63 on the Artificial Analysis Intelligence Index v4.1.
$4 in, $24 out. Zero data retention.
Change one line.
Same prompts. Same parameters. Side by side.
from openai import OpenAIclient = OpenAI(base_url="https://api.senal.ai/v1", # 1api_key=SENAL_KEY, # 2)client.chat.completions.create(model="senal-1", # 3messages=messages,)
Base URL. Key. Model name.
Streaming, function and tool calling, structured outputs, system prompts and multi-turn state all work exactly as they do today. Your harness does not know the difference.
Run it against your own evaluation set.
Point ten percent of your traffic at Senal for a week. If we do not win on your work, leaving is the same line that got you in.
| Benchmark | Senal | GPT 5.6 Sol (Max) | Fable 5 |
|---|---|---|---|
| GDPval-AA v2 | 66.0 | 62.0 | 63.0 |
| τ³-Banking | 36.9 | 33.0 | 26.8 |
| Terminal-Bench v2.1 | 91.1 | 89.5 | 84.6 |
| SciCode | 58.3 | 56.1 | 60.2 |
| HLE | 50.2 | 47.2 | 53.3 |
| GPQA-Diamond | 96.5 | 94.1 | 92.6 |
| CritPt | 33.8 | 32.3 | 28.6 |
| AA Accuracy | 62.0 | 59.0 | 61.0 |
| AA Non-Hallucination | 52.0 | 11.0 | 45.0 |
| AA LCR | 78.0 | 73.7 | 70.0 |
| AA Intelligence Index v4.1 | 63 | 59 | 60 |
No benchmark detection. No per-benchmark configuration. No tuning. Canonical public suites at pinned commits, scored by each suite's own implementation, baselines at maximum reasoning on identical parameters and task instances.
Fewer answers that need checking.
Non-hallucination measures whether a model declines instead of inventing when it lacks grounds to answer. It decides how much of your workflow can run without a person in it. When any output might be fabricated, every output gets reviewed. That is review as a bottleneck instead of a safety net.
Senal 52. Fable 5 45. GPT 5.6 Sol 11.
Capability that holds at production length.
Real prompts are far longer than test cases. Models that look equivalent on short inputs diverge as context grows.
8 points above Fable 5 on long-context reasoning.
Agents that finish the job.
τ³-Banking scores the end state of a database against an annotated goal under written policy. It measures work completed, not dialogue that sounds correct.
10.1 points more completed work than Fable 5, against a 6.2-point spread between the two baselines themselves.
No single model is trained on all of human knowledge. Every foundation model reflects the priorities and coverage decisions of the lab that built it, so the gaps are structural rather than incidental.
Senal compounds the capability of multiple independently trained frontier models rather than choosing between them. Choosing is bounded by the strongest model on the day. Compounding is not, which is why the result sits above every individual model instead of matching the best one.
The ceiling is not fixed. Performance scales with the breadth of available training distributions rather than any single model, so every capable model that ships raises it.
Identical computation on every request. No difficulty classification, no effort adaptation, no fallback tier.
No prompt. No completion. No intermediate state. Not for training, not for abuse monitoring, not for thirty days.
In regulated sectors this is not a preference, it is a procurement gate. Senal offers it as standard, at no additional cost.
If that gate is why frontier capability has not reached your most valuable workflows, it is now open.
| Senal | Fable 5 | |
|---|---|---|
| Input, per 1M tokens | $4 | $10 |
| Output, per 1M tokens | $24 | $50 |
| Added latency | Negligible | Baseline |
| Data retention | None, at any stage | 30 days, no ZDR terms |
60% below on input. 52% below on output. Roughly 55 to 60 percent lower blended.
100M input and 20M output in a month: $880 on Senal, $2,000 on Fable 5.
The rate card understates it. Production spend is dominated by retries, human review and escalation. A system that fabricates less and completes more of each task consumes fewer of all three.