Insurance claims processing is one of the cleanest enterprise use cases for AI because the workflow is repetitive, document-heavy, and expensive when routed entirely through human adjusters. A single claim can include a first notice of loss, photos, PDFs, policy language, prior claim history, repair estimates, correspondence, fraud indicators, and a final adjuster summary. That creates large token volumes, but also creates many opportunities to use cheaper models for intake and reserve premium reasoning models for the small percentage of claims that are ambiguous or high-risk.
The cost difference is material. A straightforward AI-assisted claim can cost less than one cent in API usage with a low-cost model stack. A complex claim routed through premium reasoning can cost $0.20-$1.00+ depending on document length, tool calls, and the number of review passes. At 100,000 claims per month, model selection alone can change the bill from hundreds of dollars to tens of thousands of dollars.
This guide breaks down real 2026 API costs for insurance claims workflows: FNOL intake, document extraction, policy checks, fraud flags, adjuster summaries, and exception routing. We will compare cheap multimodal intake plus low-cost text review against premium reasoning for edge cases, then turn the math into per-claim and monthly estimates you can use for budgeting.
💡 Key Takeaway: Use low-cost models for intake, extraction, classification, and routine summaries. Reserve premium reasoning for exceptions, coverage disputes, fraud escalation, litigation risk, and high-severity claims.
The claims workflow cost model
An AI claims system usually has six billable stages. The stages do not need the same model. In fact, using one premium model for every step is the fastest way to overspend.
| Stage | Typical AI task | Token pattern | Recommended model tier |
|---|---|---|---|
| FNOL intake | Convert caller/chat/email details into structured claim fields | Medium input, small output | Low-cost fast model |
| Document extraction | Extract entities from PDFs, photos, estimates, invoices, police reports | Large input, structured output | Low-cost multimodal/text model |
| Policy checks | Compare claim facts against coverage, deductible, exclusions, endorsements | Large policy context, medium output | Mid-tier model |
| Fraud flags | Score suspicious patterns and generate reviewer notes | Medium input, small output | Low-cost or mid-tier model |
| Adjuster summary | Produce concise claim narrative, timeline, next actions | Medium input, medium output | Mid-tier model |
| Exception routing | Resolve ambiguity, coverage conflict, injury severity, litigation risk | Large input, larger output | Premium reasoning model |
For this guide, the cost math uses models and prices from AI Cost Check’s model database:
| Model | Provider | Input price / 1M tokens | Output price / 1M tokens | Context window | Best claims role |
|---|---|---|---|---|---|
| Gemini 2.0 Flash-Lite | $0.075 | $0.30 | 1,000,000 | Cheapest routine extraction | |
| Gemini 2.5 Flash-Lite | $0.10 | $0.40 | 1,000,000 | Low-cost intake and extraction | |
| GPT-5 nano | OpenAI | $0.05 | $0.40 | 128,000 | Ultra-cheap classification |
| DeepSeek V4 Flash | DeepSeek | $0.14 | $0.28 | 1,000,000 | Low-cost review and summaries |
| GPT-5 mini | OpenAI | $0.25 | $2.00 | 500,000 | Balanced production workflow |
| Gemini 3 Flash | $0.50 | $3.00 | 1,000,000 | Stronger review with large context | |
| Claude Sonnet 4.6 | Anthropic | $3.00 | $15.00 | 1,000,000 | Premium adjuster reasoning |
| GPT-5.2 pro | OpenAI | $21.00 | $168.00 | 1,000,000 | Highest-cost exception reasoning |
The per-claim formula is simple:
Cost = input tokens × input price / 1,000,000 + output tokens × output price / 1,000,000
The hard part is not the arithmetic. The hard part is estimating realistic tokens per claim. Claims workflows generate more input than output because the AI reads documents, policy text, notes, and prior communications, then produces structured JSON, summaries, flags, and decisions.
For a normal property or auto claim, a practical first-pass estimate is 20,000-60,000 input tokens and 2,000-8,000 output tokens across all automated steps. Complex claims with medical records, litigation notes, multiple estimates, or subrogation evidence can exceed 200,000 input tokens.
📊 Quick Math: A claim using 40,000 input tokens and 5,000 output tokens costs $0.0055 on Gemini 2.5 Flash-Lite, $0.0200 on GPT-5 mini, and $0.1950 on Claude Sonnet 4.6.
