AI code migration is one of the highest-ROI uses of LLMs in 2026, but it is also one of the easiest places to overspend. A simple chat prompt might use 2,000 tokens. A real migration task can use 50,000 to 500,000 tokens once you include repository analysis, dependency inspection, file-by-file edits, generated tests, compiler errors, retry loops, and human review notes.
The pricing gap between models is large enough to change the business case. Migrating a medium-sized service with a premium model for every step can cost 10x to 40x more than a routed workflow that uses long-context models for analysis, coding models for edits, and budget models for test scaffolding. The right architecture is not “use the best model everywhere.” The right architecture is “use the strongest model only where mistakes are expensive.”
This guide breaks down the token costs behind code migration workflows: repository analysis, framework upgrades, legacy refactors, test generation, and review loops. You’ll get concrete monthly scenarios, model recommendations, and cost formulas you can plug into AI Cost Check before committing budget.
The four cost drivers in AI code migration
AI migration costs come from four repeatable phases. Most teams underestimate the first and last phases because the visible work is “change code,” but the token-heavy work is understanding context and reviewing failures.
| Migration phase | Typical token pattern | Main cost driver | Recommended model tier |
|---|---|---|---|
| Repository analysis | High input, low output | Reading code, configs, dependency files, docs | Long-context budget or mid-tier |
| File-by-file refactor | Medium input, medium output | Editing code while preserving behavior | Coding model or strong general model |
| Test generation | Medium input, high output | Creating unit, integration, and regression tests | Budget model for drafts, stronger model for edge cases |
| Review and repair loops | High input, medium output | Compiler errors, failing tests, diffs, reviewer feedback | Strong coding or reasoning model |
The cost formula is straightforward:
Cost = (input tokens / 1,000,000 × input price) + (output tokens / 1,000,000 × output price)
For example, GPT-5.3 Codex costs $1.75 per 1M input tokens and $14 per 1M output tokens. A migration step using 120,000 input tokens and 20,000 output tokens costs:
- Input: 0.12 × $1.75 = $0.21
- Output: 0.02 × $14 = $0.28
- Total: $0.49 per step
That looks cheap until a migration agent runs thousands of steps across hundreds of files and retries every failing test twice.
💡 Key Takeaway: Code migration is input-heavy during analysis and output-heavy during refactors. Optimize each phase separately instead of choosing one model for the entire workflow.
Real 2026 model pricing for code migration
The table below uses current model pricing from the AI Cost Check model database. For migration work, context window matters almost as much as token price. A cheap model with a small context window may require excessive chunking, while an expensive model with a large context window can process entire modules in fewer calls.
| Model | Provider | Input / 1M | Output / 1M | Context | Best migration use |
|---|---|---|---|---|---|
| GPT-5.3 Codex | OpenAI | $1.75 | $14 | 256K | Primary code edits, agentic refactors |
| Codex Mini | OpenAI | $1.50 | $6 | 200K | Lower-cost code edits and reviews |
| GPT-5 mini | OpenAI | $0.25 | $2 | 500K | Routing, simpler edits, summaries |
| GPT-5.4 mini | OpenAI | $0.75 | $4.50 | 1.05M | Large repo analysis with capable edits |
| Claude Sonnet 4.6 | Anthropic | $3 | $15 | 1M | Complex refactors and architecture review |
| Claude Opus 4.7 | Anthropic | $5 | $25 | 1M | High-stakes legacy migration decisions |
| Gemini 3 Pro | $2 | $12 | 2M | Long-context analysis and migration planning | |
| Gemini 2.5 Flash | $0.30 | $2.50 | 1M | Budget repo analysis and test drafts | |
| DeepSeek V4 Pro | DeepSeek | $0.435 | $0.87 | 1M | Low-cost bulk transformation |
| DeepSeek V4 Flash | DeepSeek | $0.14 | $0.28 | 1M | Cheapest summaries, classification, scaffolding |
| Codestral | Mistral AI | $0.30 | $0.90 | 128K | Budget code completion and localized edits |
| Grok Code Fast 1 | xAI | $0.20 | $1.50 | 256K | Fast code edits and automation loops |
The best default stack for a cost-controlled migration is:
- DeepSeek V4 Flash or Gemini 2.5 Flash for repository inventory, file classification, and summaries.
- GPT-5.3 Codex, Codex Mini, or Claude Sonnet 4.6 for actual code edits.
- GPT-5 mini or DeepSeek V4 Pro for test generation and first-pass review.
- Claude Opus 4.7 or Gemini 3 Pro only for high-risk architectural decisions.
That single-call comparison becomes serious at scale. Run it 10,000 times and the difference is about $2,940 before retries.
