How to Benchmark Enterprise AI Platforms on Cost per Task Before a POC

To benchmark enterprise AI platforms before a POC, run the same 50 to 100 real tasks through each one, log every token, tool call and retry, have reviewers score the answers blind, and divide total spend by the answers that passed. Compare cost per accepted task, not price per token or price per seat.

Vendors quote prices in different units, so a rate card cannot settle a shortlist. Microsoft 365 Copilot is a per-user add-on, ChatGPT sells seats with optional credits, and Glean tracks usage in FlexCredits. A benchmark turns all three into one number you can defend to finance. I read each vendor's price page and admin documentation on October 29, 2026, and linked every source below.

Coworker is a context layer for enterprise AI: it connects 50+ work tools, builds a permission-aware company graph (OM2) and runs agents that act across them, inside Coworker or Claude, ChatGPT and Cursor via Coworker MCP. I ran its public benchmark through this same method, and I say where that benchmark stops being a fair comparison.

What should you measure when you benchmark enterprise AI platforms?

Measure four things per task: what it cost, whether the answer was accepted, how long it took, and how often the platform failed to answer at all. Glean's token-efficiency buyer's guide lists a similar set: cost per task, latency per task, output quality and groundedness, and scalability across workflows and teams. It is a vendor's guide, but the four-outcome framing is sound and I use it here.

The one that most teams skip is acceptance. Cerebras' explainer on cost per token makes the point that a cheaper token becomes expensive once retries, slow responses and wrong answers are counted, and it recommends cost per useful response as the business metric. Anthropic's guidance on building evaluations starts from the same place: define specific, measurable success criteria before you test anything.

How do you build the task set for an enterprise AI POC benchmark?

Use real work, not demo prompts. OpenAI's evaluation best practices say to make tests reflect real-world distributions and to include typical, edge and adversarial cases. For a company-context platform, that means questions your teams actually ask across the tools they actually use.

I would start with 50 to 100 tasks. Coworker's published benchmark used 100 tasks across 7 categories (business operations, engineering, general business, marketing, people, product and sales), and Glean's guide cites a benchmark of roughly 175 enterprise queries. Grow the set where two platforms land close together, because that is where a larger sample changes the answer.

Three rules keep the task set honest:

• Write the tasks before you talk to vendors, and do not let any vendor choose them.
• Mix lookups ("who owns this ticket?"), summaries, multi-source questions and actions. Include some tasks that should return no answer.
• Hold back a quarter of the tasks until the final run, so nobody tunes to the test.

How do you compare token cost when platforms bill by seat, credit and token?

Convert every bill to total spend over the test window, then divide by accepted tasks. The billing units differ, and each vendor's own dashboard is built around usage and adoption rather than cost per task. None of the vendor pages listed below describes a cost-per-task metric, so you build it from their exports and your price sheet.

For seat-priced tools, prorate the seat price over the test window. Five testers on Microsoft 365 Copilot at the $30 list price is $150 a month, so a two-week pilot is roughly $75 plus the Microsoft 365 plan it requires, unless your reseller quotes a fixed pilot price. Add any credits or API charges on top, then divide by accepted tasks.

Here is the arithmetic on two made-up platforms. It is an illustration, not a measurement.

Platform A is cheaper to run and more expensive per accepted task. Only the second number tells you which platform produced cheaper work. My guide to AI cost optimization has the full cost-per-task formula and the list of fields to log on every request.

How do you score answer quality without bias?

Score blind and compare in pairs. OpenAI's evaluation guide warns that model graders show position bias (response order) and verbosity bias, and recommends pairwise comparison or pass and fail for more reliable results. It describes randomized, blinded human review as the highest quality option, at the price of speed and cost.

A workable setup looks like this:

1. Write a reference answer for each task before any platform runs.
2. Randomize the order of the answers and strip the platform names.
3. Have two reviewers mark each answer accepted or rejected against the reference, and settle disagreements by discussion.
4. Record the reason for every rejection. Wrong source, missing source and made-up detail need different fixes.

If you use a model as a second grader, check its scores against your human reviewers on a sample first.

How should you treat tasks a platform cannot answer?

Keep them in the quality score and leave them out of the cost average. A platform that returns nothing spent few tokens, so counting that task in the cost comparison flatters the platform that failed. Coworker's benchmark did exactly this: on roughly 5% of questions the native-connector baseline could not answer at all, so those were excluded from the cost and speed comparisons and kept in the quality comparison, since failing to answer is itself a quality signal.

Report the no-answer rate as its own line. A platform that is cheap per answered task and silent on a fifth of your questions is not cheap.

What did Coworker's own enterprise AI benchmark find?

