Official Leaderboard1
#4 of 18
Deep Cove Research Pro
#6 of 18
Deep Cove Research
A benchmark for deep research agents. It has 100 PhD-level research tasks, written by experts in 22 fields, in Chinese and English (ICLR 2026).4
45 organizations evaluate performance based on it, including:5
Agentic Research
85%
of input tokens are rereading session history
Frontier research agents work in a loop: search, open a page, take notes, repeat. Before each new step, they read all their old notes again. So most of their effort goes into rereading, not into new sources.2
GPT-6 Astra Pro · Max Reasoning
Read-Time Compute
2%
of input tokens are rereading session history
Our state-of-the-art orchestrator handles the loop itself, so the model does not reread old notes. We spend the savings on research: 5X more searches,2 and an engine built to read thousands of sources.7
Deep Cove Research · Max Reasoning
On average, frontier research agents cost $15.36 per task. Rereading their old notes is billed at a discount, but still costs $3.76: almost 4X the entire cost of Deep Cove Research, and 1.5X that of Deep Cove Research Pro. Our engines spend their budget on reading new sources and on writing.2
Agentic Research
GPT-6 Astra Pro Deep Research
$15.36
Read-Time Compute
Deep Cove Research
$0.99
Read-Time Compute
Deep Cove Research Pro
$2.50
Agentic Research
14
Average Searches per Report
Frontier research agents have limited memory, and the loop uses most of it. So they only have room for about 14 searches per report. For many sources, they read only the short snippet, not the complete content, so details are easy to miss or misread.2
GPT-6 Astra Pro · Max Reasoning
Read-Time Compute
68
Average Searches per Report
Our state-of-the-art context management keeps the model’s memory clear. So it has room for about 68 searches per report, 5X more than frontier agents. We comprehend the whole page and turn it into evidence cards for deep comprehension.2
Deep Cove Research · Max Reasoning
The comprehension engine works from real-time, post-training sources it comprehends at runtime, not from obsolete knowledge. That makes model size a minor factor. Our two entries run models priced 5X to 40X lower than GPT-6 Astra Pro and Claude Fable 5.1, and score higher.8
Cost and Score Sources: 8
Frontier agents decide every step on their own: what to search, what to open and when to stop. These decisions come from the model’s pre-training, including its biases, so the same question can give a different report each time. Our engine runs every task through one research guardrail and builds every claim from post-training sources it has read. This reduces pre-training bias and makes reports more replicable.
Frontier agents keep all their progress in one long session with the model. If the session stops halfway, the progress is lost, and you have to run the task again from the start and pay again. Our engine splits research into stages. If a run stops, you can resume it from the last checkpoint or start a new fork from there, so you never pay for the same work twice.
You can freely scale up your deep research plan without cost pressure. Our state-of-the-art orchestrator saves the model’s context window and dices the work into many small calls, so scaling up your plan makes the cost grow linearly. Meanwhile, frontier agents pay the looping cost on every extra step, so for them the same increase makes the cost grow non-linearly.7
Our engine handles the research process, so a light model is enough, at about 31X lower hardware cost.9
Frontier-Scale Model
2.8T Parameters
16 × NVIDIA B200 / $109/hr
Model Used by Deep Cove Research
117B Parameters
1 × NVIDIA H100 / $3.50/hr
Deep Cove Intelligence is a stealth team based in North Vancouver, British Columbia. We build the comprehension engine and test it in public, on DeepResearch Bench.
Deep Cove Research is opening in stages. Request an invitation for your team. Available as a web app and as an MCP server for your own agents.
Tell us what you research, and we will reply.
Every report we submitted to DeepResearch Bench, for both of our entries, with its overall score, 4 sub-scores and cost.
Per-report scores for Deep Cove Research Pro are the official verification: the mean of the benchmark team’s 3 GPT-5.5 scoring runs. Costs use public API prices.
Per-report scores are our own evaluation with the official RACE method. Costs use public API prices.
Every DeepResearch Bench task, side by side with each rival we ran on it, judged the same way.
Compare With
Scored with the official RACE method and GPT-5.5 judge. Costs are averages per report.