---
title: "Best LLM for GTM (2026): Which AI Model for Each Job"
description: "The best LLM for GTM depends on the job. Which models OneGTM runs for classification, scoring, research, copy and replies, with per-token prices and cost math."
url: https://getaob.com/best-llm-for-gtm
---

# Best LLM for GTM (2026): which model we run for each job, and what it costs

There is no single best LLM for GTM. A model that writes a good cold email opener costs fifty times too much to tag 100,000 companies, and the cheap model that tags them well writes flat copy. The right question is which model fits each job, at your volume, with output you can check.

Below are the nine jobs where GTM teams use language models, and for each one the models we would pick, best first. Where a card says "OneGTM uses this", that model runs in our own plays or our client pipelines today. Prices are per million tokens from each vendor's pricing page. If you are new to wiring models into outbound, start with [AI for GTM](https://getaob.com/ai-for-gtm) and come back for the model choice.

By [Garrett Wolfe](https://garrettawolfe.com), founder of OneGTM (Clay Artisan Solutions Partner) and former GTM engineer at Unify. Updated October 26, 2026.

Key takeaways

- Classification and enrichment parsing run on small models: GPT-4.1 mini, GPT-4.1 nano, Claude Haiku 5.5.
- Reasoning models bill for thinking. We measured 739 output tokens a call on GPT-5 mini against 57 on GPT-4.1 mini for the same label.
- Copy is where the expensive model pays. GPT-5.6 sol beat cheaper tiers on our opener benchmark by a wide margin.
- Search APIs (Serper, Exa) plus a cheap model beat a search-grounded model for most account research.
- Run a 25 to 50 case eval on your own labeled data before you switch models. Leaderboards will not tell you.

## Lead classification and enrichment parsing

High volume, short answers, closed label sets: is this company B2B, which industry, does this title fall in the buying committee, what is the clean company name. Use the cheapest model that passes your eval, set temperature 0, and force JSON output. This is most of our token spend by call count and a small share by dollars.

### [GPT-4.1 mini](https://developers.openai.com/api/docs/models/gpt-4.1-mini?utm_source=onegtm&utm_medium=referral&utm_campaign=best-llm-for-gtm)

OneGTM uses this

Our default classifier. It tags our own engine's records with closed-set labels, judges people matches in our sourcing plays, and fills enrichment fields in several client pipelines. It is a non-reasoning model, so it takes temperature 0 and gives the same answer on the same input, which makes evals repeatable.

**Best for:** Default classifier for closed label sets

**Pricing:** $0.40 input, $1.60 output per 1M tokens.

### [Claude Haiku 5.5](https://www.anthropic.com/claude/haiku?utm_source=onegtm&utm_medium=referral&utm_campaign=best-llm-for-gtm)

OneGTM uses this

The classify and extract model in our plays' Claude fallback: when the OpenAI key fails, Haiku takes the same prompt. Short prompts are priced lower than anything else on this page except the nano tiers, so it earns a place as a first choice for bulk tagging too.

**Best for:** Bulk tagging on short prompts, second vendor

**Pricing:** $0.10 input, $0.50 output per 1M tokens for prompts up to 100K tokens; $0.50 and $2.50 above that.

### [GPT-4.1 nano](https://developers.openai.com/api/docs/models/gpt-4.1-nano?utm_source=onegtm&utm_medium=referral&utm_campaign=best-llm-for-gtm)

OneGTM uses this

Runs the reply desk classifier in one of our recruiting pipelines, where speed matters more than nuance. On a harder news triage eval it scored 17 of 27 against GPT-4.1 mini's 25, so keep it to easy labels.

**Best for:** Easy labels at the lowest cost

**Pricing:** $0.10 input, $0.40 output per 1M tokens.

### [GPT-5 nano](https://developers.openai.com/api/docs/models/gpt-5-nano?utm_source=onegtm&utm_medium=referral&utm_campaign=best-llm-for-gtm)

The lowest list price in OpenAI's lineup. It is a reasoning model, so it will not take temperature 0 and spends output tokens thinking. Price it on measured tokens per call before you trust the list price.

