Built for engineers
who change tools mid-task
You already know no single model wins every job. DeepSeek burns through refactors and unit tests. Claude reads a 200k-line monorepo and writes the migration plan. ChatGPT polishes the README before you push. Namulai gives you all eight behind one chat, one history, one 19.80 EUR bill, so the question stops being which subscription am I paying for and becomes which model fits this commit.
One thread, eight specialists
Open a chat, drop in your stack trace, route it to DeepSeek for the fix. Same thread, switch to Claude, ask it to review the patch against the rest of the file. Switch again to ChatGPT to draft the changelog entry. The conversation history stays intact across model switches, so context never resets.
No more copy-pasting between tabs of ChatGPT, Claude.ai and the DeepSeek chat. The three of them sit behind the same model picker.
DeepSeek for code, Claude for review
DeepSeek V3 is roughly on par with GPT-4-class models on HumanEval and SWE-bench, at a fraction of the inference cost. It is the daily driver for hot loops, regex, type gymnastics, SQL, and stubborn TypeScript inference errors.
When the patch is large enough to risk regressions, Claude Sonnet takes over. Its long context lets you paste the full diff plus surrounding files and ask for an architectural review, not just a syntax check. Two models, two passes, one chat.
Documentation that does not sound generated
ChatGPT writes the kind of prose your README needs: tight, declarative, no filler. Feed it the function signatures and a one-line summary of intent, get a draft you can ship after light editing.
For design docs and ADRs, Claude tends to produce clearer reasoning chains, especially when you ask it to argue against its own recommendation. Pick the writer that matches the document type rather than forcing one model to do both badly.
Different models, different blind spots
Stuck on a bug for an hour means your mental model is wrong. Asking the same model twice will not fix that. Asking ChatGPT, then Claude, then Gemini the same question almost always surfaces the angle you missed, because each was trained on a different mix of corpora and reinforced with different feedback.
The Namulai chat makes this cheap: re-route the prompt, no new tab, no new key.
One bill, no per-token surprises
Three separate Pro subs (ChatGPT 20 USD, Claude 20 USD, Perplexity 20 USD) is roughly 60 USD per month and three credit-card lines. Namulai is 19.80 EUR flat, with a 30-day trial that costs 1 EUR upfront and is cancellable anytime from the customer portal.
Usage is rate-limited by daily message count, not by tokens, so a long Claude session will not eat a metered budget mid-refactor.
Common questions from engineers
Can I use Namulai inside my IDE or terminal?
Not yet. Namulai is a web chat at namulai.com. An API and editor plugin are on the roadmap but not the priority. For most engineers the browser tab next to the editor covers 90 percent of the use case.
Are my prompts used to train the models?
No. Namulai routes through OpenRouter, which contractually forbids the upstream providers from training on routed traffic. Your code does not enter any future model weights.
Which model handles the longest context?
Gemini 1.5 Pro at around 2M tokens is the leader, followed by Claude Sonnet at 200k. For a full monorepo dump or a multi-hour log, Gemini is the right call. For deep reasoning over 50k to 100k tokens, Claude is sharper.
Can I share a chat with a teammate?
Conversation export is on the roadmap. Today the chat is single-user. Most teams paste the relevant excerpt into Slack or a PR description, which keeps the AI conversation out of the permanent record on purpose.
Try the eight models on your next branch
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