Token budgets by migration task
A migration workflow should be budgeted by task type, not by repository size alone. A 50,000-line TypeScript service with clean tests can be cheaper to migrate than a 20,000-line legacy Java service with implicit behavior, generated code, and no test coverage.
Repository analysis
Repository analysis includes reading package manifests, framework configuration, dependency graphs, representative files, API boundaries, build scripts, and tests. A good analysis call produces an upgrade plan, risk map, file clusters, and migration order.
| Repository size | Input tokens | Output tokens | Example use |
|---|---|---|---|
| Small service | 150K-300K | 10K-25K | One API service, clean structure |
| Medium application | 700K-1.5M | 40K-100K | Several modules, frontend + backend |
| Large legacy system | 3M-10M | 150K-500K | Monolith, multiple languages, weak docs |
For long-context analysis, Gemini 3 Pro has a 2M-token context window at $2 input / $12 output per 1M tokens, while Claude Sonnet 4.6 has a 1M-token context window at $3 input / $15 output per 1M tokens. Gemini is usually the better first-pass long-context analyzer when the goal is repository-wide structure and dependency mapping.
File-by-file refactors
This is where output pricing dominates. A file migration usually includes the source file, related interfaces, nearby tests, project conventions, and a target diff. Typical calls use 20K-120K input tokens and 5K-40K output tokens.
For a 60K-input / 15K-output edit:
| Model | Calculation | Cost per file edit |
|---|---|---|
| DeepSeek V4 Pro | 0.06 × $0.435 + 0.015 × $0.87 | $0.039 |
| GPT-5 mini | 0.06 × $0.25 + 0.015 × $2 | $0.045 |
| Codex Mini | 0.06 × $1.50 + 0.015 × $6 | $0.180 |
| GPT-5.3 Codex | 0.06 × $1.75 + 0.015 × $14 | $0.315 |
| Claude Sonnet 4.6 | 0.06 × $3 + 0.015 × $15 | $0.405 |
| Claude Opus 4.7 | 0.06 × $5 + 0.015 × $25 | $0.675 |
For bulk mechanical edits, DeepSeek V4 Pro and GPT-5 mini are the price leaders. For edits that require deep semantic understanding, GPT-5.3 Codex and Claude Sonnet 4.6 are better primary editors.
⚠️ Warning: Output tokens are the silent budget killer. A model that is cheap on input but expensive on output becomes costly when it rewrites entire files instead of producing patches.
Cost scenario 1: React framework upgrade for a small product team
This scenario covers a small SaaS team migrating a React application from an older framework version to a current version. The repository has 120K lines of code, strong TypeScript coverage, and 220 files requiring AI-assisted edits.
Workflow assumptions
| Step | Volume | Tokens per unit | Model | Monthly cost |
|---|---|---|---|---|
| Repository analysis | 8 runs | 500K input / 40K output | Gemini 2.5 Flash | $2.00 |
| File refactors | 220 edits | 45K input / 12K output | GPT-5 mini | $14.85 |
| Test generation | 160 tests | 30K input / 10K output | DeepSeek V4 Pro | $2.97 |
| Review loops | 120 reviews | 55K input / 8K output | Codex Mini | $15.42 |
| Final architecture review | 5 reviews | 300K input / 30K output | Claude Sonnet 4.6 | $6.75 |
Total monthly AI API cost: $41.99
This is the ideal migration profile for budget routing. The codebase is modern enough that a premium model is unnecessary for every edit. GPT-5 mini handles straightforward component and API refactors at $0.25 input / $2 output per 1M tokens, while Codex Mini performs more targeted review at $1.50 input / $6 output.
A team running this workflow manually through a premium model would spend more. If all 513 calls used Claude Sonnet 4.6, the same token volume would cost roughly $144. That is still affordable, but the routed workflow saves about 71% and leaves premium capacity for the few places that need it.
📊 Quick Math: In this scenario, the average AI cost per migrated file is $0.19 across analysis, editing, tests, and review.
Cost scenario 2: Java Spring Boot migration for a platform team
This scenario covers a platform team upgrading a Java monolith from an older Spring Boot version, replacing deprecated APIs, modernizing build configuration, and generating regression tests. The repository has 450K lines of code, 900 files requiring edits, and moderate test coverage.