Per-stage token estimates for insurance claims
The following baseline assumes a practical AI-assisted claim workflow, not a toy chatbot. It includes FNOL intake, claim document extraction, policy review, risk flags, and a final adjuster-ready summary.
| Workflow step | Input tokens | Output tokens | Notes |
|---|---|---|---|
| FNOL intake normalization | 3,000 | 600 | Call transcript, web form, email, or agent notes |
| Document extraction | 18,000 | 2,000 | Photos OCR text, repair estimate, invoices, police report excerpts |
| Policy coverage check | 12,000 | 1,200 | Policy declarations, coverage terms, exclusions, deductible |
| Fraud and severity flags | 5,000 | 700 | Claim facts, prior claim metadata, suspicious pattern checklist |
| Adjuster summary | 7,000 | 1,500 | Timeline, liability notes, missing documents, next actions |
| Routing decision | 2,000 | 400 | Straight-through, human review, SIU, litigation, supervisor |
| Total routine claim | 47,000 | 6,400 | Full automated support packet |
This 47,000 input / 6,400 output routine claim is the main benchmark for this article. It is large enough to represent real claims operations and small enough for straight-through processing at scale.
Now apply current model pricing:
| Model | Cost per routine claim | Cost per 10,000 claims | Cost per 100,000 claims |
|---|---|---|---|
| GPT-5 nano | $0.0049 | $48.50 | $485 |
| Gemini 2.0 Flash-Lite | $0.0054 | $54.45 | $545 |
| Gemini 2.5 Flash-Lite | $0.0073 | $72.60 | $726 |
| DeepSeek V4 Flash | $0.0084 | $83.72 | $837 |
| GPT-5 mini | $0.0246 | $245.50 | $2,455 |
| Gemini 3 Flash | $0.0427 | $427.00 | $4,270 |
| Claude Sonnet 4.6 | $0.2370 | $2,370 | $23,700 |
| GPT-5.2 pro | $2.0610 | $20,610 | $206,100 |
The main lesson is that routine claims should not run on premium reasoning by default. For extraction, classification, and summaries, the output is structured and constrained. Low-cost models can handle these steps economically, while business rules and human QA provide guardrails.
[stat] 425x GPT-5.2 pro costs roughly 425x more than GPT-5 nano for the same routine claim token profile.
Recommended architecture: cheap intake, mid-tier review, premium exceptions
The best claims architecture is a routing system, not a single-model system. Use cheap models for high-volume deterministic work, mid-tier models for policy review and summarization, and premium models only when the claim has high financial or regulatory exposure.
A production workflow should use three lanes:
- Routine lane — low-severity, complete documentation, no coverage conflict, no fraud signal.
- Review lane — missing information, moderate severity, unclear liability, policy ambiguity, conflicting statements.
- Exception lane — injury claims, high severity, coverage dispute, suspected fraud, litigation risk, catastrophic loss, commercial complexity.
A simple model allocation looks like this:
| Lane | Share of claims | Model choice | Purpose |
|---|---|---|---|
| Routine | 70%-85% | Gemini 2.5 Flash-Lite, GPT-5 nano, DeepSeek V4 Flash | Intake, extraction, summary, routing |
| Review | 10%-25% | GPT-5 mini or Gemini 3 Flash | Policy check, better summary, adjuster preparation |
| Exception | 2%-8% | Claude Sonnet 4.6, GPT-5.2 pro, GPT-5.5 Pro | Deep reasoning, dispute analysis, escalation memo |
For most insurers, GPT-5 mini is the best default mid-tier model because its $0.25 input / $2 output per 1M tokens pricing keeps costs low while giving more headroom than nano-class models. For very large policy packets, Gemini 3 Flash is attractive because it combines a 1,000,000-token context window with $0.50 input / $3 output pricing.
Premium models have a place, but the place is exception handling. Claude Sonnet 4.6 at $3 input / $15 output is reasonable when the model is drafting an escalation memo for a claim that may involve thousands of dollars of leakage, coverage risk, or litigation exposure. GPT-5.2 pro at $21 input / $168 output should be reserved for the narrowest set of high-stakes reviews because output tokens are expensive.
The comparison above is not saying Claude Sonnet 4.6 is a bad model. It says using premium reasoning for every routine claim is a budget mistake. A claim with clean documents and no dispute does not need a premium model to extract claimant name, date of loss, deductible, vehicle damage, and missing document checklist.
