# We benchmarked 18 models on our sales agent. 16 tied with the one we pay a premium for.

*A field report from Salesteq — the team that runs Sara, a bilingual (English + Arabic)
car-sales agent, live for dealerships in Saudi Arabia.*

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We pay real money, every day, for GPT-5.2 to power our production sales agent. So we did
the uncomfortable experiment: we swapped in 18 other models — the same agent, the same
prompt, the same tools — and scored every one of them on the actual job.

**Sixteen of them landed in a statistical dead heat with GPT-5.2.** One of them costs
one-sixtieth as much.

Not because GPT-5.2 is bad. Because the field has quietly gotten *very* good — good enough
that, for a well-built agent, the model you pick is no longer a quality decision. It's a
cost decision. And almost nobody is measuring that on anything that looks like real work.

## The gap nobody's testing

If you're about to put a model inside an agent, the leaderboards don't help you. MMLU tells
you a model can pass exams. BFCL and τ-bench test tool-calling — but in English, on
synthetic academic or single-industry tasks. None of them tell you what happens when a real
customer opens your chat widget, in Arabic, on a Friday, and asks whether the BMW X5 is in
stock and can they book a test drive.

That last sentence *is* our production system. So instead of trusting a proxy, we benchmarked
the models on the real thing.

## What we actually did

**Sara** is our customer-facing agent. Under the hood she runs a "dual-layer" prompt
assembler — a static, cache-friendly prefix plus one dynamic block — and a real tool set
(inventory search, lead capture, OTP, booking, human handoff). We took **62 hand-built
car-sales scenarios** — 49 English, 13 Arabic — drawn from the kinds of conversations Sara
handles in production, and ran **18 models** through the exact same agent.

Scoring is deliberately boring:

- A model **passes** a scenario when an independent judge (**Claude-Haiku** — a different
  vendor from every model on the board) rates the response *complete enough*, **and** the
  agent actually **calls the right tools**. Talking about booking a test drive doesn't
  count; calling the booking tool does.
- **Cost** is computed from real token usage × live OpenRouter pricing, so "quality per
  dollar" is a real number, not a vibe.

Same agent, same prompt, same tools. Only the model changes.

## What we found

**Quality is a statistical tie.** Run the numbers properly — a two-proportion z-test, not
eyeballed ranks — and **16 of the 18 models fall into one statistical tier**, GPT-5.2 among
them. DeepSeek-V4-Flash and Qwen3.5-122B lead by point estimate (90.3%), but at 62 scenarios
you can't say they're *significantly* better than our GPT-5.2 — or than an 8B open model. The
raw ranking is inside the noise. **The tie is the finding.** And remember: the prompt was tuned
*for* GPT-5.2, so it walked in with every advantage and still couldn't pull clear.

**So cost is the whole decision.** gpt-oss-20B — OpenAI's own open-weight model, small enough
to self-host — sits in that same top tier at roughly **1/60th the cost per conversation**. Not
60% cheaper. Sixty *times*. When quality ties, you're only choosing what to pay.

**Arabic separates the field like nothing else.** Several models handle Arabic sales
conversations as well as English (DeepSeek, Qwen3.5-35B, and our own gpt-5.6-luna all hit
100% on the Arabic set). Others collapse — Llama 3.3 70B dropped to 54% in Arabic while
scoring 78% in English. If you serve Arabic-speaking customers, the "best model" is a
different model. Almost no public leaderboard will ever tell you that.

**Security mostly held — and that's about our agent as much as the models.** On a
90-scenario adversarial probe (jailbreak, PII-exfiltration, prompt-injection), most models
resisted *every* attack; only the smallest open models leaked a little. When 15 different
models all refuse the same attacks, that's the security *layer* doing its job, not luck.

**And we ran one model off the cloud entirely.** Qwen3.5-4B on a MacBook (Apple M5) — no
API, no bill — scored **77.4%** with balanced English and Arabic. A capable sales agent, a
4-billion-parameter model, on a laptop. That's the self-hosting future in one data point.

## What it means if you're building an agent

- **You probably don't need a frontier model.** For a well-scoped agent with a good prompt,
  a self-hostable open model can match a frontier closed one — and the cost difference is
  large enough to change your business model, not just your bill.
- **Benchmark on your own task.** A model's MMLU score won't predict whether it calls your
  booking tool correctly. Ours didn't.
- **If you're bilingual, test bilingual.** The English ranking and the Arabic ranking are
  not the same ranking.

## Scope, and what's next

Every number here comes from a **live production agent** — real tools, tool-call-verified
scoring, two languages, cost from live market pricing. We're deliberate about what it covers:

- **We score the decision turn.** Each scenario is the highest-signal moment — the
  tool-grounded response, in full context — where a car sale is actually won or lost.
  Multi-turn booking rollouts are the next axis we're adding.
- **Curated, not crawled.** 62 scenarios hand-built by the people who run Sara in production —
  search, booking, OTP, handoff, financing, cash-price, identity — in English and Arabic.
  Depth of real coverage over synthetic volume. We report **statistical tiers** (two-proportion
  z-test) with Wilson CIs, not exact ranks; the Arabic set is growing, so treat it as an early
  signal.
- **Neutral judging, checked.** A third-vendor judge (Claude-Haiku) scores every model — no
  home-team advantage. And we validated it: an independent *stronger* judge (Claude-Sonnet)
  agreed with it on **80% of a balanced sample** (Cohen's κ=0.58). Real judge noise exists —
  another reason to read tiers, not ranks — and a multi-judge panel is on the roadmap.
- **Measured on the real agent, not a proxy.** That's the entire point, and why it's a domain
  benchmark rather than a universal capability score.
- **Cost is uncached list price** — real deployments with prompt caching pay less, which is
  exactly why the dual-layer design exists.

The **security column** samples a 5,000-scenario adversarial corpus, run across every model.
(Two dedicated guard models — Llama-Guard-4, gpt-oss-safeguard — scored a perfect 100%, but
guards are classifiers, not agents, so that's expected, not comparable.) Next on the roadmap:
multi-turn rollouts, a larger Arabic set, and more self-hosted models — LFM2.5-2.6B is queued
(its laptop runtime isn't stable yet).

## Open method, private data, living board

The methodology and a representative fixture sample are public. The full 62-fixture set stays
private, on purpose — an open test set is a test set that gets gamed. This board will keep
moving as new models ship.

Thanks to the teams shipping open weights that made this interesting — Qwen, DeepSeek, Zhipu,
Mistral, Google, Meta, Moonshot, and OpenAI's open-weight line. You made our own model work
to keep its seat.

**→ Explore the interactive leaderboard:**
[Salesteq/sara-agent-benchmark-leaderboard](https://huggingface.co/spaces/Salesteq/sara-agent-benchmark-leaderboard)
· **Data:** [Salesteq/sara-agent-benchmark](https://huggingface.co/datasets/Salesteq/sara-agent-benchmark)

*Built by [Salesteq](https://salesteq.com). Numbers are a snapshot; see the dataset for the
current board.*