---
license: apache-2.0
tags: [benchmark, agents, sales, arabic, bilingual, open-weight, leaderboard, tool-calling]
language: [en, ar]
pretty_name: Sara Open-Model Benchmark
configs:
  - config_name: leaderboard
    data_files: leaderboard.csv
    default: true
  - config_name: fixtures_sample
    data_files: fixtures-sample.jsonl
---

# 🚗 Sara Open-Model Benchmark

> **We run a production AI sales agent. We pay a premium for GPT-5.2 to power it. So we
> tested 19 models on our *own* agent** — and **17 of them — open and closed — are statistically tied with GPT-5.2 on quality** (Tier 1), and one of them, **gpt-oss-20B**, costs **~1/60th** as much. We're paying
> a premium for a lead we don't have.

Everyone is shipping AI agents. Almost nobody knows which model to actually put *inside*
one — because the benchmarks that rank models test trivia (MMLU) or English synthetic
tool-calls (BFCL, τ-bench), not a real agent talking to real customers.

We're not an eval lab. We're **[Salesteq](https://salesteq.com)** — we run **Sara**, a
bilingual (English + Arabic) car-sales agent live for dealerships in Saudi Arabia. So we
benchmarked the models on the real thing: our production **dual-layer prompt assembler**,
the real tool set, and **62 real car-sales scenarios** in both languages. Same agent,
same prompt, same tools — only the model swaps.

## 📊 What we found

- **Quality is a statistical tie.** 17 models — open and closed — land in one
  top tier (Tier 1); a two-proportion z-test can't separate GPT-5.2 from the open models above
  it (DeepSeek-V4-Flash leads by point estimate at 90.3%). At n=62 the raw
  ranking is noise — **the tie *is* the finding.** And the prompt was tuned *for* GPT-5.2, so
  it had every advantage.
- **So cost decides — and it's lopsided.** gpt-oss-20B sits in that same top tier at
  **~1/60th** GPT-5.2's cost per conversation. When
  quality ties, price is the whole decision.
- **Arabic separates the field.** Some models handle Arabic sales conversations as well as
  English; others fall off a cliff (worst: Llama 3.3 70B).
  This is the gap most leaderboards never test.
- **Security holds — mostly.** Against a 90-scenario adversarial probe (jailbreak, PII-exfil,
  prompt-injection), most models resist **every** attack on our agent; the smallest open
  models leak a little. Sara's security layer travels across models.
- **Self-hostable, proven.** Qwen3.5-4B (self-host) running on a **MacBook (Apple M5)** — no cloud — scored **77.4%** with balanced English/Arabic. A capable agent, on a laptop.

The full, sortable board — with the quality-vs-cost chart and the EN/AR view — is the
interactive Space: **[Salesteq/sara-agent-benchmark-leaderboard](https://huggingface.co/spaces/Salesteq/sara-agent-benchmark-leaderboard)**.

## 🔬 Method (plain)

- **One real turn** through the live Sara agent (with prior-message context), scored on
  **62 hand-built car-sales fixtures** (49 EN + 13 AR).
- **Pass** = an LLM judge scores *completeness* ≥ 0.5 **and** the agent calls the required
  tools (*tool-recall* ≥ 0.5). Judge = **Claude-Haiku** — a different vendor from every
  model on the board.
- **Cost** = real input/output tokens × **live OpenRouter pricing**. `$/1k convos` projects
  6 turns/conversation, at *uncached list price* — real deployments with prompt caching pay
  less (that's the whole point of the dual-layer design).
- **Tiers, not ranks.** Models are grouped by a two-proportion z-test (α=0.05) with Wilson
  95% CIs — read the tier, not the exact position. Latency is excluded (it was measured under
  concurrent load, so it isn't a clean per-model number).
- **Judge, checked.** An independent stronger judge (Claude-Sonnet) agreed with the primary Haiku judge on **80.0%** of a balanced sample (Cohen's κ=0.58). Real judge noise exists — a multi-judge panel is next.
- **Dual-layer only.** We report the production prompt architecture, never the deprecated one.

## 📐 Scope & how to read it

**20 models · 62 curated sales scenarios · a 5,000-scenario security corpus · 2 languages.**
Every model runs through a **live production agent** — real tools, **tool-call-verified**
scoring, cost from **live market pricing**. Deliberate design choices, stated plainly:

- **We score the decision turn.** Each scenario tests the highest-signal moment — the
  tool-grounded response, with full conversation context — where a car sale is won or lost.
  Multi-turn booking rollouts are the next axis we're adding.
- **Curated, not crawled.** 62 scenarios **hand-built by the team that runs Sara in
  production** — inventory search, booking, OTP, human handoff, financing, cash-price and
  identity behavior — across English and Arabic. Depth of real coverage over synthetic
  volume. Read scores within a few points of each other as a **tier, not an exact rank**;
  the Arabic set is expanding, so treat AR as an early signal.
- **Neutral judge.** A **third-vendor** model (Claude-Haiku) scores every entry — no
  home-team advantage. A multi-judge panel is on the roadmap.
- **Measured on the real agent, not a proxy.** Scores reflect a **deployed** production
  system — which is the whole point, and why this is a domain benchmark rather than a
  universal capability score.
- **Security is measured**, not promised: the column samples a **5,000-scenario adversarial
  corpus** (jailbreak, PII-exfiltration, prompt-injection). Two dedicated guard models
  (Llama-Guard-4, gpt-oss-safeguard) were also run and scored 100% — but note guards are
  classifiers, not agents, so their "perfect" score is expected, not comparable.

A **living production eval** we maintain and share. The method and a representative fixture
sample (`fixtures-sample.jsonl`) are public; the full set stays private so the benchmark
can't be gamed.

## 📁 Files
- `leaderboard.csv` / `.json` / `.md` — the ranked results.
- `models.json`, `pricing.json` — roster + the OpenRouter pricing snapshot used for cost.
- `fixtures-sample.jsonl` — 8 representative fixtures (of 62).

Read the story → [`BLOG.md`](./BLOG.md). Built by [Salesteq](https://salesteq.com).