Coworker ran 100 tasks across 7 categories on a Claude harness with real connected tools, including Google Drive, Gmail, Jira, Slack, HubSpot and GitHub, and repeated each task until the result was statistically significant. The baseline was Claude with its own native connectors. The other arm was the same Claude through Coworker MCP, with OM2 narrowing the context each request carries. Cost is total input and output tokens per session. Source: Coworker benchmarks, checked October 29, 2026.

On answer quality, blind reviewers preferred the OM2 answer in 84.5% of pairwise comparisons against a human-authored reference. The 9x figure comes from context optimization alone, with no change to the model, and it holds on retrieval-heavy categories such as Jira, GitHub, Slack and CRM data, not across every task. The 51x figure requires Optimized Routing on top of OM2.

Two limits matter for your own benchmark. Coworker ran this test itself, so it is a vendor-run result. And the baseline is Claude's native connectors, not Glean, Copilot or ChatGPT Enterprise, so the table shows what the method can measure, not who wins a three-way bake-off. Glean publishes a comparable test with its own caveats: about 175 enterprise queries, where its enterprise context was preferred about 2.5x as often as off-the-shelf MCP tools, which used around 30% more tokens on average, with the model and harness held constant.

Can Coworker run this benchmark on your company's data?

Yes. Coworker offers a Context Impact Report that runs the same benchmark on your own systems and covers the token costs. The steps, from the benchmarks page:

1. You sign an NDA and name the systems to cover.
2. You name one or two testers who install Coworker's benchmarking app.
3. Coworker connects your sources and builds OM2 on your data.
4. It runs the full question set twice, once against your existing Claude setup and once through Coworker MCP.
5. It scores every answer blind and verifies the results, tunes your graph and runs again until the number settles. The last run is your number.

Be clear about what this tests. It compares Coworker MCP against the Claude setup you already have, so it answers "would a context layer cut our token cost" and does not rank Coworker against the three seat-priced assistants. Coworker also does not run inside Word or on your Microsoft tenant, so if your shortlist is Microsoft 365 Copilot versus ChatGPT Enterprise versus Glean, run the method above yourself and add Coworker as a fourth arm only if context cost is a real concern. Coworker MCP access is through a sales call: book a demo.

What mistakes make an AI platform benchmark misleading?

• Comparing price per seat with price per token. Convert both to cost per accepted task first.
• Dropping retries and failed runs. You paid for them, so they stay in the numerator.
• Letting the vendor pick the tasks. Vendors choose the ones their platform handles best.
• Running each task once. Repeat runs until the result settles, because agent runs vary.
• Benchmarking models instead of platforms. Public leaderboards such as Artificial Analysis score models. My AI token cost comparison uses them for exactly that, but a platform's cost also depends on how it retrieves and routes context.
• Ignoring permissions. A platform that returns an answer from a document the tester cannot open has failed the task, whatever the reviewer thinks of the answer.

Frequently asked questions

How do you benchmark token cost and answer quality of enterprise AI platforms before a POC?

Run the same 50 to 100 real tasks through each platform, log tokens, tool calls and retries, score the answers blind against reference answers, and divide total spend by accepted answers. Keep tasks a platform could not answer in the quality score and out of the cost average.

What is cost per accepted task?

Cost per accepted task is total spend on a platform over a test window divided by the number of tasks whose answers passed blind review. It folds retries, failures and seat or credit charges into one number, which makes seat-priced and token-priced platforms comparable.

How many test tasks do you need?

I would start with 50 to 100 and add more where two platforms are close. Coworker's benchmark used 100 tasks across 7 categories, and Glean's published test used roughly 175 queries. Repeat each task until the result settles.

How do you compare seat pricing with token pricing in a benchmark?

Prorate the seat price over the test window, add any credit or API charges, and divide by accepted tasks. Five testers on Microsoft 365 Copilot at the $30 per user per month list price is $150 a month before the required Microsoft 365 plan.

Does a model leaderboard tell you which enterprise AI platform is cheapest?

No. Leaderboards such as Artificial Analysis score models on fixed tasks. A platform's cost per task also depends on how much company context it sends to the model and how it routes work between models.

Can Coworker run a cost and quality benchmark on my data?

Yes. The Context Impact Report runs Coworker's benchmark on your own systems, covers the token costs and compares Coworker MCP against your current Claude setup, scored blind and verified by a Coworker engineer. Book a demo to start.

Related reading

• Coworker benchmarks: the 100-task method and results
• AI cost optimization: where the money goes and 9 levers that cut it
• AI token cost: 13 models compared by cost per task
• Enterprise AI pricing compared
• Enterprise AI price index, Q3 2026
• Glean pricing
• ChatGPT Enterprise pricing
• Microsoft 365 Copilot enterprise pricing
• Model routing
• LLM cost calculator
• What is a context layer?
• Coworker MCP