Scenario 1: Regional carrier with 10,000 claims per month
A regional auto or property carrier processing 10,000 claims per month can run AI assistance very cheaply if it routes most claims through low-cost models.
Assume this monthly mix:
| Claim type | Share | Monthly claims | Token profile | Model |
|---|---|---|---|---|
| Routine | 80% | 8,000 | 47k input / 6.4k output | Gemini 2.5 Flash-Lite |
| Review | 17% | 1,700 | 75k input / 10k output | GPT-5 mini |
| Exception | 3% | 300 | 160k input / 18k output | Claude Sonnet 4.6 |
Cost math:
- Routine claim on Gemini 2.5 Flash-Lite: 47,000 × $0.10 / 1M + 6,400 × $0.40 / 1M = $0.00726
- Review claim on GPT-5 mini: 75,000 × $0.25 / 1M + 10,000 × $2.00 / 1M = $0.03875
- Exception claim on Claude Sonnet 4.6: 160,000 × $3 / 1M + 18,000 × $15 / 1M = $0.75000
Monthly cost:
| Claim lane | Monthly claims | Cost per claim | Monthly cost |
|---|---|---|---|
| Routine | 8,000 | $0.0073 | $58.08 |
| Review | 1,700 | $0.0388 | $65.88 |
| Exception | 300 | $0.7500 | $225.00 |
| Total | 10,000 | — | $348.96/month |
This is the right pattern for a regional carrier: cheap automation for most claims, modest review spend, and premium reasoning only for edge cases. The average API cost is $0.0349 per claim.
If the same 10,000 claims were all processed on Claude Sonnet 4.6 using the routine profile, the monthly cost would be $2,370. If they were all processed on GPT-5.2 pro, the monthly cost would be $20,610. Routing saves $2,021-$20,261 per month in this scenario.
✅ TL;DR: A 10,000-claim regional carrier can run a three-lane AI claims workflow for about $349/month in model API costs when only 3% of claims use premium reasoning.
Scenario 2: National carrier with 100,000 claims per month
At national scale, small per-claim differences become budget line items. A carrier processing 100,000 claims per month should treat model routing as financial infrastructure.
Assume a slightly more complex monthly mix:
| Claim type | Share | Monthly claims | Token profile | Model |
|---|---|---|---|---|
| Routine | 75% | 75,000 | 47k input / 6.4k output | Gemini 2.5 Flash-Lite |
| Review | 20% | 20,000 | 75k input / 10k output | GPT-5 mini |
| Exception | 5% | 5,000 | 180k input / 22k output | Claude Sonnet 4.6 |
Exception claims are larger here because national carriers typically include more injury, commercial, catastrophe, and disputed claims in the monthly pool.
Cost math:
- Routine: $0.00726 per claim
- Review: $0.03875 per claim
- Exception: 180,000 × $3 / 1M + 22,000 × $15 / 1M = $0.87000
Monthly cost:
| Claim lane | Monthly claims | Cost per claim | Monthly cost |
|---|---|---|---|
| Routine | 75,000 | $0.0073 | $544.50 |
| Review | 20,000 | $0.0388 | $775.00 |
| Exception | 5,000 | $0.8700 | $4,350.00 |
| Total | 100,000 | — | $5,669.50/month |
The blended average is $0.0567 per claim. That is a strong budget for a workflow that reads claim documents, extracts structured fields, checks policy language, flags fraud indicators, summarizes the file, and routes exceptions.
Now compare that to a single-model approach:
| Strategy | Monthly cost for 100,000 claims | Difference vs routed |
|---|---|---|
| Routed stack: Gemini 2.5 Flash-Lite + GPT-5 mini + Claude Sonnet 4.6 | $5,669.50 | Baseline |
| All claims on GPT-5 mini routine profile | $2,455.00 | Lower, but weaker exception reasoning |
| All claims on Gemini 3 Flash routine profile | $4,270.00 | Lower, but no premium exception lane |
| All claims on Claude Sonnet 4.6 routine profile | $23,700.00 | +$18,030.50 |
| All claims on GPT-5.2 pro routine profile | $206,100.00 | +$200,430.50 |
The routed stack costs more than running everything through GPT-5 mini because it intentionally spends money on exceptions. That is the correct trade: spend less than one cent on routine claims and spend $0.87 on claims where reasoning quality can prevent coverage leakage, missed fraud, regulatory complaints, or rework.
