As a legal AI startup, we keep seeing confusion when lawyers, owners, or professionals of all sorts try to figure out the right way to think about and pick AI tools for their legal tasks. So we put together a solutions overview framed around privacy - a simple framework for evaluating the options.

LegalBench scores below are from vals.ai (April 2026 update). I list top 3 models in each tier where a comparable benchmark applies.

Tier 1: Agentic AI "co-workers"

What it is: Tools that take action - read your screen, navigate the browser, click through documents, draft inside Word. They run as a desktop app or browser extension and have access to your local files, online accounts, and live web.

Examples: Claude in Chrome / Computer Use, Perplexity Comet, ChatGPT Atlas / Operator, Cursor (for desk research)

Models behind them: Whatever the vendor wires in - typically Claude Sonnet 4.6, GPT-5.x, Gemini 3 Pro

Setup: Easy. Install extension or app, sign in, grant permissions. ~5 minutes.

Cost: $20–$200/user/mo

Privacy: Lowest. Agents screenshot, read local files, and stream them to the vendor's cloud. Some offer enterprise tiers with no-training guarantees, but you're trusting a third party with raw work product. Verify your firm's policies before letting one of these touch a client folder.

Productivity: Highest. Actual work gets done - not just text suggestions.

Support: Easy. Vendor handles it.

Best fit: Solo practitioners, in-house teams with permissive data policies, anything pre-discovery or non-confidential.

Tier 2: General-purpose proprietary chat

What it is: Direct chat interfaces: ChatGPT, Claude, Gemini app, Grok. You paste, you ask, you copy back.

Top 3 by LegalBench:

• Gemini 3.1 Pro Preview - 87.40% ($2 / $12 per 1M tokens)
• Gemini 3 Pro - 87.04% ($2 / $12)
• Gemini 3 Flash - 86.86% ($0.50 / $3) ← best price/performance

For reference: GPT 5.5 ranks 4th (86.52%, $5/$30), Claude Opus 4.6 (Thinking) ranks 8th (85.30%, $5/$25).

Setup: Easy. Sign up, log in.

Cost: $20–30/mo on consumer plans; $25–200/user/mo on enterprise tiers

Privacy: Low–medium. Consumer tiers often train on your inputs unless you opt out. Enterprise/Team tiers contractually exclude training and offer DPAs (sometimes BAAs). None of these will sign a no-sub-processor commitment - you're transitively trusting OpenAI/Anthropic/Google's vendor stack.

Productivity: High. Frontier-grade models, broad capability, no legal-specific tuning.

Support: Easy. Vendor handles it.

Best fit: Non-confidential research, public-data analysis, drafting boilerplate, learning. Not appropriate for client work without enterprise contracts and a documented policy review.

Tier 3: Privacy-improved or legal-specific platforms

What it is: Vendors that wrap proprietary or open models with stricter data handling - DPAs by default, no-training defaults, sometimes EU-only hosting, sometimes legal-specific tuning (clause libraries, redlining, citation grounding).

Examples:

• Legal-specific: Harvey, Thomson Reuters CoCounsel, Spellbook, Justee AI
• General privacy-first: Lumo (Proton), Brave Leo

Models behind them: Often a mix. Some vendors fine-tune open-weight models on legal corpora; others route different tasks to different models - frontier models for drafting, cheap models for classification, specialized models for citation grounding - picking the optimal model per product layer. This flexibility is one reason a well-built Tier 3 platform can outperform Tier 2 on legal tasks despite drawing from the same underlying base models.

What's different from Tier 2: the data layer (what's logged, retained, trained on) and the application layer (legal-specific UX, evals, domain logic).

Setup: Easy–medium. Sign up, sometimes SSO/onboarding. 5–60 minutes.

Cost: Wide range: $19 to $600, and more. Free tiers exist (Justee has a free tier with paid plans from $19/user/mo - one of the most affordable solutions for SMB on the market; Lumo and Brave Leo are free for individuals). Paid plans run from ~$19/user/mo at the consumer end up to $500+/user/mo for full legal-specific enterprise tools (Harvey, CoCounsel).

Privacy: Medium–high. Real DPAs, no training on inputs, often regional hosting, published sub-processor lists. Still cloud - your data leaves your network - but with contractual guardrails and (for the better vendors) audit trails.

Productivity: High when the platform is genuinely tuned for legal workflows; only marginally better than Tier 2 if it's a thin wrapper.

Support: Easy. Vendor handles it.

Best fit: Firms and in-house teams that need cloud convenience but require contracts and policies that consumer chat can't satisfy.

Tier 4: Self-hosted in your own cloud

What it is: You run the models in your own AWS, Google Cloud, or Azure account - via AWS Bedrock, Google Vertex AI, Azure OpenAI / Azure ML - or by deploying open-weight models on your own VMs.