Workflow assumptions
| Step | Volume | Tokens per unit | Model | Monthly cost |
|---|---|---|---|---|
| Repository analysis | 20 runs | 1.2M input / 90K output | Gemini 3 Pro | $69.60 |
| Dependency migration plans | 60 plans | 250K input / 25K output | Claude Sonnet 4.6 | $67.50 |
| File refactors | 900 edits | 70K input / 18K output | GPT-5.3 Codex | $346.50 |
| Test generation | 700 tests | 45K input / 16K output | GPT-5 mini | $299.25 |
| Failing test repair | 420 loops | 90K input / 18K output | Codex Mini | $178.20 |
| Final review | 35 reviews | 500K input / 45K output | Claude Sonnet 4.6 | $114.75 |
Total monthly AI API cost: $1,075.80
This is the point where model routing becomes a management requirement. The platform team is not spending four figures because the models are expensive. It is spending four figures because migration workflows multiply calls: 900 edits, 700 tests, and 420 repair loops.
The strongest recommendation for this class of migration is to split editing and verification. Use GPT-5.3 Codex for code changes because its 256K context window is enough for most file clusters and its coding specialization reduces retry loops. Use GPT-5 mini for test drafts because output price is only $2 per 1M tokens.
If every step used Claude Sonnet 4.6, the total would be roughly $2,055. If every step used GPT-5.3 Codex, the total would be roughly $1,653. The routed workflow lands at $1,075.80 while still reserving Claude Sonnet for dependency planning and final review.
[stat] 48% Approximate cost reduction from routing a Spring Boot migration across Gemini 3 Pro, GPT-5.3 Codex, GPT-5 mini, Codex Mini, and Claude Sonnet 4.6 instead of using Claude Sonnet 4.6 for every step
Cost scenario 3: Legacy .NET modernization for an enterprise team
This scenario covers an enterprise modernization program moving a legacy .NET Framework application toward modern .NET, replacing internal libraries, documenting implicit behavior, and creating regression coverage before code changes. The repository has 1.8M lines of code, multiple services, generated files, and low test coverage.
Workflow assumptions
| Step | Volume | Tokens per unit | Model | Monthly cost |
|---|---|---|---|---|
| Repository mapping | 80 runs | 1.5M input / 100K output | Gemini 3 Pro | $336.00 |
| Legacy behavior summaries | 1,200 summaries | 80K input / 12K output | DeepSeek V4 Flash | $17.47 |
| Architecture decisions | 120 reviews | 650K input / 60K output | Claude Opus 4.7 | $570.00 |
| File refactors | 3,500 edits | 95K input / 24K output | GPT-5.3 Codex | $1,758.75 |
| Test generation | 2,800 tests | 60K input / 20K output | DeepSeek V4 Pro | $177.24 |
| Repair loops | 2,200 loops | 110K input / 22K output | Codex Mini | $561.00 |
| Security and compliance review | 180 reviews | 500K input / 45K output | Claude Sonnet 4.6 | $499.50 |
Total monthly AI API cost: $3,919.96
This is an enterprise-grade workload, but the API cost is still small compared with engineering time. At 3,500 edited files, the all-in AI cost is about $1.12 per edited file, including repository mapping, architecture decisions, tests, repairs, and compliance review.
The highest-value routing decision is using DeepSeek V4 Flash for legacy behavior summaries. At $0.14 input / $0.28 output per 1M tokens, it can process 96M input tokens and 14.4M output tokens for under $18. Running that same summary workload through Claude Opus 4.7 would cost $840.
Claude Opus 4.7 is still justified for architectural decisions because those calls influence thousands of downstream edits. The budget rule is simple: use premium models for decisions that affect many files, not for every individual file.
✅ TL;DR: Enterprise code migration does not require enterprise-sized API bills. Route bulk summaries and test generation to budget models, reserve premium models for architecture and final review, and use coding models for file edits.
Cost scenario 4: Continuous migration pipeline for a developer tools company
Some teams do not run a one-time migration. They operate a continuous code transformation pipeline: updating SDKs, rewriting examples, converting generated clients, modernizing tests, and keeping sample apps current across languages.
This scenario covers a developer tools company processing 10,000 AI migration tasks per month.
| Step | Monthly volume | Tokens per unit | Model | Monthly cost |
|---|---|---|---|---|
| Task classification | 10,000 | 12K input / 1K output | DeepSeek V4 Flash | $19.60 |
| Local code edits | 6,000 | 40K input / 10K output | Grok Code Fast 1 | $138.00 |
| Complex code edits | 2,000 | 80K input / 18K output | GPT-5.3 Codex | $784.00 |
| Test generation | 7,000 | 25K input / 8K output | DeepSeek V4 Pro | $124.43 |
| Review sampling | 1,000 | 90K input / 15K output | Claude Sonnet 4.6 | $495.00 |
Total monthly AI API cost: $1,561.03
This workflow benefits from aggressive triage. Only 20% of edits go to GPT-5.3 Codex. The rest use Grok Code Fast 1, which costs $0.20 input / $1.50 output per 1M tokens and has a 256K-token context window.