⚠️ Warning: Do not optimize only for lowest monthly API bill. A national carrier saving $3,000/month by eliminating premium exception review can lose more than that on a single mishandled coverage dispute.
Scenario 3: High-touch commercial claims operation
Commercial claims are heavier. A commercial property, liability, marine, cyber, or specialty workflow often includes endorsements, contracts, loss runs, emails, attachments, expert reports, invoices, and longer adjuster notes. Token volume rises quickly.
Assume 5,000 commercial claims per month with this mix:
| Claim type | Share | Monthly claims | Token profile | Model |
|---|---|---|---|---|
| Standard commercial | 60% | 3,000 | 100k input / 12k output | GPT-5 mini |
| Complex review | 30% | 1,500 | 220k input / 25k output | Gemini 3 Flash |
| Exception / dispute | 10% | 500 | 400k input / 45k output | Claude Sonnet 4.6 |
Cost math:
- Standard commercial on GPT-5 mini: 100,000 × $0.25 / 1M + 12,000 × $2 / 1M = $0.04900
- Complex review on Gemini 3 Flash: 220,000 × $0.50 / 1M + 25,000 × $3 / 1M = $0.18500
- Exception on Claude Sonnet 4.6: 400,000 × $3 / 1M + 45,000 × $15 / 1M = $1.87500
Monthly cost:
| Claim lane | Monthly claims | Cost per claim | Monthly cost |
|---|---|---|---|
| Standard commercial | 3,000 | $0.0490 | $147.00 |
| Complex review | 1,500 | $0.1850 | $277.50 |
| Exception / dispute | 500 | $1.8750 | $937.50 |
| Total | 5,000 | — | $1,362.00/month |
This workflow has a blended cost of $0.2724 per claim, much higher than personal auto or property, but still small compared with adjuster labor and claim leakage. The key is that 10% of claims consume nearly 69% of the model budget. That is normal for commercial workflows.
For commercial claims, the recommendation is clear: use GPT-5 mini for normal file preparation, Gemini 3 Flash for large-context reviews, and Claude Sonnet 4.6 for disputes. Do not use GPT-5.2 pro broadly. At the exception profile of 400k input / 45k output, GPT-5.2 pro costs:
400,000 × $21 / 1M + 45,000 × $168 / 1M = $15.96 per claim
For 500 exceptions per month, that is $7,980/month for the exception lane alone. Use it only for claims where the expected financial exposure justifies the spend.
Scenario 4: Catastrophe event surge
Catastrophe claims create a different cost pattern: very high volume, many similar documents, and strong pressure to triage quickly. Hurricanes, hailstorms, floods, and wildfires generate thousands of FNOLs and photos in a short period.
Assume a carrier receives 50,000 catastrophe claims in one month. The best architecture is aggressive low-cost triage with a small escalation lane.
| Claim type | Share | Monthly claims | Token profile | Model |
|---|---|---|---|---|
| Fast triage | 90% | 45,000 | 35k input / 3k output | Gemini 2.0 Flash-Lite |
| Adjuster packet | 8% | 4,000 | 80k input / 8k output | GPT-5 mini |
| Severe exception | 2% | 1,000 | 220k input / 20k output | Claude Sonnet 4.6 |
Cost math:
- Fast triage on Gemini 2.0 Flash-Lite: 35,000 × $0.075 / 1M + 3,000 × $0.30 / 1M = $0.003525
- Adjuster packet on GPT-5 mini: 80,000 × $0.25 / 1M + 8,000 × $2 / 1M = $0.03600
- Severe exception on Claude Sonnet 4.6: 220,000 × $3 / 1M + 20,000 × $15 / 1M = $0.96000
Monthly cost:
| Claim lane | Monthly claims | Cost per claim | Monthly cost |
|---|---|---|---|
| Fast triage | 45,000 | $0.0035 | $158.63 |
| Adjuster packet | 4,000 | $0.0360 | $144.00 |
| Severe exception | 1,000 | $0.9600 | $960.00 |
| Total | 50,000 | — | $1,262.63/month |
This scenario shows why cheap intake models matter. The catastrophe triage lane processes 45,000 claims for about $159 in API usage. The premium exception lane processes only 1,000 claims but costs $960.