**Best for:** Cheap tagging where some variance is fine

**Pricing:** $0.05 input, $0.40 output per 1M tokens.

## ICP and lead scoring

Scoring asks the model to weigh evidence against your ICP, which is a step up from tagging. Keep the rubric in the prompt, ask for the evidence quote with the score, and keep the points math in code. The model reads, your code adds up the points.

### [GPT-5 mini](https://developers.openai.com/api/docs/models/gpt-5-mini?utm_source=onegtm&utm_medium=referral&utm_campaign=best-llm-for-gtm)

OneGTM uses this

Runs fit classification on our own dashboard and the B2B check in our sourcing plays. It handles messier evidence than GPT-4.1 mini, at the cost of more output tokens and some run-to-run drift.

**Best for:** Fit scoring on messy company evidence

**Pricing:** $0.25 input, $2.00 output per 1M tokens.

### [GPT-5.6 luna](https://developers.openai.com/api/docs/models/gpt-5.6-luna?utm_source=onegtm&utm_medium=referral&utm_campaign=best-llm-for-gtm)

OneGTM uses this

The cheap tier of the GPT-5.6 family. In our client scoring pipelines it reads firm and person evidence, verifies funding claims, and checks whether a company fits. It costs a fraction of a cent per record.

**Best for:** Evidence reads at scale

**Pricing:** $0.20 input, $1.20 output per 1M tokens.

### [Claude Sonnet 5.5](https://www.anthropic.com/claude/sonnet?utm_source=onegtm&utm_medium=referral&utm_campaign=best-llm-for-gtm)

OneGTM uses this

The judge model in our plays' Claude fallback, used to check scores and copy. Use it on the borderline band of a score, not on every row.

**Best for:** Second opinion on borderline scores

**Pricing:** $2 input, $10 output per 1M tokens.

### [Gemini 3.5 Flash-Lite](https://ai.google.dev/gemini-api/docs/models?utm_source=onegtm&utm_medium=referral&utm_campaign=best-llm-for-gtm)

Google's low-cost Flash-Lite tier. A good fit when the scoring input is long, such as a full About page plus job posts, and you want one vendor for the model and Google Search grounding.

**Best for:** Long scoring inputs on a budget

**Pricing:** $0.30 input, $2.50 output per 1M tokens.

Choosing tools for an outbound build?

Send me your current stack and what you want it to do. I'll tell you what I'd keep and what I'd change.

Get a stack review

## Web search and research

For account research we split the job: a search API finds the pages, a cheap model reads them. That is cheaper and easier to debug than one search-grounded model call, because you can see exactly which pages the answer came from. Use a search-grounded model when the question is open and you want a cited paragraph back.

### [Serper](https://serper.dev/?utm_source=onegtm&utm_medium=referral&utm_campaign=best-llm-for-gtm)

OneGTM uses this

Google search results as JSON. Our plays call it for news, funding, and company lookups, then hand the result pages to a small model. It is the cheapest way we know to put fresh web facts in front of a model.

**Best for:** Google results feeding a cheap model

**Pricing:** 2,500 free queries. Prepaid packs from $50 for 50,000 queries ($1.00 per 1,000) down to $0.30 per 1,000 at 12.5M.

### [Exa](https://exa.ai/?utm_source=onegtm&utm_medium=referral&utm_campaign=best-llm-for-gtm)

OneGTM uses this

Search built for models, plus a contents endpoint that returns clean page text. One of our client pipelines pulls page contents through Exa when a plain fetch is blocked.

**Best for:** Semantic search and clean page text

**Pricing:** Search $7 per 1,000 requests (auto and fast), $4 for lite and instant; contents $1 per 1,000 pages; Answer $5 per 1,000 requests.

### [Perplexity Sonar](https://docs.perplexity.ai/?utm_source=onegtm&utm_medium=referral&utm_campaign=best-llm-for-gtm)

Search-grounded answers with citations in one call. Good for open questions like "what changed at this company this quarter" when you will read the answer yourself. We could not confirm current Sonar token rates on Perplexity's own pricing page.

