Email classification is one of the highest-ROI AI automation use cases because the task is short, repetitive, and easy to measure. A model reads an inbound email, assigns labels such as billing, technical_support, sales_qualified, spam, urgent, or needs_human, then optionally produces a short routing reason or escalation summary. The cost profile is very different from AI agents or long-form chat: most email classification calls use 500-2,500 input tokens and 20-200 output tokens, which means even high-volume inboxes can stay under a few hundred dollars per month with the right model.
The pricing gap is large. Classifying 1 million emails with a compact first-pass model can cost under $150/month, while sending every email to a premium model with verbose summaries can push the same workload above $7,000/month. The cheapest architecture is rarely a single model; the best production setup is a layered workflow: ultra-cheap classifier first, premium review only for ambiguous or high-value messages, and summary generation only when a human actually needs context.
This guide breaks down real 2026 API cost math for support routing, sales qualification, spam and abuse triage, priority detection, and escalation summaries. All pricing uses the model rates listed on AI Cost Check, including GPT-5 nano, GPT-5 mini, Gemini 2.0 Flash-Lite, DeepSeek V4 Flash, Claude Haiku 4.5, and premium review models such as Claude Opus 4.7 and GPT-5.2.
💡 Key Takeaway: For standard email classification, use an ultra-cheap model for the first pass and reserve premium models for the 5-15% of emails that require judgment, policy review, or revenue-sensitive handling.
The baseline token model for email classification
Email classification cost is determined by four numbers:
- Average input tokens per email
- Average output tokens per classification
- Email volume per month
- Model input and output price per 1 million tokens
A practical production classification prompt usually includes:
| Prompt component | Typical tokens | Notes |
|---|---|---|
| System instruction | 150-400 | Defines labels, output JSON, safety rules |
| Routing taxonomy | 150-600 | Department names, priority levels, examples |
| Email subject and body | 200-1,500 | Short support emails are cheap; long threads cost more |
| Metadata | 30-150 | Sender domain, customer tier, language, attachments present |
| Output JSON | 30-150 | Label, confidence, priority, reason, next action |
For a clean first-pass workflow, a strong default estimate is 900 input tokens and 80 output tokens per email. That covers subject, body, sender metadata, a compact label schema, and a JSON result.
For escalation summaries, use a larger estimate: 2,000 input tokens and 250 output tokens. Summaries often include longer email threads, account context, previous ticket notes, and a human-readable explanation.
For deep review workflows, use 3,000 input tokens and 400 output tokens. This covers abuse triage, compliance review, chargeback disputes, legal escalations, and enterprise customer complaints where the model needs more context and a more detailed recommendation.
📊 Quick Math: A first-pass classifier using 900 input tokens and 80 output tokens consumes 980 total tokens per email, but input and output tokens are priced separately. At 100,000 emails/month, that is 90 million input tokens and 8 million output tokens.
2026 model pricing for email classification
Email classification does not require the largest model for every message. The best choices are small, fast models with low output costs and reliable JSON formatting. Premium models are valuable for escalation review, not routine routing.
| Model | Provider | Input price / 1M | Output price / 1M | Context | Best use |
|---|---|---|---|---|---|
| Gemini 2.0 Flash-Lite | $0.075 | $0.30 | 1,000,000 | Cheapest high-volume first pass | |
| GPT-5 nano | OpenAI | $0.05 | $0.40 | 128,000 | Low-cost structured classification |
| Mistral Small 3.2 | Mistral AI | $0.10 | $0.30 | 128,000 | Cheap multilingual routing |
| DeepSeek V4 Flash | DeepSeek | $0.14 | $0.28 | 1,000,000 | Low-cost bulk classification |
| GPT-4o mini | OpenAI | $0.15 | $0.60 | 128,000 | Reliable support triage |
| GPT-5 mini | OpenAI | $0.25 | $2.00 | 500,000 | Higher-accuracy routing and summaries |
| Claude Haiku 4.5 | Anthropic | $1.00 | $5.00 | 200,000 | Sensitive customer support review |
| Gemini 3 Flash | $0.50 | $3.00 | 1,000,000 | Better reasoning for mixed workflows | |
| GPT-5.2 | OpenAI | $1.75 | $14.00 | 1,000,000 | Premium escalation summaries |
| Claude Opus 4.7 | Anthropic | $5.00 | $25.00 | 1,000,000 | Executive, legal, and high-risk review |
The cheapest input price in this set is GPT-5 nano at $0.05 per 1M input tokens. The cheapest output price is shared by models such as DeepSeek V4 Flash at $0.28 per 1M output tokens and Gemini 2.0 Flash-Lite at $0.30 per 1M output tokens. For classification, output is small, so input price usually dominates.
