app on Postgres with heavy transactional writes. Compare migration story, transaction ergonomics, cold-start weight, and the failure modes people actually hit in production. Flag anything likely to have changed since your training data.
Start with 200 free credits and up to 2 models per prompt.
Confidentiality
Built for code you cannot post on Stack Overflow
Conversations are encrypted in transit and at rest, are never used to train models, and you can export or permanently delete everything at any time. Prompts go to model providers through enterprise APIs with no-training terms, not consumer chatbots. Follow your company's AI policy, and strip secrets and credentials before pasting (you should anyway); the workflow works exactly the same.
Questions software engineers ask
Work in a different field? Deepest works the same way for product managers, researchers, consultants, and every other profession.
Give every diff a second opinion
Start free with 200 credits. No credit card required.