**Best for:** Cited answers to open research questions

**Pricing:** See vendor pricing. Perplexity's separate Search API is $5 per 1,000 requests, or $1 per 1,000 with Fast Search.

### [Grok 4.7](https://docs.x.ai/docs/models?utm_source=onegtm&utm_medium=referral&utm_campaign=best-llm-for-gtm)

xAI's flagship model, with search tools that cover X posts. Worth a test when your buyers talk on X and you want to pull what an account has posted recently.

**Best for:** Research where X posts matter

**Pricing:** $2 input, $6 output per 1M tokens under 200K-token prompts; $4 and $12 above. Search tool pricing: see vendor pricing.

## Web page analysis

Reading a company's site to answer "what do they sell and to whom" is the most common GTM model call after classification. Strip the HTML to text first, cap the length, and ask for a quote from the page with every claim. Most bad outputs here come from thin or blocked pages, so check the input before you blame the model.

### [GPT-5.6 luna](https://developers.openai.com/api/docs/models/gpt-5.6-luna?utm_source=onegtm&utm_medium=referral&utm_campaign=best-llm-for-gtm)

OneGTM uses this

Reads stored site text to describe what a company does and who it sells to, and reads customer logo walls as images when the alt text says nothing.

**Best for:** Site descriptions and logo walls

**Pricing:** $0.20 input, $1.20 output per 1M tokens.

### [GPT-4.1 mini](https://developers.openai.com/api/docs/models/gpt-4.1-mini?utm_source=onegtm&utm_medium=referral&utm_campaign=best-llm-for-gtm)

OneGTM uses this

Scores an employer's website against the same fit rule as every cold account in one of our signal lanes. Deterministic, fast, and cheap enough to run on every signup.

**Best for:** Fit checks on a single page

**Pricing:** $0.40 input, $1.60 output per 1M tokens.

### [Gemini 3.8 Flash](https://ai.google.dev/gemini-api/docs/models?utm_source=onegtm&utm_medium=referral&utm_campaign=best-llm-for-gtm)

Google's current Flash model, with a large context window for reading many pages of one site at once. Note the scheduled price increase on January 1, 2027.

**Best for:** Reading a whole site in one call

**Pricing:** $0.75 input, $3.75 output per 1M tokens through December 31, 2026, rising to $1.50 and $7.50 on January 1, 2027.

## Decisions and routing

Gates, routing, and go or no-go calls: should this signal become an email, which lane does this lead go to, is this post about a real workload. Wrong answers here cost you sends and reputation, so this is where a reasoning model earns its price. Return structured output with a reason field and log every decision.

### [GPT-5.6 sol](https://developers.openai.com/api/docs/models/gpt-5.6-sol?utm_source=onegtm&utm_medium=referral&utm_campaign=best-llm-for-gtm)

OneGTM uses this

The top GPT-5.6 tier. It runs the workload gate in one of our client signal pipelines, deciding whether a post or article describes a job worth emailing about. It is a reasoning model: we measured 1,572 output tokens a call, most of it thinking, so set the token ceiling high or it returns nothing.

**Best for:** High-stakes gates on messy text

**Pricing:** $4 input, $20 output per 1M tokens.

### [Claude Sonnet 5.5](https://www.anthropic.com/claude/sonnet?utm_source=onegtm&utm_medium=referral&utm_campaign=best-llm-for-gtm)

OneGTM uses this

The judge in our plays' Claude fallback, at low effort. Steady at following a long rule list and explaining which rule it applied.

**Best for:** Rule-heavy judgments with a reason

**Pricing:** $2 input, $10 output per 1M tokens.

### [GPT-4.1](https://developers.openai.com/api/docs/models/gpt-4.1?utm_source=onegtm&utm_medium=referral&utm_campaign=best-llm-for-gtm)

OneGTM uses this

Non-reasoning and fast. Our competitor monitor uses it for its daily backlog call because a reasoning model on that prompt ran past the play's fetch deadline three times. Pick it when latency is a hard limit.

