Mission-criticalRequest access
Konic Tres-1
Konic Tres-1 is the mission-critical enterprise family — the highest-capability tier, for regulated and high-stakes enterprise AI workloads where accuracy carries real cost and the model must run on on-prem infrastructure inside your own boundary.
Why Tres-1
01
Accuracy where it carries cost.
Risk, compliance, and operations decisions where a wrong answer is expensive. Tres-1 holds the highest accuracy bar in the Konic enterprise families.
02
Regulated-boundary deployment.
Banking, insurance, healthcare, and manufacturing buyers need jurisdiction and data control, not just price. Tres-1 deploys on on-prem infrastructure you own — including air-gapped environments.
03
Auditable lifecycle.
Versioned releases, evaluation libraries per vertical, and support through integration and upgrades.
Deployment
Runs on infrastructure you own — versioned, auditable, and inside your compliance boundary.
On-prem. Air-gapped capable.
Maximum-control on-prem deployment for regulated and sovereign enterprise workloads.
Managed inference option.
For teams that want Konic to run serving, Tres-1 is also available as managed inference.
Performance
Konic Tres-1 benchmark comparison
Benchmark scores across coding, reasoning, and agentic evaluations. Published numbers ship with the evaluation harness so every result is reproducible.
Coding
| Benchmark | Konic Tres-1 | Claude Opus 4.8 | DeepSeek V4 Pro (0813) | Kimi K3 | Qwen3.8-Max | Qwen3.8-27B | GLM-5.2 | MiniMax M3 |
|---|---|---|---|---|---|---|---|---|
| DeepSWE 1.1 | 58.7 | 59.0 | 62.8 | 67.3 | 56.6 | 42.2 | 46.2 | 20.0 |
| SWE-bench Pro | 62.5 | 69.2 | 55.4 | — | 67.7 | 61.7 | 62.1 | 59.0 |
| SWE-bench Multilingual | 81.0 | 84.4 | 76.2 | — | — | 73.8 | — | — |
| LiveCodeBench v6 | 91.9 | 87.8 | 87.5 | 87.2 | 87.8 | 90.3 | 69.5 | 82.2 |
Reasoning
| Benchmark | Konic Tres-1 | Claude Opus 4.8 | DeepSeek V4 Pro (0813) | Kimi K3 | Qwen3.8-Max | Qwen3.8-27B | GLM-5.2 | MiniMax M3 |
|---|---|---|---|---|---|---|---|---|
| GPQA Diamond | 91.7 | 93.6 | 90.1 | 93.5 | 92.6 | 89.2 | 91.2 | 93.0 |
| HLE (no tools) | 35.9 | 49.8 | 37.7 | 43.5 | 43.6 | 30.8 | 40.5 | 37.0 |
| IFBench | 81.3 | 62.2 | 76.5 | — | 82.8 | 79.5 | 73.3 | 82.9 |
Agentic
| Benchmark | Konic Tres-1 | Claude Opus 4.8 | DeepSeek V4 Pro (0813) | Kimi K3 | Qwen3.8-Max | Qwen3.8-27B | GLM-5.2 | MiniMax M3 |
|---|---|---|---|---|---|---|---|---|
| Agents' Last Exam — Pass rate | 24.3 | ~25.7 to 27.0 | 16.5 | 28.3 | 27.0 | 20.4 | 20.4 | — |
| Agents' Last Exam — Score | 51.2 | ~44 to 45 | — | 51.6 | 52.4 | 42.9 | 41.1 | — |
| Toolathlon Verified | 73.5 | ~59.9* | 51.8 | 76.5 | 72.5 | 67.1 | 59.9 | — |
| CharXiv (RQ) — With CI | 90.6 | 89.9* | — | — | 93.5 | 90.2 | — | — |
We show a suite when both Konic and the compared model have a published score. Open-model scores use each vendor’s official benchmark harness — we exclude results from a vendor’s own private harness and keep the lower figure when published reports conflict. Where a vendor does not publish a figure, independent evaluations and the benchmark owner’s leaderboard are used. DeepSeek V4 Pro figures are from its April 2026 preview build, except DeepSWE 1.1 and LiveCodeBench, which use current public runs. MiniMax M3’s GPQA and HLE figures are as published by Zhipu; Qwen3.8-Max combines vendor and independent results and ships under a custom licence; Qwen3.8-27B figures are from its Apache-2.0 model card. A dash means we found no comparable published number, and * marks approximate or interpolated figures.
Tres-1 FAQ
Common questions.
Konic Tres-1 is the mission-critical enterprise family — the highest-capability tier, for regulated and high-stakes enterprise AI workloads where accuracy carries real cost and the model must run on on-prem infrastructure inside your own boundary.
Mission-critical tier under an annual enterprise licence, no per-token cost. Commercial terms are scoped per engagement — request access for details.
Yes. Konic enterprise families are engineered for on-prem deployment on infrastructure you control — on-premise, private cloud VPC, edge, or air-gapped environments. Versioned releases keep your integration stable across upgrades.
Choose one enterprise AI flow already running in production, deploy the family inside your environment, and A/B test it against the model you use today — or against your success criteria if there is no incumbent. Decide on measured results, not claims.