For continuous pipelines, the cost control target is not just dollars per task. It is dollars per accepted pull request. If 10,000 tasks produce 7,500 accepted changes, the AI cost is $0.21 per accepted change.
When to use long-context models
Long-context models are best for repository-wide understanding, not bulk rewriting. Use them when the model needs to see multiple modules, conventions, test patterns, dependency files, and architectural boundaries in one pass.
Recommended long-context choices:
- Gemini 3 Pro: Best for large repository analysis with 2M context and pricing of $2 input / $12 output per 1M tokens.
- Claude Sonnet 4.6: Best for design review and nuanced refactor planning with 1M context and $3 / $15 pricing.
- GPT-5.4 mini: Strong value for long-context analysis with 1.05M context and $0.75 / $4.50 pricing.
- DeepSeek V4 Flash: Cheapest option for bulk repository summaries with 1M context and $0.14 / $0.28 pricing.
A practical pattern is to run repository analysis in three layers:
- Inventory pass: classify files, dependencies, and risk using DeepSeek V4 Flash.
- Module pass: summarize each subsystem using GPT-5.4 mini or Gemini 2.5 Flash.
- Architecture pass: review risky migration decisions using Gemini 3 Pro or Claude Sonnet 4.6.
This avoids stuffing everything into premium calls. It also gives developers reusable summaries that reduce input tokens during later file edits.
When to use coding models
Coding models should handle transformations where syntax, API behavior, and project conventions matter. They are the right default for edits that must compile on the first or second attempt.
Use GPT-5.3 Codex for:
- Framework upgrades with breaking API changes
- Multi-file refactors requiring consistent naming
- Agentic edit loops with test failures
- Code that needs patch-style output instead of full rewrites
Use Codex Mini for:
- Review loops after tests fail
- Smaller pull requests
- Lower-cost patch generation
- Diff explanation and cleanup
Use Codestral or Grok Code Fast 1 for:
- Localized changes
- SDK example migrations
- Repetitive code transformations
- Fast pipeline automation
A strong coding model often reduces total cost even when its token price is higher. If a cheap model creates bad patches and doubles review loops, the workflow gets slower and more expensive. For production migrations, pay for stronger code edits and save money on summaries, classification, and test drafts.
💡 Key Takeaway: The cheapest migration workflow is not the workflow with the cheapest model. It is the workflow with the fewest failed edits, shortest review loops, and lowest premium-model usage.
When to use budget routing
Budget routing means sending each migration step to the least expensive model that can complete it reliably. The best candidates are tasks with low ambiguity and easy validation.
Route to budget models for:
- File classification
- Dependency inventory
- README and changelog summaries
- Test scaffolding
- Mechanical syntax changes
- Generated client updates
- First-pass documentation updates
Keep premium models for:
- Architecture decisions
- Security-sensitive code paths
- Authentication and authorization changes
- Data migration logic
- Concurrency and distributed systems changes
- Final review of large pull requests
Here is a routing matrix teams can adopt immediately:
| Task | Default model | Upgrade when |
|---|---|---|
| Repo inventory | DeepSeek V4 Flash | The repo exceeds context or has poor structure |
| Migration plan | Gemini 3 Pro | The plan changes architecture or data flow |
| Simple file edit | GPT-5 mini or Grok Code Fast 1 | Tests fail twice |
| Complex file edit | GPT-5.3 Codex | The file touches critical business logic |
| Test generation | DeepSeek V4 Pro | Tests encode subtle legacy behavior |
| Review loop | Codex Mini | Security, auth, payments, or data loss risk |
| Final architecture review | Claude Sonnet 4.6 | Use Claude Opus 4.7 for executive-level risk calls |
For broader model selection, compare model pairs directly with pages like GPT-5 vs DeepSeek V3.2, GPT-5 vs Claude Sonnet 4.5, and Claude Opus 4.6 vs Gemini 3 Pro.
How to estimate your own migration budget
Use this five-step budget method before running a migration agent across a full repository.
1. Count target files
Separate files into three groups:
- No edit: referenced for context only
- Simple edit: mechanical migration
- Complex edit: behavior-preserving refactor
A good first estimate is 40% simple, 20% complex, and 40% no edit for framework upgrades. Legacy modernization usually shifts toward 30% simple, 35% complex, and 35% no edit.
2. Assign token budgets per file
Use these defaults:
| File type | Input tokens | Output tokens | Review loops |
|---|---|---|---|
| Simple edit | 35K | 8K | 0.5 |
| Complex edit | 90K | 22K | 1.2 |
| Test generation | 45K | 14K | 0.7 |
These numbers include surrounding context, related interfaces, and instructions. They do not assume full repository context on every call.
