Business · 14 tasks · 44 models
Fastest AI models for Sales
Which models write outbound, follow-ups, discovery, and objection responses that a real buyer would respond to?
The fastest capable model for Sales is gemini-3.1-flash-lite, at about 17.7s per run.
Top score — strong
Clears the quality bar at $0.023/run
Quality vs. cost
Every model placed by what it delivers and what it costs. The best value sits high and to the left.
Full ranking
| # | Model | Score | Cost/run | Speed | Best for |
|---|---|---|---|---|---|
| 1 | gemini-3.1-flash-lite | 73.8 Usable | $0.0233 | 17.7s | Needs review |
| 2 | gpt-5.4-mini | 72.4 Usable | $0.0269 | 20.8s | Needs review |
| 3 | grok-4.20 | 72.2 Usable | $0.0270 | 21.2s | Needs review |
| 4 | grok-4.20-beta | 76.3 Usable | $0.0272 | 21.3s | Strong drafts |
| 5 | gpt-5.4-low | 75.0 Usable | $0.0302 | 21.7s | Strong drafts |
| 6 | claude-haiku-4.5 | 74.0 Usable | $0.0247 | 21.7s | Needs review |
| 7 | gemini-3.5-flash-low | 77.1 Usable | $0.0322 | 24.1s | Strong drafts |
| 8 | gpt-5.5-low | 71.3 Usable | $0.0395 | 24.5s | Needs review |
| 9 | gpt-5.4 | 78.2 Usable | $0.0342 | 24.8s | Strong drafts |
| 10 | claude-opus-4.8-low | 83.8 Strong | $0.0399 | 25.1s | Strong drafts |
| 11 | gemini-3-flash-preview | 79.9 Usable | $0.0273 | 25.6s | Strong drafts |
| 12 | deepseek-v3.2 | 71.2 Usable | $0.0240 | 26.1s | Needs review |
| 13 | claude-opus-4.8-high | 81.9 Strong | $0.0417 | 27.2s | Strong drafts |
| 14 | gemini-3.5-flash-high | 80.1 Strong | $0.0396 | 28.4s | Strong drafts |
| 15 | gemini-3.1-pro-preview-low | 78.6 Usable | $0.0393 | 31.5s | Strong drafts |
| 16 | claude-opus-4.5 | 81.5 Strong | $0.0401 | 32.5s | Strong drafts |
| 17 | claude-sonnet-4.5 | 80.4 Strong | $0.0315 | 32.8s | Strong drafts |
| 18 | gpt-5.4-high | 77.4 Usable | $0.0483 | 33.0s | Strong drafts |
| 19 | gpt-5.5 | 75.1 Usable | $0.0479 | 33.5s | Strong drafts |
| 20 | gemini-3.1-pro-preview-high | 80.1 Strong | $0.0411 | 33.7s | Strong drafts |
| 21 | gemini-3.1-pro-preview | 79.4 Usable | $0.0463 | 34.4s | Strong drafts |
| 22 | gpt-5.5-high | 79.3 Usable | $0.0582 | 35.8s | Strong drafts |
| 23 | claude-sonnet-4.5-low | 81.7 Strong | $0.0340 | 36.9s | Strong drafts |
| 24 | claude-opus-4.5-low | 83.7 Strong | $0.0553 | 40.0s | Strong drafts |
| 25 | claude-opus-4.6 | 80.3 Strong | $0.0457 | 40.7s | Strong drafts |
| 26 | claude-opus-4.5-high | 83.1 Strong | $0.0579 | 41.8s | Strong drafts |
| 27 | claude-sonnet-4.6-low | 82.9 Strong | $0.0438 | 43.0s | Strong drafts |
| 28 | claude-opus-4.6-low | 84.6 Strong | $0.0506 | 44.0s | Strong drafts |
| 29 | claude-sonnet-4.6-high | 82.6 Strong | $0.0471 | 45.1s | Strong drafts |
| 30 | kimi-k2.7-code | 78.7 Usable | $0.0303 | 48.1s | Strong drafts |
| 31 | claude-opus-4.6-high | 81.3 Strong | $0.0570 | 49.1s | Strong drafts |
| 32 | claude-sonnet-4.5-high | 82.4 Strong | $0.0481 | 50.1s | Strong drafts |
| 33 | qwen3.7-max-low | 81.3 Strong | $0.0333 | 62.7s | Strong drafts |
| 34 | qwen3.7-max | 77.9 Usable | $0.0335 | 65.0s | Strong drafts |
| 35 | kimi-k2.5 | 81.9 Strong | $0.0238 | 65.3s | Strong drafts |
| 36 | qwen3.7-max-high | 79.7 Usable | $0.0333 | 65.6s | Strong drafts |
| 37 | qwen3.5-plus-02-15 | 81.0 Strong | $0.0248 | 71.7s | Strong drafts |
| 38 | deepseek-v3.2-low | 66.9 Needs editing | $0.0198 | 21.6s | Needs review |
| 39 | deepseek-v3.2-high | 69.9 Needs editing | $0.0240 | 26.6s | Needs review |
| 40 | mistral-medium-3.1 | 65.9 Needs editing | $0.0281 | 27.6s | Needs review |
| 41 | gpt-5-mini | 64.3 Needs editing | $0.0288 | 32.6s | Needs review |
| 42 | deepseek-v3.1-terminus | 68.4 Needs editing | $0.0241 | 35.5s | Needs review |
| 43 | glm-5 | 69.9 Needs editing | $0.0232 | 57.7s | Needs review |
| 44 | minimax-m2.7 | 59.6 Weak | $0.0286 | 73.2s | Needs review |
What separates the top models
Cold Outbound Email
mediumTests whether the model writes a first-touch cold email that leads with the prospect's problem, stays short, and makes one low-friction ask.
