# Best AI model for emails — September 2026

> Best AI model for emails (September 2026): on BenchLM's evidence, MAI-Thinking-1 has the highest instruction following score estimate (95.4) among models that meet the page's constraints, followed by Grok 4.3 and GLM-5.1. Evidence, constraints, and caveats included.

## Constraints on this page

A builder choosing by accuracy, hosted processing allowed, any price, ordinary input size. Every refine link below changes one answer.

## The shortlist for emails

| # | Model | Creator | Instruction following score estimate | Why |
|---|---|---|---|---|
| 1 | [MAI-Thinking-1](/models/mai-thinking-1) | Microsoft | 95.4 | Highest instruction following estimate among models that meet every stated constraint; 256K context |
| 2 | [Grok 4.3](/models/grok-4-3) | xAI | 93.2 | Estimate 93.2 on the same instruction following evidence; $1.75 for the stated workload; 1M context |
| 3 | [GLM-5.1](/models/glm-5-1) | Z.AI | 92.4 | Estimate 92.4 on the same instruction following evidence; $2.28 for the stated workload; 203K context; Open weights, so it can run on your own hardware |
| 4 | [GPT-5.2-Codex](/models/gpt-5-2-codex) | OpenAI | 92.4 | Estimate 92.4 on the same instruction following evidence; $4.55 for the stated workload; 400K context |
| 5 | [MiMo-V2.5-Pro](/models/mimo-v2-5-pro) | Xiaomi | 92.4 | Estimate 92.4 on the same instruction following evidence; 1M context |

Ordered by task score under the stated constraints.

## What this shortlist rests on

- 70% · [IFBench](/benchmarks/ifbench) · Current
- 30% · [AA-IFBench](/benchmarks/aaifbench) · Current

## What to verify before choosing

- Brief adherence is a proxy; tone and useful editing need a sample of your own work.
- Composite scores are estimates. A small score gap does not establish a reliably better model.

## Refine

- [Change any answer in the selector](/tools/llm-selector?audience=builders&family=writing&useCase=emails&priority=accuracy&privacy=hosted&budget=any&context=normal)
- [Must run on my own hardware](/tools/llm-selector?audience=builders&family=writing&useCase=emails&priority=accuracy&privacy=local&context=normal)
- [Cost matters most](/tools/llm-selector?audience=builders&family=writing&useCase=emails&priority=cost&privacy=hosted&budget=any&context=normal)
- [Speed matters most](/tools/llm-selector?audience=builders&family=writing&useCase=emails&priority=speed&privacy=hosted&budget=any&context=normal)
- [Long documents or a large codebase](/tools/llm-selector?audience=builders&family=writing&useCase=emails&priority=accuracy&privacy=hosted&budget=any&context=large)
- [Choosing an everyday assistant, not an API](/tools/llm-selector?audience=everyday&family=writing&useCase=emails&priority=accuracy&privacy=hosted&budget=any&context=normal)
- [Describe your own job instead](/tools/llm-selector)

## Questions

**Which AI model is best for emails?**

On BenchLM's public evidence, MAI-Thinking-1 by Microsoft has the highest instruction following score estimate among models that meet the page's default constraints (95.4). Ordered by task score under the stated constraints. Brief adherence is a proxy; tone and useful editing need a sample of your own work.

**What are the alternatives to MAI-Thinking-1 for emails?**

Grok 4.3 (93.2), GLM-5.1 (92.4), GPT-5.2-Codex (92.4), MiMo-V2.5-Pro (92.4) follow on the same evidence. A small gap does not establish a reliably better model; compare them on three representative examples of your own work.

**How does BenchLM pick the best ai model for emails?**

The page runs the LLM Selector with fixed answers: a builder choosing by accuracy, hosted processing allowed, any price, ordinary input size. The selector uses the instruction following evidence surface, filters by the stated constraints, and orders by that evidence. It never adds a hidden fit score or a bonus for open weights or reasoning style.

**Can I change the constraints?**

Yes. Every link under "Refine" opens the selector with one answer changed, and the address carries the answers so a result can be shared or reopened against the current dataset.

Data updated 2026-09-18. Method: bench-align-v5.5-2026-09-04.

Canonical page: https://benchlm.ai/best/for/emails