3. Add repository analysis
Add one repository analysis phase per major module. For most teams:
- Small repo: 500K input / 40K output
- Medium repo: 3M input / 250K output
- Large repo: 10M input / 800K output
Run analysis once, store summaries, and inject only the relevant summary into each edit call.
4. Add retries and review loops
Set retry budget by code risk:
- UI and docs: 10-20%
- Backend business logic: 30-50%
- Legacy systems: 60-100%
- Security and payments: 100-150%
Review loops are normal. Budgeting for them upfront prevents surprise bills and gives the team a realistic completion forecast.
5. Price the workflow in a calculator
Multiply tokens by each model’s input and output price, then compare a premium-only workflow with a routed workflow. Use AI Cost Check to calculate exact totals and compare models side by side. For token fundamentals, read the AI token guide before estimating repository-scale workloads.
Clear recommendations for 2026
For most code migration projects, use this default model strategy:
| Migration type | Recommended stack | Target cost posture |
|---|---|---|
| Small framework upgrade | Gemini 2.5 Flash + GPT-5 mini + Codex Mini | Lowest cost with good reliability |
| Medium backend migration | Gemini 3 Pro + GPT-5.3 Codex + GPT-5 mini | Balanced cost and correctness |
| Enterprise legacy modernization | DeepSeek V4 Flash + Gemini 3 Pro + GPT-5.3 Codex + Claude Sonnet 4.6/Opus 4.7 | Premium only for high-risk decisions |
| Continuous code pipeline | DeepSeek V4 Flash + Grok Code Fast 1 + GPT-5.3 Codex sampling | Optimize cost per accepted PR |
The single best recommendation is to avoid premium-model bulk processing. A model like Claude Opus 4.7 is valuable at $5 input / $25 output per 1M tokens, but it should review migration strategy, not summarize thousands of files. A model like DeepSeek V4 Flash is extremely cost-effective for those summaries at $0.14 input / $0.28 output.
The second recommendation is to produce patches, not full rewritten files. Patch-style outputs reduce output tokens, make diffs easier to review, and lower the chance of formatting churn. If a full file is 2,000 lines, a patch may be 100 lines. That difference can cut output cost by 80-95% on large migrations.
The third recommendation is to cache everything: repository summaries, dependency maps, interface descriptions, test failure explanations, and migration plans. Caching reduces repeated input tokens and makes the workflow more deterministic.
Frequently asked questions
How much does AI code migration cost in 2026?
Small framework upgrades can cost $40-$150 in API usage when routed across budget and coding models. Medium backend migrations commonly land around $500-$2,000, while large enterprise legacy modernization can reach $3,000-$10,000+ per month depending on file count, retry loops, and premium review volume. Use AI Cost Check to price your exact token mix.
Which AI model is best for code migration?
Use GPT-5.3 Codex for primary code edits, Gemini 3 Pro for long-context repository analysis, DeepSeek V4 Flash for low-cost summaries, and Claude Sonnet 4.6 for high-risk architecture review. This routed stack beats a single-model workflow on cost and keeps strong models focused on the decisions that matter.
How many tokens does a code migration task use?
A single file migration usually uses 20K-120K input tokens and 5K-40K output tokens. Repository analysis can use 500K to 10M input tokens across multiple calls, while review loops often add 30-100% more tokens after tests fail or reviewers request changes.
Is it cheaper to use one premium model for the whole migration?
No. A premium-only workflow is consistently more expensive because summaries, classification, and test scaffolding do not need premium reasoning. In the Spring Boot scenario above, routing reduced cost from about $2,055 with Claude Sonnet 4.6 everywhere to $1,075.80, a reduction of roughly 48%.
How do I reduce AI code migration costs?
Reduce costs by caching repository summaries, generating patches instead of full files, routing simple tasks to budget models, and reserving premium models for architecture, security, and final review. The fastest win is moving bulk summaries from premium models to DeepSeek V4 Flash or Gemini Flash-tier models.
Calculate your migration cost
Before launching a migration agent across a full repository, price the workflow with real input and output assumptions. Start with file count, expected edit complexity, test generation volume, and review loop rate, then compare a premium-only plan against a routed plan.
Use AI Cost Check to calculate exact costs across GPT, Claude, Gemini, DeepSeek, Mistral, Llama, Grok, and Cohere models. For model-specific pricing, review GPT-5.3 Codex, Claude Sonnet 4.6, Gemini 3 Pro, and DeepSeek V4 Flash.