**Best for:** Decisions under a tight time limit

**Pricing:** $2 input, $8 output per 1M tokens.

## Copywriting: cold email and LinkedIn

Copy volume is low per campaign and the cost of a bad line is a burned prospect, so spend here. Score each model on how often its output passes your copy checks without a retry. Our copy rules and lint script live in the cold email method we run for every client; see [Claude for sales](https://getaob.com/claude-for-sales) for how we prompt for it.

### [GPT-5.6 sol](https://developers.openai.com/api/docs/models/gpt-5.6-sol?utm_source=onegtm&utm_medium=referral&utm_campaign=best-llm-for-gtm)

OneGTM uses this

Writes the signal-based openers in our client copy lanes. On our benchmark of how often a brief passed the copy gates unretried, sol scored 88%, terra 81%, luna 56%, and GPT-4.1 mini 46%. A second test on live news signals gave the same order: sol 9 of 10, GPT-4.1 mini 3 of 10.

**Best for:** Signal-based openers and first lines

**Pricing:** $4 input, $20 output per 1M tokens.

### [Claude Opus 5.5](https://www.anthropic.com/claude/opus?utm_source=onegtm&utm_medium=referral&utm_campaign=best-llm-for-gtm)

OneGTM uses this

Anthropic's top everyday model. We ran it head to head with Claude Fable 5.1 and GPT-5 as the writer in a copy pilot, scored by a judge model on how human, specific, and sales-ready each email read. Strong at holding a voice across a whole sequence.

**Best for:** Full sequences in a specific voice

**Pricing:** $4 input, $20 output per 1M tokens.

### [Claude Sonnet 5.5](https://www.anthropic.com/claude/sonnet?utm_source=onegtm&utm_medium=referral&utm_campaign=best-llm-for-gtm)

OneGTM uses this

The copy model in our plays' Claude fallback, at medium effort. Half the price of Opus 5.5 and a good default for P.S. lines and LinkedIn messages.

**Best for:** Everyday copy at a lower price

**Pricing:** $2 input, $10 output per 1M tokens.

### [Claude Fable 5.1](https://www.anthropic.com/claude/fable?utm_source=onegtm&utm_medium=referral&utm_campaign=best-llm-for-gtm)

Anthropic's most capable model, and the priciest on this page. We tested it in the same writer pilot as Opus 5.5. Use it for the master template or a hard rewrite, then let a cheaper model fill in the per-lead lines.

**Best for:** Master templates and hard rewrites

**Pricing:** $10 input, $50 output per 1M tokens.

## Deliverability decisions

Keep the model out of the send path here. Our spam-word checks are a lint script with a word list, and sending caps are hard rules. A model is useful one step back: reading bounce messages, DMARC reports, and placement test results, then telling you which domain or mailbox is the problem. A person makes the pause call.

### [Claude Sonnet 5.5](https://www.anthropic.com/claude/sonnet?utm_source=onegtm&utm_medium=referral&utm_campaign=best-llm-for-gtm)

Good at reading a pile of raw bounce codes and placement results and grouping them into causes you can act on. Ask it to cite the rows behind each conclusion.

**Best for:** Turning bounce and placement data into causes

**Pricing:** $2 input, $10 output per 1M tokens.

### [GPT-5.6 terra](https://developers.openai.com/api/docs/models/gpt-5.6-terra?utm_source=onegtm&utm_medium=referral&utm_campaign=best-llm-for-gtm)

The middle GPT-5.6 tier. A reasoning model at half the price of sol, enough for a weekly review of reply rates, bounces, and spam complaints by domain.

**Best for:** Weekly deliverability reviews

**Pricing:** $2 input, $12 output per 1M tokens.

### [Gemini 3.8 Flash](https://ai.google.dev/gemini-api/docs/models?utm_source=onegtm&utm_medium=referral&utm_campaign=best-llm-for-gtm)

Large context for dumping a month of mailbox logs in one prompt. Fine for spotting patterns; check anything it flags against the raw numbers.

