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sara-agent-benchmark

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.

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