Leader: kimi-k2.5
Follow-up & Break-up
mediumTests multi-touch follow-ups that add new value instead of 'just checking in', and a genuine pressure-free break-up email.
Leader: claude-opus-4.5-high
Discovery Call Prep
hardTests preparation of a discovery call: open problem-centric questions, MEDDIC-style qualification, and a value hypothesis.
Leader: claude-opus-4.6-low
Objection Handling
hardTests honest, non-manipulative responses to real objections — probing the concern instead of countering, with no false urgency or reflexive discounting.
Leader: grok-4.20-beta
See the difference
Real model outputs and how our judge scored them.
Top score
claude-haiku-4.5
Outcome-sceptic clinician
95/100
“The response perfectly executes the instructions by validating the skepticism, explicitly disclaiming clinical outcomes, and effectively reframing the value around verified operational and reimbursement benefits.”
You're right to be sceptical. We don't claim Lumen reduces readmissions or improves outcomes—we can't, because that's not what we've proven, and honestly, vendors who make those promises without evidence aren't doing anyone a favour. What we *do* is handle the mechanics of remote monitoring in a way that's actually usable. Your care coordinators see alerts on a single dashboard instead of juggling vendor portals and phone calls. The ECG and BP readings stream in automatically. And we manage the entire reimbursement workflow—the documentation, the coding, the billing—so you're not leaving money on the table or drowning in compliance work. The real value is operational: you can staff remote monitoring efficiently, you get paid for it properly, and your clinicians spend time on patient decisions instead than on administrative friction. Whether *that* translates to better outcomes depends on how you use it—and that's a clinical question you'd need to answer with your own data. We're a tool that makes it feasible to monitor more patients consistently. What you do with that data is up to you. We work with 90 cardiology clinics. If you want to talk to one about whether the workflow actually works in practice, we can arrange that. But we'll never tell you it's a readmission silver bullet.
Mid-pack
grok-4.20
Insight-led touch to a finance buyer
81/100
“The model followed all instructions perfectly, including the negative constraints and the strict word count limit. The insight provided is well-articulated and relevant. The score is kept from being perfect only due to a slightly abrupt transition into the product pitch and a CTA that could be even lower friction for a C-level executive.”
**Subject:** Duplicate-invoice risk after acquisitions Dear [First Name], When companies consolidate entities post-acquisition, duplicate invoices often rise sharply. Different ERP instances, retained legacy vendors, and currency variations make it easy for the same invoice to be paid twice before the finance team can catch it. Ferrovia connects read-only to your ERP and AP systems, flags duplicates and anomalies across subsidiaries and currencies, and provides an audit-ready trail for every flag. 160 enterprises currently use us for this. Would you be open to a 15-minute screen share next week? Best regards, [Your Name]
Lowest score
glm-5
Restaurant GM, shift gaps
0/100
“The model completely failed to generate the requested email, outputting only a single digit.”
1
Where models still fail
The most common problems we flagged across all models.
Frequently asked
What is the best AI model for sales?
In our benchmarks, claude-opus-4.6-low ranks first for sales, scoring strong, across 14 test cases.
What is the cheapest good model for sales?
gemini-3.1-flash-lite is the best value: it clears our quality bar for sales at $0.023 per run.
Which model is fastest for sales?
gemini-3.1-flash-lite is the fastest model that still performs well for sales.
How we test
Each model output is scored by a strict JSON LLM judge, supported by deterministic heuristics, then normalized to a 0-100 score.
Judge: gemini-3.1-pro-preview · 700 model runs across 4 benchmarks · last tested 2026-06-30
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Prompt × model results
12 test cases · 3 evals