The comparison above uses 900 input tokens and 80 output tokens per email. Gemini 2.0 Flash-Lite costs $0.0915 per 1,000 emails. Claude Opus 4.7 costs $6.50 per 1,000 emails. That is a 71x cost difference for the same classification volume.
Cost formula for email classification
Use this formula for any inbox automation workload:
Monthly cost = (monthly input tokens / 1,000,000 × input price) + (monthly output tokens / 1,000,000 × output price)
For first-pass classification:
- Input tokens per email: 900
- Output tokens per email: 80
- Emails per month: 100,000
- Monthly input tokens: 90,000,000
- Monthly output tokens: 8,000,000
With GPT-5 nano:
- Input: 90M × $0.05 = $4.50
- Output: 8M × $0.40 = $3.20
- Total: $7.70/month
With Gemini 2.0 Flash-Lite:
- Input: 90M × $0.075 = $6.75
- Output: 8M × $0.30 = $2.40
- Total: $9.15/month
With GPT-5 mini:
- Input: 90M × $0.25 = $22.50
- Output: 8M × $2.00 = $16.00
- Total: $38.50/month
With Claude Haiku 4.5:
- Input: 90M × $1.00 = $90.00
- Output: 8M × $5.00 = $40.00
- Total: $130.00/month
The same 100,000-email workload ranges from $7.70/month to $130/month across common classification models before reaching premium models. That range is small compared with human review costs, but the spread becomes material at millions of emails per month.
⚠️ Warning: Long email threads can multiply cost by 3-10x. Strip quoted replies, signatures, tracking footers, and previous thread history before classification unless the label requires full conversation context.
Scenario 1: Startup support inbox routing
A startup receives 20,000 inbound emails per month across support, billing, partnerships, and general contact forms. The goal is simple routing:
- Assign department
- Detect urgent complaints
- Identify refunds and cancellations
- Send spam or autoresponder bait to a low-priority queue
- Return JSON with label, confidence, and reason
Recommended model: GPT-5 nano or Gemini 2.0 Flash-Lite
Recommended prompt size:
- 800 input tokens per email
- 60 output tokens per email
Monthly volume:
- Input: 20,000 × 800 = 16M input tokens
- Output: 20,000 × 60 = 1.2M output tokens
| Model | Monthly input cost | Monthly output cost | Total monthly cost |
|---|---|---|---|
| GPT-5 nano | 16M × $0.05 = $0.80 | 1.2M × $0.40 = $0.48 | $1.28 |
| Gemini 2.0 Flash-Lite | 16M × $0.075 = $1.20 | 1.2M × $0.30 = $0.36 | $1.56 |
| DeepSeek V4 Flash | 16M × $0.14 = $2.24 | 1.2M × $0.28 = $0.34 | $2.58 |
| GPT-5 mini | 16M × $0.25 = $4.00 | 1.2M × $2.00 = $2.40 | $6.40 |
For this volume, API cost is almost negligible. The main risk is not model price; it is bad taxonomy design. A startup should avoid 40 overlapping labels and use 8-12 clear categories:
billingtechnical_supportaccount_accessbug_reportsalespartnershiprefund_or_cancellationspam_or_abuseurgent_human_reviewother
The recommended setup is one first-pass classifier and a confidence threshold. If confidence is below 0.75, route to a general support queue instead of calling a premium model. At 20,000 emails/month, a second model is usually unnecessary unless the company has high-value enterprise accounts.
Recommendation: Use GPT-5 nano for the lowest OpenAI cost, or Gemini 2.0 Flash-Lite for a very low-cost Google option with a large context window. Keep output short and structured.