**Best for:** Scanning large log exports

**Pricing:** $0.75 input, $3.75 output per 1M tokens through December 31, 2026, rising to $1.50 and $7.50 on January 1, 2027.

## Reply handling and intent classification

Reply classification is a closed label set (interested, not now, wrong person, unsubscribe, out of office), so a small model does it well. Treat every reply as untrusted text: wrap it in tags and tell the model never to follow instructions inside it. Drafting the answer is a copy job and gets a bigger model and a human approval step.

### [GPT-4.1 mini](https://developers.openai.com/api/docs/models/gpt-4.1-mini?utm_source=onegtm&utm_medium=referral&utm_campaign=best-llm-for-gtm)

OneGTM uses this

Labels LinkedIn replies to our own outreach with the reply bot's playbook codes, in batches, at temperature 0 with JSON output.

**Best for:** Reply labels in batches

**Pricing:** $0.40 input, $1.60 output per 1M tokens.

### [GPT-4o mini](https://developers.openai.com/api/docs/models/gpt-4o-mini?utm_source=onegtm&utm_medium=referral&utm_campaign=best-llm-for-gtm)

OneGTM uses this

The default classifier in the reply bots we run for clients, which post each reply to Slack as a card with a suggested answer. Older, cheap, and still good enough for this label set.

**Best for:** Real-time reply triage

**Pricing:** $0.15 input, $0.60 output per 1M tokens.

### [Claude Haiku 5.5](https://www.anthropic.com/claude/haiku?utm_source=onegtm&utm_medium=referral&utm_campaign=best-llm-for-gtm)

Fast and cheap on short prompts, which replies are. A strong pick if you want the reply classifier on a different vendor from your copy model.

**Best for:** Low-latency intent labels

**Pricing:** $0.10 input, $0.50 output per 1M tokens for prompts up to 100K tokens; $0.50 and $2.50 above that.

## Coding GTM tooling

Every play, scraper, scoring script, and reply bot above was built with a coding agent. This is where the strongest model pays back fastest, because one bad assumption in a pipeline repeats on every row. More on how we work in [Claude Code for GTM](https://getaob.com/claude-code-gtm).

### [Claude Code](https://claude.com/product/claude-code?utm_source=onegtm&utm_medium=referral&utm_campaign=best-llm-for-gtm)

OneGTM uses this

Anthropic's coding agent in the terminal. We build and run every client workspace with it: plays, enrichment waterfalls, scoring, CRM sync, dashboards, and reply bots.

**Best for:** Building and running GTM pipelines

**Pricing:** Included in Pro ($20 per month, or $17 per month billed annually), Max (from $100 per month), and Team plans; or pay API rates.

### [OpenAI Codex](https://openai.com/codex?utm_source=onegtm&utm_medium=referral&utm_campaign=best-llm-for-gtm)

OpenAI's coding agent, in the CLI, IDE, and cloud. A good choice if your team already pays for ChatGPT and your pipelines already run on OpenAI models.

**Best for:** Teams already on ChatGPT plans

**Pricing:** Included in ChatGPT Free, Go ($8), Plus ($20), Pro (from $100), and Business ($20 per user per month billed annually); or pay API rates.

Prices are from each vendor's site as of October 2026 and change often, so check before you buy. This is an opinionated list, not a paid ranking. Some links are referral links that may earn us a credit; that never changes what you pay or who makes the list.

