Top picks for Email Drafting (2026)
Cold emails, replies, and outreach at the right tone. Ranked from 405 live models on the OpenRouter catalog, weighted for low cost, low latency, reasoning quality.
| # | Model | Score | In / 1M | Out / 1M | Context | |
|---|---|---|---|---|---|---|
| 1 | OpenAI: GPT-5.6 Lunaopenai/gpt-5.6-luna | 128 | $0.10 | $0.60 | 1,050,000 | Details → |
| 2 | OpenAI: GPT-5.6 Luna (batch)openai/gpt-5.6-luna:batch | 128 | $0.10 | $0.60 | 1,050,000 | Details → |
| 3 | Z.ai: GLM 5.2 (batch)z-ai/glm-5.2:batch | 128 | $0.70 | $2.20 | 512,000 | Details → |
| 4 | OpenAI: GPT-5 (batch)openai/gpt-5:batch | 127 | $0.62 | $5.00 | 400,000 | Details → |
| 5 | MoonshotAI: Kimi K2.6moonshotai/kimi-k2.6 | 127 | $0.95 | $4.00 | 262,144 | Details → |
| 6 | Z.ai: GLM 5.2z-ai/glm-5.2 | 127 | $0.50 | $3.15 | 1,048,576 | Details → |
| 7 | Google: Gemini 3.6 Flash (batch)google/gemini-3.6-flash:batch | 127 | $0.75 | $3.75 | 1,048,576 | Details → |
| 8 | MiniMax: MiniMax M3 (batch)minimax/minimax-m3:batch | 127 | $0.15 | $0.60 | 524,288 | Details → |
| 9 | MiniMax: MiniMax M3minimax/minimax-m3 | 127 | $0.30 | $1.20 | 1,048,576 | Details → |
| 10 | Google: Gemini 3.5 Flash (batch)google/gemini-3.5-flash:batch | 127 | $0.75 | $4.50 | 1,048,576 | Details → |
| 11 | Upstage: Solar Pro 4upstage/solar-pro4 | 126 | $0.03 | $0.12 | 524,288 | Details → |
| 12 | MoonshotAI: Kimi K2.7 Code (batch)moonshotai/kimi-k2.7-code:batch | 126 | $0.47 | $2.00 | 262,144 | Details → |
| 13 | OpenAI: GPT-5.4 Nano (batch)openai/gpt-5.4-nano:batch | 126 | $0.10 | $0.62 | 400,000 | Details → |
| 14 | Thinking Machines: Inkling Smallthinkingmachines/inkling-small | 126 | $0.45 | $1.20 | 524,288 | Details → |
| 15 | Thinking Machines: Inkling (batch)thinkingmachines/inkling:batch | 126 | $0.50 | $2.02 | 524,288 | Details → |
How we ranked these
For Email Drafting, we weight models on low cost, low latency, reasoning quality. Scores combine each model's public specs with independent benchmark results (Aider Polyglot coding scores, Artificial Analysis intelligence/coding/agentic indices) and live pricing. See full methodology →
About Email Drafting
Email drafting is the task of generating outreach messages, replies, and cold emails that match a specific tone and achieve a business objective. You need this when you're managing high-volume communication, personalizing at scale, or struggling to hit the right voice for your audience. A good model understands context clues (recipient role, prior conversation, industry), maintains consistency across threading, and avoids generic phrases that trigger spam filters or signal automation. Poor performers either sound robotic, miss tone entirely, or lose key details from your brief. The main trade-off is latency: streaming responses feel snappier but batch processing (10+ emails at once) is cheaper per token and faster at scale. Claude or GPT-4 handle this well because they preserve nuance without overthinking brevity.
When to use: Use this when you're writing multiple emails and need consistent tone, personalizing outreach messages to different recipients, or responding to inbound messages where you want to save time but stay authentic.
Common questions
Which AI model is best for cold email outreach?
Claude 3.5 Sonnet and GPT-4 both excel here because they understand nuance and avoid sounding like templates. For pure speed on a budget, GPT-4o mini works well for straightforward replies, though it can oversimplify tone adjustments. Test both with your actual email type to see which matches your brand voice most closely.
How much faster is AI email drafting compared to writing manually?
Most users see 70-85% time savings on first drafts, since the model handles structure and baseline tone instantly. The real win is revision cycles: you spend 2-3 minutes refining instead of 15-20 minutes writing from scratch. For 50+ emails per week, that compounds to 5+ hours saved.