Scenario 2: Mid-market support and sales triage
A SaaS company receives 250,000 emails per month across support, sales, abuse, billing, and customer success. The workflow has more business value than basic routing:
- Classify support issue type
- Detect churn risk
- Score sales leads
- Flag enterprise customer complaints
- Summarize only escalated messages
- Send high-value accounts to human review
Recommended architecture:
- First-pass classifier on every email
- Premium review for 10% of emails
- Escalation summary for 5% of emails
First pass assumptions:
- 900 input tokens
- 80 output tokens
- 250,000 emails/month
First-pass monthly tokens:
- Input: 225M
- Output: 20M
Using Gemini 2.0 Flash-Lite:
- Input: 225M × $0.075 = $16.88
- Output: 20M × $0.30 = $6.00
- First-pass total: $22.88/month
Premium review assumptions:
- 10% of emails = 25,000 emails/month
- 2,000 input tokens
- 200 output tokens
- Model: GPT-5 mini
Premium review tokens:
- Input: 25,000 × 2,000 = 50M
- Output: 25,000 × 200 = 5M
Premium review cost:
- Input: 50M × $0.25 = $12.50
- Output: 5M × $2.00 = $10.00
- Review total: $22.50/month
Escalation summary assumptions:
- 5% of emails = 12,500 emails/month
- 2,500 input tokens
- 300 output tokens
- Model: GPT-5 mini
Summary tokens:
- Input: 31.25M
- Output: 3.75M
Summary cost:
- Input: 31.25M × $0.25 = $7.81
- Output: 3.75M × $2.00 = $7.50
- Summary total: $15.31/month
Total layered workflow:
| Layer | Model | Monthly volume | Monthly cost |
|---|---|---|---|
| First-pass classification | Gemini 2.0 Flash-Lite | 250,000 emails | $22.88 |
| Premium review | GPT-5 mini | 25,000 emails | $22.50 |
| Escalation summaries | GPT-5 mini | 12,500 emails | $15.31 |
| Total | $60.69/month |
This is the sweet spot for most mid-market inbox automation. The model bill is low enough that engineering time and quality measurement matter more than marginal token savings.
A single-model approach with GPT-5 mini on every email would cost:
- First-pass equivalent input: 225M × $0.25 = $56.25
- First-pass equivalent output: 20M × $2.00 = $40.00
- Total: $96.25/month
The layered workflow costs $60.69/month and adds better handling for escalations. It is 37% cheaper than using GPT-5 mini for every first-pass classification while producing richer outputs where they matter.
✅ TL;DR: For mid-market support and sales triage, run every email through a sub-$0.10-per-1M-input model, then send the riskiest 10% to GPT-5 mini or a comparable mid-tier model.
Scenario 3: Enterprise abuse, trust, and safety triage
An enterprise platform receives 2 million inbound messages per month across user reports, abuse complaints, fraud disputes, phishing reports, marketplace policy issues, and executive escalations. This workload needs higher accuracy and auditability.
Recommended architecture:
- Bulk first-pass classification on all messages
- Trust-and-safety review for 15%
- Premium legal or executive review for 2%
- Human-readable summaries for 5%
First-pass assumptions:
- 1,000 input tokens
- 100 output tokens
- 2,000,000 emails/month
- Model: DeepSeek V4 Flash
First-pass tokens:
- Input: 2,000M
- Output: 200M
First-pass cost:
- Input: 2,000M × $0.14 = $280.00
- Output: 200M × $0.28 = $56.00
- Total: $336.00/month
Trust-and-safety review assumptions:
- 15% = 300,000 emails/month
- 2,500 input tokens
- 250 output tokens
- Model: GPT-5 mini
Review tokens:
- Input: 750M
- Output: 75M
Review cost:
- Input: 750M × $0.25 = $187.50
- Output: 75M × $2.00 = $150.00
- Total: $337.50/month
Premium review assumptions:
- 2% = 40,000 emails/month
- 3,000 input tokens
- 400 output tokens
- Model: Claude Opus 4.7
Premium tokens:
- Input: 120M
- Output: 16M
Premium review cost:
- Input: 120M × $5.00 = $600.00
- Output: 16M × $25.00 = $400.00
- Total: $1,000.00/month
Escalation summary assumptions:
- 5% = 100,000 emails/month
- 2,000 input tokens
- 250 output tokens
- Model: GPT-5.2
Summary tokens:
- Input: 200M
- Output: 25M
Summary cost:
- Input: 200M × $1.75 = $350.00
- Output: 25M × $14.00 = $350.00
- Total: $700.00/month
Enterprise monthly total:
| Layer | Model | Volume | Monthly cost |
|---|---|---|---|
| First-pass abuse classification | DeepSeek V4 Flash | 2,000,000 | $336.00 |
| Trust-and-safety review | GPT-5 mini | 300,000 | $337.50 |
| Premium executive/legal review | Claude Opus 4.7 | 40,000 | $1,000.00 |
| Escalation summaries | GPT-5.2 | 100,000 | $700.00 |
| Total | $2,373.50/month |
This is a realistic enterprise-grade AI triage bill. The premium layer is the largest cost even though it touches only 2% of messages. That is the correct design: spend expensive reasoning where mistakes carry legal, financial, or brand risk.