## Best LLM for each GTM job: quick reference

| Tool | Best for | Starting price |
| --- | --- | --- |
| GPT-4.1 mini (OneGTM uses) | Default classifier for closed label sets | $0.40 / $1.60 per 1M |
| Claude Haiku 5.5 (OneGTM uses) | Bulk tagging on short prompts, second vendor | $0.10 / $0.50 per 1M (prompts up to 100K) |
| GPT-4.1 nano (OneGTM uses) | Easy labels at the lowest cost | $0.10 / $0.40 per 1M |
| GPT-5 nano | Cheap tagging where some variance is fine | $0.05 / $0.40 per 1M |
| GPT-5 mini (OneGTM uses) | Fit scoring on messy company evidence | $0.25 / $2.00 per 1M |
| GPT-5.6 luna (OneGTM uses) | Evidence reads at scale | $0.20 / $1.20 per 1M |
| Claude Sonnet 5.5 (OneGTM uses) | Second opinion on borderline scores | $2 / $10 per 1M |
| Gemini 3.5 Flash-Lite | Long scoring inputs on a budget | $0.30 / $2.50 per 1M |
| Serper (OneGTM uses) | Google results feeding a cheap model | $0.30 to $1.00 per 1,000 queries |
| Exa (OneGTM uses) | Semantic search and clean page text | Search $7 per 1,000 requests |
| Perplexity Sonar | Cited answers to open research questions | See vendor pricing |
| Grok 4.7 | Research where X posts matter | $2 / $6 per 1M |
| GPT-5.6 luna (OneGTM uses) | Site descriptions and logo walls | $0.20 / $1.20 per 1M |
| GPT-4.1 mini (OneGTM uses) | Fit checks on a single page | $0.40 / $1.60 per 1M |
| Gemini 3.8 Flash | Reading a whole site in one call | $0.75 / $3.75 per 1M (rises Jan 2027) |
| GPT-5.6 sol (OneGTM uses) | High-stakes gates on messy text | $4 / $20 per 1M |
| Claude Sonnet 5.5 (OneGTM uses) | Rule-heavy judgments with a reason | $2 / $10 per 1M |
| GPT-4.1 (OneGTM uses) | Decisions under a tight time limit | $2 / $8 per 1M |
| GPT-5.6 sol (OneGTM uses) | Signal-based openers and first lines | $4 / $20 per 1M |
| Claude Opus 5.5 (OneGTM uses) | Full sequences in a specific voice | $4 / $20 per 1M |
| Claude Sonnet 5.5 (OneGTM uses) | Everyday copy at a lower price | $2 / $10 per 1M |
| Claude Fable 5.1 | Master templates and hard rewrites | $10 / $50 per 1M |
| Claude Sonnet 5.5 | Turning bounce and placement data into causes | $2 / $10 per 1M |
| GPT-5.6 terra | Weekly deliverability reviews | $2 / $12 per 1M |
| Gemini 3.8 Flash | Scanning large log exports | $0.75 / $3.75 per 1M (rises Jan 2027) |
| GPT-4.1 mini (OneGTM uses) | Reply labels in batches | $0.40 / $1.60 per 1M |
| GPT-4o mini (OneGTM uses) | Real-time reply triage | $0.15 / $0.60 per 1M |
| Claude Haiku 5.5 | Low-latency intent labels | $0.10 / $0.50 per 1M (prompts up to 100K) |
| Claude Code (OneGTM uses) | Building and running GTM pipelines | Pro $20/month; Max from $100/month |
| OpenAI Codex | Teams already on ChatGPT plans | Plus $20/month; Pro from $100/month |

Starting prices as published by each vendor in October 2026.

## Quick picks by job

If you only read one part of this page, read this. Each line is the model we would start with, then the one to test against it.

- Classification and enrichment parsing: GPT-4.1 mini, test Claude Haiku 5.5.
- ICP and lead scoring: GPT-5 mini or GPT-5.6 luna, with Claude Sonnet 5.5 on the borderline band.
- Web research: Serper or Exa for the pages, a small model to read them.
- Web page analysis: GPT-5.6 luna, test Gemini 3.8 Flash for whole-site reads.
- Decisions and routing: GPT-5.6 sol when wrong answers are costly, GPT-4.1 when latency is the limit.
- Copywriting: GPT-5.6 sol for openers, Claude Opus 5.5 for full sequences.
- Deliverability: rules and scripts decide; Claude Sonnet 5.5 reads the logs.
- Reply handling: GPT-4.1 mini or GPT-4o mini to label, a bigger model to draft, a person to approve.
- Building the tooling: Claude Code.

