Datasets:

Salesteq
/

sara-agent-benchmark

	---
	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).