Top 3 open-weight by LegalBench:

• Qwen 3.5 Plus - 85.10% ($0.40 / $2.40 via API; deployable)
• Kimi K2.6 - 84.74% ($0.95 / $4 via API; deployable)
• GLM 5.1 - 84.39% ($1 / $3.20 via API; deployable)

Honest caveat: these are 100B+ parameter MoE models. "Self-hosting" them realistically means a managed service (AWS Bedrock, Google Vertex AI Model Garden, Azure ML, Together AI) inside your cloud account - not literally on-prem unless you have datacenter GPUs.

Setup: Hard. Cloud account, model deployment, API wrapper, application layer, evals. Days to weeks.

Cost: Pay per token + infrastructure. Typically $0.10–$5 per 1M tokens at scale, plus engineering time.

Privacy: High. Data stays in your cloud account. Sub-processors are limited to your cloud vendor (AWS / Azure / GCP) - typically already covered by your existing vendor approvals.

Productivity: Depends entirely on the application layer you build or buy. The model is there; the workflow isn't.

Support: Hard. You + cloud vendor + (optionally) the model provider's enterprise tier.

Best fit: Firms with engineering capacity and high data-sensitivity requirements, or those with strict GDPR / data-residency constraints.

Tier 5: Local AI

What it is: Models running on your own hardware. Nothing leaves the workstation.

Tools: Ollama, LM Studio, llama.cpp, vLLM - desktop apps that load and run models locally.

Models that actually fit consumer/prosumer hardware: smaller Llama, Qwen, Mistral, Gemma variants. The frontier models on the LegalBench leaderboard mostly don't fit on a laptop. Realistic options for a 32–64 GB workstation are Llama-class 70B quantized or Qwen 32B-class - these aren't in the top 20 of LegalBench. Expect a 10–15 percentage-point drop from frontier accuracy.

Setup: Hardest. Hardware procurement, software install, model download, prompt engineering, your own UI. Hours to days minimum.

Cost: Hardware ($2K–$10K for a capable workstation; more for multi-GPU) + electricity. No per-token cost.

Privacy: Highest. Nothing leaves your machine.

Productivity: Lower than frontier - model quality is meaningfully worse, and you're building the workflow on top yourself.

Support: Hardest. You + open-source community.

Best fit: Highly sensitive matters, classified/government work, jurisdictions with strict data residency, or anyone unwilling to extend third-party trust at all.

Aside: Wearable AI

Limitless, Plaud, Friend, Rabbit, Bee. Niche for legal work - most are meeting-capture devices, not document workflow. Privacy varies wildly (some local-only, most pipe to vendor cloud). Useful for client-meeting note synthesis if your jurisdiction's recording rules allow it. Not a substitute for any tier above.

Quick comparison

Tier	Privacy	Productivity	Setup	Cost	Support
1. Agentic co-workers	Low	Highest	Easy	$$	Easy
2. General chat	Low–Med	High	Easy	$	Easy
3. Privacy / legal-specific	Med–High	High	Easy–Med	$$–$$$	Easy
4. Own-cloud	High	Depends	Hard	$ at scale	Hard
5. Local	Highest	Lower	Hardest	$$$ upfront	Hardest

A few honest takes

• Everyone wants Tier 1 productivity at Tier 5 privacy. That product doesn't exist. Pick a tradeoff and document why.
• "No training" is necessary but not sufficient. Read sub-processor lists. Most "private" tools still send data to AWS / Anthropic / OpenAI / Google - they just don't train on it. The data is still leaving your network.
• Local AI is overhyped for serious legal work. The quality gap vs. frontier is real. It's a fit for narrow tasks (PII redaction, classification, summarization), not full contract review or research.
• The frontier moves fast. This leaderboard will look different in three months. Pick a tier (architecture), not a specific model - models are swappable, architectures aren't.

Happy to go deeper on any of these in the following posts.

The setup:

- PartnerStack for tracking.

- 25% recurring commission for the first year (generous on purpose).

- Site converts at 1.5–2% visitor-to-paid, so the funnel itself works.

PartnerStack's paid marketplace wants $12K for placement. Hesitant to commit at that level because:

1. Affiliate traffic usually converts worse than our organic, so the real economics are unknown.

2. We don't have battle-tested ad creatives yet. It feels premature to pay for distribution before we know what works.

So: where else can we list the offer to attract real marketers?

Any directories, Slack/Discord communities, or affiliate networks worth trying before we drop $12K?

Is it possible at all or marketers work only with proven offers?

Bonus points for anywhere that skews B2B SaaS rather than e-com.

Thanks in advance.