## How to pick a model for a GTM job

Start from the cost of a wrong answer. A wrong industry tag costs nothing until it changes who gets emailed. A wrong opener costs a prospect. Spend model budget in proportion.

Then multiply price by volume. A job that runs on 100,000 rows a month and a job that runs on 40 briefs a month can afford models 100 times apart in price. Use real token counts, including the reasoning tokens a thinking model spends before it answers. Those are billed as output and they are invisible in the response.

Check structured output. Every GTM job on this page feeds code, so you want JSON that parses every time. OpenAI, Anthropic, and Google all offer JSON or schema-constrained output; turn it on and validate it anyway.

Check latency. Reasoning models can take many seconds per call. That is fine in a nightly batch and breaks a webhook or a play with a fetch deadline.

Last, and most important, run your own eval. Benchmarks measure general skill. Your job is your prompt on your data.

- What does a wrong answer cost, and who sees it?
- How many calls a month, at how many input and output tokens each?
- Does it need temperature 0 and repeatable answers?
- Does it return valid JSON against your schema?
- Does it finish inside your latency budget?
- Does it pass your labeled eval set?

## Run a small eval before you switch models

When we consider a model change, we start with a labeled set of 25 to 50 real cases and a script that runs each candidate over them. In one news triage eval of 27 hand-labeled cases, GPT-4.1 mini scored 25, GPT-5 mini 26 and then 24 on a repeat of the same cases, GPT-5.5 25, GPT-5.4 mini 18, and GPT-4.1 nano 17. The newer model's one-case lead was noise, it spent 739 output tokens a call to GPT-4.1 mini's 57, and it ran about four times slower. We kept GPT-4.1 mini.

The same eval showed something more useful: four rounds of prompt tightening moved the weak model 8 points, and the model swap moved it 43. Fix the prompt first, then test models, and remember that a prompt tuned on one model favors that model.

For copy, change the score. Count how often a model's output passes your copy gates without a retry, since retries are where the real cost hides.

- Pull 25 to 50 real inputs that cover easy, hard, and edge cases. Freeze them to a file.
- Label the right answer by hand before you look at any model output.
- Run each candidate with the production prompt, twice if it cannot run at temperature 0.
- Record accuracy, output tokens per call, latency, and JSON failures.
- Price the winner at your monthly volume before you switch.

## Cost math: classifying 100,000 companies

Say you classify 100,000 companies from a scraped homepage. Each call sends about 1,200 input tokens (prompt plus page text) and a non-reasoning model answers in about 60 output tokens. That is 120 million input tokens and 6 million output tokens.

For reasoning models we use output we measured on our own jobs: 739 tokens a call for GPT-5 mini on a triage label, and 1,572 for GPT-5.6 sol on a gate decision. List prices are per million tokens from each vendor's pricing page as of October 26, 2026, before any batch discount.

The spread runs from about $14 to over $3,600 for the same 100,000 labels. Pick the cheapest model that passes your eval, and spend the difference on copy, where the expensive model earns it. More tools for each step are in [AI tools for GTM](https://getaob.com/ai-tools-for-gtm).

- GPT-4.1 nano: 120M x $0.10 + 6M x $0.40 = about $14.40.
- Claude Haiku 5.5 (prompts under 100K tokens): 120M x $0.10 + 6M x $0.50 = about $15.00.
- GPT-4.1 mini: 120M x $0.40 + 6M x $1.60 = about $57.60.
- GPT-5 mini at 739 output tokens a call: 120M x $0.25 + 73.9M x $2.00 = about $177.80.
- Claude Sonnet 5.5: 120M x $2 + 6M x $10 = about $300.
- GPT-5.6 sol at 1,572 output tokens a call: 120M x $4 + 157.2M x $20 = about $3,624.

## When to get help choosing and running models

Picking a model takes an afternoon with an eval set. The work that pays off is the pipeline around it: clean inputs, structured output, cost caps per run, a fallback vendor when a key fails, and a log of every decision so you can audit it later.

OneGTM builds these pipelines with Claude Code and the models on this page, and hands you the code and the evals. Our [GTM context](https://getaob.com/gtm-context-tools) setup keeps your ICP and voice in one place so every model reads the same rules.