For catastrophe response, the goal is not perfect reasoning on every file. The goal is speed, prioritization, and clean handoff: identify total losses, vulnerable policyholders, missing photos, suspicious duplicates, emergency living expense triggers, and claims requiring immediate adjuster contact.
What to automate first
Start with document extraction and adjuster summaries. These produce the fastest operational value and are easy to QA. Avoid beginning with fully automated coverage decisions because those require stronger governance, auditability, and human approval.
The best first five automations are:
-
FNOL structuring
Convert phone transcripts, web forms, emails, and agent notes into normalized claim fields. Use a low-cost model such as GPT-5 nano, Gemini 2.5 Flash-Lite, or DeepSeek V4 Flash. -
Document checklist generation
Identify missing police reports, photos, invoices, estimates, medical forms, proof of ownership, or contractor documentation. This reduces adjuster back-and-forth. -
Estimate and invoice extraction
Extract line items, totals, vendors, dates, vehicle/property details, and inconsistencies. Use cheap extraction first, then escalate only if totals conflict. -
Adjuster summary drafts
Generate timeline, claim status, open questions, deductible, liability notes, and next best action. GPT-5 mini is a strong default because output quality matters more here. -
Exception routing
Route claims to human review, SIU, supervisor, litigation, field adjuster, or straight-through handling. The model should explain the routing reason in structured fields.
Do not start with final claim payment approval. AI can prepare the packet, identify gaps, and recommend next actions, but payment authority should remain controlled by claim rules, adjusters, and compliance workflows.
💡 Key Takeaway: The first production milestone should be an AI-generated adjuster packet, not an AI-generated final coverage decision.
When to use cheap models, mid-tier models, and premium models
Use cheap models when the task is structured, repetitive, and easy to validate. FNOL intake, entity extraction, document classification, duplicate detection, and checklist generation are perfect cheap-model workloads.
Use mid-tier models when the task requires better synthesis. Policy checks, liability summaries, comparative estimate review, repair narrative generation, and adjuster packets benefit from stronger language quality and larger context. GPT-5 mini is the best default for this lane because it costs $0.25 input / $2 output per 1M tokens and supports a 500,000-token context window.
Use premium models when the task has financial, legal, regulatory, or reputational exposure. Coverage disputes, conflicting statements, suspected fraud, injury severity, litigation risk, complex commercial coverage, and supervisor escalation deserve stronger reasoning. Claude Sonnet 4.6 is a practical premium lane at $3 input / $15 output, while GPT-5.2 pro should be reserved for the most sensitive edge cases because it costs $21 input / $168 output.
For insurer teams comparing model families, use the model pages and comparisons directly. Start with GPT-5 mini, Gemini 3 Flash, and Claude Sonnet 4.6. For broader selection, compare GPT-5 vs Gemini 3 Pro or GPT-5 vs Claude Sonnet 4.5.
Cost controls that reduce claims AI spend
Claims AI costs are already low compared with labor, but bad architecture can still waste money. The most effective cost controls are simple.
Compress documents before model calls. Do not send every page of a policy packet to every step. Extract relevant sections first: declarations, coverage forms, exclusions, endorsements, deductibles, limits, and claim-specific clauses. A 200,000-token policy packet reduced to 25,000 relevant tokens cuts input cost by 87.5%.
Use structured outputs. JSON schemas reduce verbose output. Since output tokens often cost more than input tokens, this matters. GPT-5 mini output is 8x its input price, Claude Sonnet 4.6 output is 5x its input price, and GPT-5.2 pro output is 8x its input price.
Cache stable context. Policy language, state-specific rules, coverage templates, and internal claim handling guidelines are reused across claims. Cache or retrieve only the relevant excerpts rather than attaching the full manual each time.
Route by severity and confidence. Use the cheap model to assign severity, confidence, and escalation reason. Send only low-confidence, high-severity, or high-dollar claims to premium review.
Cap retry loops. Tool-using agents can create hidden cost spikes by retrying OCR, search, extraction, and summarization steps. Set maximum passes per claim and require human review when confidence remains low.
Track cost per closed claim, not cost per API call. A claim may trigger dozens of calls. The operational metric that matters is API cost per claim packet, API cost per closed claim, and API cost per dollar of indemnity reviewed.