[stat] $2,373.50/month Estimated API cost to triage 2 million enterprise trust-and-safety emails with layered classification, review, and summaries
A naïve architecture that sends all 2 million emails directly to Claude Opus 4.7 with 3,000 input tokens and 400 output tokens would cost:
- Input: 6,000M × $5.00 = $30,000
- Output: 800M × $25.00 = $20,000
- Total: $50,000/month
The layered architecture costs $2,373.50/month, saving $47,626.50/month compared with premium-only processing.
Scenario 4: Sales qualification and lead routing
Sales inboxes have a different cost profile because the value per correct classification is higher. A support email may save minutes; a qualified enterprise lead may create thousands of dollars in pipeline.
Assume a B2B company processes 60,000 inbound sales and marketing emails per month:
- Contact form submissions
- Reply-to-campaign emails
- Demo requests
- Vendor spam
- Investor messages
- Customer expansion signals
- Enterprise procurement requests
Recommended workflow:
- Cheap first-pass classifier for all messages
- Enrichment-aware lead scoring for 25%
- Summary generation for 10% high-priority leads
First-pass assumptions:
- 850 input tokens
- 80 output tokens
- Model: GPT-5 nano
Tokens:
- Input: 60,000 × 850 = 51M
- Output: 60,000 × 80 = 4.8M
Cost:
- Input: 51M × $0.05 = $2.55
- Output: 4.8M × $0.40 = $1.92
- Total: $4.47/month
Lead scoring assumptions:
- 25% = 15,000 emails
- 1,800 input tokens including CRM fields and company metadata
- 180 output tokens
- Model: Gemini 3 Flash
Tokens:
- Input: 27M
- Output: 2.7M
Cost:
- Input: 27M × $0.50 = $13.50
- Output: 2.7M × $3.00 = $8.10
- Total: $21.60/month
High-priority summary assumptions:
- 10% = 6,000 emails
- 2,200 input tokens
- 300 output tokens
- Model: GPT-5.2
Tokens:
- Input: 13.2M
- Output: 1.8M
Cost:
- Input: 13.2M × $1.75 = $23.10
- Output: 1.8M × $14.00 = $25.20
- Total: $48.30/month
Total sales qualification workflow:
| Layer | Model | Monthly cost |
|---|---|---|
| First-pass sales/spam classification | GPT-5 nano | $4.47 |
| Lead scoring | Gemini 3 Flash | $21.60 |
| High-priority summaries | GPT-5.2 | $48.30 |
| Total | $74.37/month |
The summary layer costs more than the first two layers combined because output tokens are expensive on stronger models. That cost is still justified when summaries go only to sales reps for high-intent accounts. Do not summarize every newsletter reply, out-of-office response, or vendor pitch.
First-pass classification vs layered review
A single-model workflow is simpler to implement, but layered review wins at scale. The difference becomes visible once email volume exceeds 100,000 messages per month or when premium models are used.
Here is a structured comparison for 500,000 emails/month:
Assumptions:
- First-pass call: 900 input, 80 output
- Deep review call: 2,500 input, 300 output
- Layered workflow: first pass on all emails, deep review on 10%
| Workflow | Model strategy | Estimated monthly cost | Best for |
|---|---|---|---|
| Cheapest first-pass only | Gemini 2.0 Flash-Lite on all emails | $45.75 | Basic routing, spam, simple labels |
| OpenAI first-pass only | GPT-5 nano on all emails | $38.50 | Low-cost JSON classification |
| Mid-tier all emails | GPT-5 mini on all emails | $192.50 | Higher confidence without routing layers |
| Layered review | Gemini 2.0 Flash-Lite all + GPT-5 mini on 10% | $77.00 | Production support and sales triage |
| Premium all emails | Claude Opus 4.7 on all emails | $3,250.00 | Not recommended for routine classification |
Layered review beats premium all-email processing by a wide margin. At 500,000 emails/month, using Claude Opus 4.7 for every classification costs $3,250/month under the basic first-pass token estimate. A layered Gemini + GPT-5 mini workflow costs $77/month, a 97.6% reduction.