- Your enrichment or scoring bill is growing faster than your pipeline.
- You switched to a newer model and the results got worse or less consistent.
- Your AI-written copy reads like everyone else's.
- You want reply handling that labels, drafts, and waits for approval.

Book a call

## Best LLM for GTM FAQ

### What is the best LLM for GTM?

There is no single best LLM for GTM. Use small models such as GPT-4.1 mini or Claude Haiku 5.5 for classification and reply labels, mid-tier models such as GPT-5 mini or GPT-5.6 luna for scoring and page reads, and top models such as GPT-5.6 sol or Claude Opus 5.5 for copy and high-stakes decisions. Confirm each pick on your own labeled data.

### What is the best LLM for lead scoring?

We use GPT-5 mini and GPT-5.6 luna to read evidence against an ICP rubric, with Claude Sonnet 5.5 as a second read on borderline scores. Keep the points math in code and have the model return the evidence quote with each score.

### What is the best LLM for cold email copy?

In our benchmarks GPT-5.6 sol wrote openers that passed our copy checks without a retry 88% of the time, against 46% for GPT-4.1 mini. Claude Opus 5.5 is our pick for full sequences in a specific voice. Copy volume is low, so the expensive model is worth it here.

### Is Claude or GPT better for sales and GTM work?

Both, for different jobs. We run OpenAI models for most high-volume classification and copy, and Claude as the fallback vendor and for building everything with Claude Code. Running two vendors also means a dead API key or quota limit does not stop your pipeline.

### Should I use a reasoning model to classify leads?

Usually not. Reasoning models cannot run at temperature 0, drift between runs, and bill for thinking tokens. On one of our triage evals GPT-5 mini used 739 output tokens a call against 57 for GPT-4.1 mini and scored no better. Use a reasoning model for gates and copy.

### How much does it cost to classify 100,000 companies with an LLM?

At about 1,200 input tokens per company, roughly $14 on GPT-4.1 nano, $15 on Claude Haiku 5.5, $58 on GPT-4.1 mini, $300 on Claude Sonnet 5.5, and over $3,600 on GPT-5.6 sol once its reasoning tokens are counted, at list prices from October 2026.

### Do I need Perplexity for account research?

Not for most of it. A search API such as Serper or Exa plus a cheap model is cheaper and shows you which pages the answer came from. Perplexity Sonar is useful for open questions where you want a cited paragraph and will read it yourself.

### What is the best AI model for building GTM tools?

We build every client workspace with Claude Code. OpenAI Codex is a reasonable choice for teams already on ChatGPT plans. Use the strongest model you can for building, since a bug in a pipeline repeats on every row.

Paying for the wrong model somewhere in your pipeline?

## Related

- [AI tools for GTM](https://getaob.com/ai-tools-for-gtm): Claude, ChatGPT, Gemini, Copilot and coding agents compared for sales, marketing, RevOps and GTM engineering.
- [Claude Code for GTM](https://getaob.com/claude-code-gtm): Custom GTM tooling, data pipelines, and internal apps built with AI coding agents.
- [Claude for sales](https://getaob.com/claude-for-sales): The Claude sales plugin, Claudeforce, and the workflows that work.
- [AI for GTM](https://getaob.com/ai-for-gtm): Where AI actually helps sales, marketing, and RevOps teams.
- [GTM context tools](https://getaob.com/gtm-context-tools): What a GTM context system is, and the tools that ground AI agents and reps in your ICP, messaging, and call history.
- [AI sales automation](https://getaob.com/ai-sales-automation): Account research, follow-up, and CRM hygiene handled by AI your reps will use.
- [Best web scraping tools](https://getaob.com/web-scraping-tools): Scraping, search and browser tools for GTM research and AI agents, with what OneGTM uses and pricing.

## Get a model and cost review

Tell me what you're building. I reply the same day.

Book a call at [getaob.com/#contact](https://getaob.com/#contact) or email [team@getaob.com](mailto:team@getaob.com).