JJAgent Hits 87.1% on Terminal-Bench 2.0 — Multi-Model Routing Now Leads Every Single-Lab Agent
Terminal-Bench 2.0 is no longer a model competition. It’s a scaffold competition.
As of May 15, the leaderboard at tbench.ai shows JJAgent — a third-party agent from team JJ using multiple models — at 87.1%, ranking second overall. Only vix (90.2%, also third-party, running Claude Opus 4.7) sits above it. Below JJAgent: NexAU-AHE at 84.7%, LemonHarness from Lenovo’s CTO Research Lab at 84.5%, and Capy at 83.1%. All use multiple models or GPT-5.5.
OpenAI’s own Codex CLI, the lab’s direct entrant, ranks seventh at 82.0% — behind five other agents, four of which are from independent organisations.
The Leaderboard as of May 15–16, 2026
| Rank | Agent | Model(s) | Org | Accuracy |
|---|---|---|---|---|
| 1 | vix | Claude Opus 4.7 | vix | 90.2% |
| 2 | JJAgent | Multiple | JJ | 87.1% |
| 3 | NexAU-AHE | GPT-5.5 | china-qijizhifeng | 84.7% |
| 4 | LemonHarness | Multiple | Lenovo CTO Org | 84.5% |
| 5 | Capy | GPT-5.5 | Capy | 83.1% |
| 6 | Polaris | Multiple | PolarisOps | 82.2% |
| 7 | Codex CLI | GPT-5.5 | OpenAI | 82.0% |
Four of the top six entries below vix use “Multiple” as their model designation, meaning they route adaptively across models per task. The JJAgent and LemonHarness results in particular — from a small team and a corporate research group, respectively — confirm that the technique is reproducible across organisations without access to frontier compute.
What Multi-Model Routing Gets You
The 5.1-point gap between JJAgent (87.1%) and Codex CLI (82.0%) using the same or equivalent underlying models is not explained by base model capability. It’s explained by scaffold architecture: how the agent chooses models, manages context, handles tool chains, and routes subtasks.
Terminal-Bench 2.0 measures real terminal workflow performance across categories including system administration, code compilation, server setup, data science, and cybersecurity. These tasks have heterogeneous requirements — some favour long-context reasoning, others favour fast code generation, others favour security-focused models. A scaffold that dynamically selects the right model for each subtask has a structural advantage over one locked to a single provider.
JJAgent’s 87.1% result — 5.1 points above OpenAI’s own Codex CLI — is the clearest quantification yet of what that advantage looks like at scale.
Why Labs Should Care
Model rankings at the frontier have treated Terminal-Bench as a proxy for model capability. The May 15 leaderboard challenges that assumption. A third-party JJ team without a foundation model beat OpenAI’s first-party agent on OpenAI’s own ground.
The commercial implication: scaffolding companies that nail multi-model routing — Augment Code, vix, and now JJAgent — may consistently outperform the labs’ own deployed products regardless of which underlying model is strongest. The base model still matters, but it’s no longer sufficient.
Terminal-Bench 2.0 now has 146 entries. The top 7 compress into a 8.2-point band (82.0%–90.2%). The next capability step requires either a substantially better base model or a substantially better routing strategy. Currently, routing is winning.