Anrak Ensemble
anrak-ensembleanrak/ensembleThe same multi-model panel with every piece of legal machinery removed: no planner, no research lanes, no citation manifest, no Indian-law framing. Your messages travel to all four experts with roles preserved, so your own system prompt genuinely governs each drafter. A senior partner consolidates the drafts and the Logic Cop alone reviews the result for internal consistency. Use it for general reasoning, code, and strategy — anywhere you want ensemble quality without legal grounding.
The Panel
Four independent jurists draft the same question in parallel. They are deliberately different model families, so their errors are largely independent — the condition under which fan-out plus aggregation actually beats one strong model.
| Jurist | Lens | What it contributes |
|---|---|---|
| Athena | The structured analyst | Decomposes the question and covers every sub-issue methodically. Usually the most complete draft. |
| Vikram | The frontier reasoner | Pushes maximum depth on the hardest part of the problem. The rollout engine for deep search. |
| Themis | The meticulous verifier | Careful step-by-step chains that flag their own uncertainty rather than leaping to a conclusion. |
| Chanakya | The pragmatic strategist | Trained closest to Indian legal language and drafting convention; on the pure family, the practical-implications lens. |
Pricing
One flat charge per call, whatever the answer's length. A request to this id runs ~6 underlying model calls — that fan-out, not your token count, is what it costs us, so that is what we price on.
Estimate a month
Requests per month to anrak-ensemble.
- Per request
- ₹27.20
- Per month
- ₹8,16,000
- Per 1,000 calls
- ₹27,200
Comparing against a per-token model?
On a reference request (2,000 tokens in, 2,000 out) this price works out to an effective $30.91 / $123.64 per 1M tokens.
That number looks high next to a raw frontier model, and it should: a raw model bills you for one call, while this id bills for 6 and a verification pass. We publish the comparison rather than hide it — but we bill per request, so a long answer does not cost you more.
Verification
Logic Cop only
With no retrieval corpus there is nothing to check citations against, so a single consistency reviewer reads the answer for internal contradictions and fabricated-looking specifics. It annotates; it does not correct. Treat factual specifics as unverified.
The verdict rides on every response as anrak.verify, the drafts and their grounded scores as anrak.orchestration, and the ordered trace as anrak.reasoning. When streaming, the same narration arrives live as delta.reasoning_content.
Quickstart
Two changes from an ordinary OpenAI call: the base_url and your ANRAK- key.
from openai import OpenAI
client = OpenAI(
base_url="https://anrak.legal/api/v1",
api_key="ANRAK-xxxxx",
)
resp = client.chat.completions.create(
model="anrak-ensemble",
messages=[{"role": "user", "content": "Design a rate limiter for a multi-tenant API."}],
)
print(resp.choices[0].message.content)
# What it cost, and whether a downgrade happened:
extra = resp.model_extra or {}
print(extra["anrak"]["billing"]) # {'model': 'anrak-ensemble', 'price': '₹27.20', ...}
print(extra["anrak"]["downgraded"]) # None unless the pipeline was downgradedLimits and trade-offs
- No retrieval. Nothing here is checked against any corpus, so treat factual specifics as unverified.
- The Logic Cop reviews consistency only and annotates — it does not correct the answer the way the legal family's panel does.
- For Indian-law questions the conductor family is strictly better: it grounds and it verifies.
Best for
- • Hard general reasoning where four independent attempts beat one
- • Work under your own system prompt and output format
- • Non-legal domains — architecture, strategy, analysis, code review