⚠️ Warning: Output tokens are the silent budget killer. Long narrative summaries, repeated reasoning traces, and unrestricted agent loops can cost more than document ingestion.
Build-vs-buy cost expectations
A claims AI system has costs beyond model APIs: OCR, document storage, retrieval infrastructure, evaluation, redaction, audit logs, security review, and human QA. However, the model API line item is usually small enough that teams should optimize for accuracy and workflow fit before shaving fractions of a cent.
For a practical deployment, budget model APIs like this:
| Organization type | Monthly claim volume | Recommended budget range | Notes |
|---|---|---|---|
| Small MGA or TPA | 1,000-5,000 | $25-$250/month | Mostly low-cost intake and summaries |
| Regional carrier | 10,000-25,000 | $300-$1,500/month | Three-lane routing with small exception lane |
| National personal lines carrier | 100,000+ | $5,000-$25,000/month | More review and exception volume |
| Commercial/specialty carrier | 5,000-25,000 | $1,000-$15,000/month | Larger files and more premium review |
| Catastrophe surge month | 50,000+ | $1,000-$10,000/month | Cheap triage plus severe exception routing |
These ranges assume disciplined routing. A single-model premium deployment can exceed these numbers by 10x-40x without improving routine claim outcomes.
The recommended production stack for most insurers in 2026 is:
- Gemini 2.5 Flash-Lite or Gemini 2.0 Flash-Lite for high-volume intake and extraction.
- GPT-5 mini for adjuster packet generation and standard policy review.
- Gemini 3 Flash for large-context review where longer files matter.
- Claude Sonnet 4.6 for exception reasoning, disputes, and supervisor-ready memos.
- GPT-5.2 pro only for the narrowest, highest-stakes escalations.
That stack keeps routine cost near $0.01 per claim, standard review near $0.04-$0.20 per claim, and complex exception handling around $0.75-$2.00 per claim for most files.
Frequently asked questions
How much does AI insurance claims processing cost per claim?
A routine AI-assisted claim costs about $0.005-$0.04 in model API usage when using low-cost models such as Gemini 2.5 Flash-Lite, GPT-5 nano, DeepSeek V4 Flash, or GPT-5 mini. Complex exception claims using Claude Sonnet 4.6 typically cost around $0.75-$2.00 based on document length and output size.
What is the cheapest model for claims intake and document extraction?
For pure cost, GPT-5 nano at $0.05 input / $0.40 output per 1M tokens and Gemini 2.0 Flash-Lite at $0.075 input / $0.30 output per 1M tokens are the cheapest options in this guide. For a balanced intake and extraction lane, Gemini 2.5 Flash-Lite is a strong default because it costs $0.10 input / $0.40 output and supports a 1,000,000-token context window.
Should insurers use premium models for every claim?
No. Premium models should be reserved for exceptions, disputes, fraud escalation, injury claims, high-severity claims, and complex commercial files. Running every routine claim on Claude Sonnet 4.6 costs about $0.237 per routine claim, compared with $0.0073 on Gemini 2.5 Flash-Lite for the same token profile.
How much does a 100,000-claim monthly workflow cost?
A routed workflow with 75% routine, 20% review, and 5% exception volume costs about $5,669.50/month using Gemini 2.5 Flash-Lite, GPT-5 mini, and Claude Sonnet 4.6. Running all 100,000 routine-profile claims on GPT-5.2 pro would cost about $206,100/month, so routing is mandatory at scale.
What claims tasks should be automated first?
Automate FNOL structuring, document extraction, missing-document checklists, adjuster summaries, and exception routing first. These tasks reduce manual review time while keeping final coverage and payment decisions under existing claims governance.
Calculate your own claims AI budget
The fastest way to estimate your real monthly cost is to model your own claim mix: routine volume, review volume, exception rate, average document length, and summary length. Use AI Cost Check to compare models by input tokens, output tokens, and monthly volume.
Start with these model pages:
- Gemini 2.5 Flash-Lite for low-cost intake and extraction.
- GPT-5 mini for standard review and adjuster summaries.
- Gemini 3 Flash for large-context review.
- Claude Sonnet 4.6 for exception handling.
For model tradeoffs, review GPT-5 vs Gemini 3 Pro, GPT-5 vs Claude Sonnet 4.5, and Claude Opus 4.6 vs Gemini 3 Pro. Then plug your own claim volumes into AI Cost Check and build a routed budget before you ship.