For additional model-level tradeoffs, compare general-purpose options like GPT-5 vs Claude Opus 4.6, GPT-5 vs Gemini 3 Pro, and GPT-5 vs DeepSeek V3.2.
When to use each model tier
Use ultra-cheap models for deterministic labels
Use GPT-5 nano, Gemini 2.0 Flash-Lite, Mistral Small 3.2, or DeepSeek V4 Flash when the task is classification with a compact taxonomy.
Good tasks:
- Department routing
- Spam detection
- Language detection
- Priority bucket assignment
- Autoresponder and bounce detection
- Basic sentiment labels
- “Needs human” flags
Recommended output:
{
"category": "billing",
"priority": "normal",
"confidence": 0.91,
"reason": "Customer asks about an invoice charge."
}
Short JSON keeps output cost down and reduces parsing failures. For very large volumes, every extra 100 output tokens across 1 million emails adds 100 million output tokens. On GPT-5 mini, that extra verbosity costs $200. On Claude Opus 4.7, it costs $2,500.
Use mid-tier models for ambiguous customer workflows
Use GPT-5 mini, Gemini 3 Flash, or Claude Haiku 4.5 when classification needs judgment.
Good tasks:
- Churn risk detection
- Refund intent classification
- Sales lead qualification
- Abuse report prioritization
- Complex support routing
- Multistep policy tagging
- Short escalation summaries
Mid-tier models are also a strong choice when a wrong label creates operational cost. If misrouting a ticket adds 10 minutes of human work, preventing only a few hundred mistakes can justify the entire monthly API bill.
Use premium models only for high-risk review
Use GPT-5.2, Claude Opus 4.7, or similar premium models for review layers where the output must be nuanced and defensible.
Good tasks:
- Legal escalation summaries
- Executive customer complaints
- Enterprise account risk review
- Trust and safety adjudication
- Fraud or chargeback dispute summaries
- Policy-sensitive abuse triage
Premium models should touch 1-5% of messages in most inbox systems. If a premium model is processing more than 15% of inbound email, tighten the first-pass classifier, add confidence thresholds, and separate routine summaries from high-risk analysis.
💡 Key Takeaway: The best inbox automation stack is not “one best model.” It is a routing system: cheap model for all email, mid-tier model for uncertain cases, premium model for the small percentage where mistakes are expensive.
Cost controls that matter in production
Remove quoted text and signatures
Email threads are noisy. Quoted replies, legal footers, images converted to text, tracking URLs, and signatures can double or triple token usage. Before calling the model, strip:
- Previous replies beginning with “On [date], [person] wrote”
- Signature blocks
- Legal disclaimers
- Repeated support macros
- Tracking links
- Marketing footers
- MIME artifacts
If average input drops from 2,000 tokens to 900 tokens across 1 million emails, GPT-5 mini input cost drops from $500 to $225. On Claude Opus 4.7, the same reduction saves $5,500 in input cost.
Keep the taxonomy small
A 60-label taxonomy increases confusion, prompt length, and review rates. Most teams should start with 8-15 labels, then add sublabels only for categories with enough volume.
A practical support taxonomy:
| Top-level label | Example sublabels |
|---|---|
| Billing | invoice, refund, payment_failed, cancellation |
| Technical support | bug, integration, performance, login |
| Account | password, permissions, plan_change |
| Sales | demo_request, pricing, procurement, expansion |
| Abuse | spam, phishing, harassment, fraud |
| Priority | urgent, enterprise, executive, legal |
| Other | unclear, vendor, autoresponder |
Use confidence thresholds
Every classification result should include a confidence score or equivalent uncertainty signal. A strong production policy is:
- 0.90+: auto-route
- 0.75-0.89: route with lower confidence flag
- 0.50-0.74: send to mid-tier review
- Below 0.50: send to human or catch-all queue
This routing strategy cuts premium review volume while protecting customer experience.
Summarize only when a human will read it
Summaries are often the largest hidden cost because they use more output tokens. A 300-token summary across 1 million emails equals 300 million output tokens. At GPT-5.2 pricing of $14 per 1M output tokens, that is $4,200 in output cost alone.
