The popular benchmarks don't tell you how AI does real work. They're gamed for themselves. So we gave 16 models five real jobs to do. Same tools, same prompts. We graded how each one did.
Why this is different
Salespeople researching their prospects. Marketers reading what customers are actually saying. A CX team mining product reviews. An engineer writing the Stripe webhook they keep meaning to ship. Real jobs people do every day.
We gave the models Google search, the Crunchbase API, Google Shopping reviews, and a real site scraper. These are the same tools our customers use every day across different verticals.
Each task gets graded on a rubric. The rubric checks the output for accuracy and for the style it's returning in. Pass or fail, explicitly. We also look at how much each run cost and how long it took.
The five jobs
We ran a Cotera agent for each task. Same tools, same prompt. Everything besides the actual LLM was kept consistent across runs.
Find Hightouch on Crunchbase and return their total funding, last round type, and a one-line description.
See full agent breakdownRead a real r/sales thread on enrichment tools and rank the top 5 by how many people recommended them.
See full agent breakdownPull Google Shopping reviews for AirPods Pro 2 (USB-C) and return the top 3 recurring complaints, with verbatim quotes.
See full agent breakdownRead Stripe's official docs and write a real, working webhook-verification function in TypeScript.
See full agent breakdownScrape Apollo.io's pricing page and return every tier (Free, Basic, Professional, Organization) with name, price, and top 3 features.
See full agent breakdownThe scoreboard
Sorted by pass count, ties broken by total dollars spent. Hover any ✗ to see why it failed.
| Rank | Model | Pass | Total | Sales | Marketing | CX | Coding | Scrape |
|---|---|---|---|---|---|---|---|---|
| 🥇1 |  Mistral Large 3 | 5/5 | $0.267 | ✓ $0.058 | ✓ $0.048 | ✓ $0.066 | ✓ $0.071 | ✓ $0.025 |
| 🥈2 |  GPT-5 Mini | 5/5 | $0.376 | ✓ $0.146 | ✓ $0.056 | ✓ $0.108 | ✓ $0.037 | ✓ $0.029 |
| 🥉3 |  Gemini 3 Flash | 5/5 | $0.758 | ✓ $0.032 | ✓ $0.217 | ✓ $0.339 | ✓ $0.092 | ✓ $0.077 |
| 4 | GPT-5.5 | 5/5 | $1.21 | ✓ $0.104 | ✓ $0.435 | ✓ $0.348 | ✓ $0.266 | ✓ $0.053 |
| 5 | Gemini 3.5 Flash | 5/5 | $1.41 | ✓ $0.148 | ✓ $0.118 | ✓ $0.623 | ✓ $0.486 | ✓ $0.035 |
| 6 | Gemini 3.1 Pro | 5/5 | $2.73 | ✓ $0.086 | ✓ $0.665 | ✓ $1.48 | ✓ $0.388 | ✓ $0.107 |
| 7 |  Claude Sonnet 4.6 | 5/5 | $3.83 | ✓ $0.100 | ✓ $1.82 | ✓ $0.723 | ✓ $1.11 | ✓ $0.084 |
| 8 | Claude Opus 4.8 | 5/5 | $4.58 | ✓ $1.22 | ✓ $1.74 | ✓ $0.998 | ✓ $0.439 | ✓ $0.185 |
| 9 | Claude Haiku 4.5 | 4/5 | $0.941 | ✗ $0.007 | ✓ $0.125 | ✓ $0.119 | ✓ $0.632 | ✓ $0.058 |
| 10 |  DeepSeek V3.1 | 3/5 | $0.113 | ✗ $0.001 | ✓ $0.045 | ✓ $0.053 | ✗ $0.001 | ✓ $0.014 |
| 11 | MMiniMax M3 | 3/5 | $1.10 | ✓ $0.254 | ✓ $0.200 | ✗ $0.092 | ✗ $0.508 | ✓ $0.044 |
| 12 | GGLM 5.1 | 3/5 | $1.19 | ✓ $0.048 | ✗ $0.000 | ✓ $0.420 | ✗ $0.667 | ✓ $0.052 |
| 13 | KKimi K2.6 | 3/5 | $1.27 | ✓ $0.104 | ✗ $0.423 | ✓ $0.333 | ✗ $0.268 | ✓ $0.144 |
| 14 |  Llama 4 Maverick | 2/5 | $0.004 | ✗ $0.001 | ✗ $0.001 | ✗ $0.001 | ✓ $0.001 | ✓ $0.001 |
| 15 |  Grok 4.1 Fast | 1/5 | $0.089 | ✓ $0.035 | ✗ $0.052 | ✗ $0.000 | ✗ $0.000 | ✗ $0.002 |
| 16 |  Qwen 3.6 Plus | 0/5 | $2.98 | ✗ $1.91 | ✗ $0.399 | ✗ $0.229 | ✗ $0.251 | ✗ $0.188 |
Dollars = what Cotera bills per agent run (~$0.005 per credit). Lower is better. A ✓ at $0.00 means the model emitted an answer from training memory without calling tools — we treat that as a pass on the rubric, but flag it as a meaningful behavior in the failure breakdown.
Where they broke
Every benchmark suite advertises the wins. We're showing the failures. Here's where each model usually breaks down. The pattern, not the one-off.
Llama 4 MaverickThis model doesn't use the tools, but instead looks at the training data (which is super old). This makes it very confident, and very wrong.
This model starts writing the answer, but gets stuck repeating the same fragment over and over (until the run has to be killed). It looks busy, but it never actually finishes.
This model writes a confident explanation of what the function should do, but then forgets to actually write the function (so the code field comes back empty). It looks correct, until you try to run it.
This model keeps thinking and figuring out the code in a private scratchpad, but it never actually responds with anything real (you can see it working, you just don't get any output).
This model doesn't have reliable structured output, so the answer comes back in the wrong shape (which the agent can't read). It tried to answer, but the format is broken.
Qwen 3.6 PlusThis model uses a ton of tools and burns a ton of compute, but it never actually returns a usable answer (which makes it the most expensive way to do nothing in the matrix).
Claude Haiku 4.5This model tells you it's going to do the work, but then just doesn't (the whole response is a polite sentence of intent, with no tools actually called).
How we ran it
For each task we built a Cotera agent — a system prompt, a tool list, and a default model. To benchmark, we kept the agent and overrode the model via context.model on the chat endpoint. Tools and prompts stay identical.
Every task ends with "return ONLY a JSON object conforming to this schema." Rubrics parse the final message, validate field types, ranges, array lengths, and required values. No regex on free-form text.
We don't estimate. Cotera tracks per-run cost with an LLM/tool-call breakdown in chatStatus.totals — the dollar figures here are the same per-run charges customers see in their dashboard (1 credit ≈ $0.005).
Every ✗ is tagged: wrong value, wrong format, truncated output, never-used-tools, infra error. The full per-cell breakdown — including the actual final messages — lives in REPORT.md alongside the run.
Audit it. Add your own benchmark. Re-run the matrix with models we missed.
Cotera agents work with the same tools, the same data, and any of the 16 models on this page. Start with a free org, swap models with a config change, and only pay for what your agents actually do.