Generate summaries for:
- Escalations
- VIP accounts
- Legal-sensitive complaints
- Sales-qualified leads
- Tickets moving to a human queue
Do not generate summaries for:
- Spam
- Autoresponders
- Simple password resets
- Low-confidence vendor pitches
- Emails closed by automation
Recommended architecture for 2026
The recommended production architecture for AI email classification is a four-stage pipeline.
Stage 1: Preprocess
Clean the email before model inference. Extract subject, sender domain, recipient alias, plain-text body, attachment indicators, customer tier, and recent account metadata. Remove quoted history unless the classification requires thread context.
Stage 2: First-pass classify
Run every message through a cheap classifier such as GPT-5 nano, Gemini 2.0 Flash-Lite, or DeepSeek V4 Flash. Return strict JSON with category, priority, confidence, and routing target.
Stage 3: Selective review
Send uncertain, high-value, or high-risk messages to GPT-5 mini, Gemini 3 Flash, or Claude Haiku 4.5. Add relevant business context: customer plan, ARR tier, open incidents, recent invoices, or CRM stage.
Stage 4: Premium escalation
Use GPT-5.2 or Claude Opus 4.7 only for the small slice of messages that need nuanced reasoning, executive-ready summaries, or policy interpretation.
For most companies, the target cost per 1,000 emails should be:
| Workflow type | Target cost / 1,000 emails | Recommended setup |
|---|---|---|
| Basic routing | $0.08-$0.20 | GPT-5 nano or Gemini 2.0 Flash-Lite |
| Support triage with confidence review | $0.20-$0.80 | Cheap first pass + GPT-5 mini review |
| Sales qualification with summaries | $0.50-$2.00 | Cheap first pass + Gemini 3 Flash + GPT-5.2 summaries |
| Trust and safety triage | $1.00-$5.00 | DeepSeek/Gemini first pass + mid-tier review + premium escalation |
| Premium-only review | $5.00-$25.00+ | Reserved for legal, executive, and high-risk workflows |
These ranges give finance and engineering teams a concrete planning baseline. A 100,000-email/month support inbox should usually cost under $100/month. A 1 million-email/month enterprise triage system should usually cost hundreds to low thousands, not tens of thousands.
Frequently asked questions
How much does AI email classification cost in 2026?
Basic AI email classification costs about $0.08-$0.20 per 1,000 emails with ultra-cheap models such as GPT-5 nano or Gemini 2.0 Flash-Lite. A production workflow with selective review and summaries usually costs $0.50-$5.00 per 1,000 emails, depending on how many messages reach mid-tier or premium models.
What is the cheapest model for email classification?
GPT-5 nano is one of the cheapest options at $0.05 per 1M input tokens and $0.40 per 1M output tokens. Gemini 2.0 Flash-Lite is also extremely low-cost at $0.075 per 1M input tokens and $0.30 per 1M output tokens, making it a strong first-pass classifier for bulk inbox routing.
Should I use GPT-5 mini or a cheaper model for support routing?
Use a cheaper model such as GPT-5 nano or Gemini 2.0 Flash-Lite for routine support routing, then use GPT-5 mini for the 5-15% of emails with low confidence, enterprise customers, churn risk, or complex intent. This layered approach gives better cost control than sending every email to a mid-tier model.
How many tokens does one email classification use?
A compact first-pass email classification typically uses 800-1,000 input tokens and 60-100 output tokens. Escalation summaries usually use 2,000-3,000 input tokens and 200-400 output tokens because they include more thread context, customer metadata, and a human-readable explanation.
How do I estimate my own inbox automation bill?
Estimate average input and output tokens per email, multiply by monthly email volume, then apply your model’s per-1M-token pricing. For fast scenario planning, enter your expected token counts and model choices into AI Cost Check and compare first-pass, layered review, and premium-only workflows side by side.
Calculate your email classification costs
The safest default for 2026 is clear: use an ultra-cheap model for first-pass classification, reserve mid-tier models for uncertain or valuable messages, and call premium models only for escalations. That architecture keeps a 100,000-email support inbox near single-digit to low-double-digit monthly API cost, while scaling enterprise triage into the hundreds or low thousands instead of tens of thousands.
Use AI Cost Check to model your exact email volume, token counts, and routing percentages. Start with 900 input tokens and 80 output tokens for first-pass classification, then add separate scenarios for 10% review and 5% summaries. For model selection, compare GPT-5 vs GPT-5 mini, GPT-5 vs DeepSeek V3.2, and Claude Opus 4.6 vs GPT-5 mini before committing to a production architecture.