Long-context is becoming a migration primitive. This week, Google and Alibaba both shipped models that can ingest massive chunks of real software—monorepo slices, multi-service call chains, generated code, vendor SDKs, and long design docs—without collapsing into “summarize it first” workflows.
The hype trap: bigger context doesn’t automatically mean better engineering outcomes. But for modernization work—where correctness depends on cross-file invariants, API contracts, and subtle behavioral coupling—these releases meaningfully change what you can automate.
Models released (Feb 14–Feb 21, 2026)
| Model | Provider | Context | Key Capabilities | Migration Relevance |
|---|---|---|---|---|
| Gemini 3.1 Pro Preview | 1,048,576 tokens | reasoning, long-context, tool-use | End-to-end repo analysis, multi-step refactors with tool calls, whole-program migration planning | |
| Qwen3.5-Plus-02-15 | Alibaba (via OpenRouter listing) | 1,000,000 tokens | reasoning, long-context, code-generation | Large-scale code review and transformation across many files; “single prompt” modernization briefs |
| Qwen3.5-397B-A17B | Alibaba (via OpenRouter listing) | 262,144 tokens | reasoning, long-context, code-generation | High-end reasoning + strong code generation for complex module-by-module migrations |
1) Gemini 3.1 Pro Preview (Google)
What makes it notable
Gemini 3.1 Pro Preview is the clearest statement yet that long-context isn’t just for document QA—it’s for systems work. A 1,048,576-token window changes the ceiling on what you can keep “in working memory” at once: architectural docs + schema + multiple services + test suites + migration playbooks.
Even more important for modernization teams: Gemini’s positioning around tool-use. In practice, migrations succeed when the model can iterate—query code search, run linters, call build/test tools, and reconcile findings—rather than producing a single “best effort” patch.
How it could help with migration/modernization
Practical use cases we expect to become simpler (and cheaper in engineer time):
- Whole-slice dependency reasoning: Paste in (or retrieve) a service’s key packages plus its public interfaces, then ask for a staged plan: interface stabilization → adapter layer → new implementation → deprecation path.
- Contract-preserving refactors: With enough context to include callers and tests, you can ask the model to refactor internals (e.g., replace homegrown retry logic with a standard library) while keeping behavior consistent.
- Modernization “narratives” that actually compile: Long-context lets you include build files, config, and CI constraints. The model can propose changes that respect your toolchain reality, not just your code.
Where to stay skeptical:
- Context ≠ comprehension. Large windows reduce retrieval friction, but you still need validation loops (tests, type checks, diff review) and careful prompts that demand evidence (“cite the function and call sites”).
Key technical specs
- Context window: 1,048,576 tokens
- Capabilities: reasoning, long-context, tool-use
- Weights: closed
- Release: 2026-02-19
2) Qwen3.5-Plus-02-15 (Alibaba)
What makes it notable
Qwen3.5-Plus-02-15 brings a 1,000,000-token context option to teams building on OpenRouter’s ecosystem. For migration work, that matters because long-context is often bottlenecked by integration friction: you want to wire a model into a pipeline that can fetch repo artifacts, chunk intelligently, and run transformation steps.
This model is positioned as a “Plus” general-purpose variant with code-generation and analysis aimed at long-context workloads—exactly the mix modernization teams need for reviewing, proposing, and generating diffs.
How it could help with migration/modernization
High-value patterns where 1M context can reduce orchestration complexity:
- Monorepo change impact analysis: Provide multiple packages plus cross-package build configs and ask: “If we migrate package A from X to Y, which packages break and why?”
- Library/framework upgrades across many files: For example: Spring Boot major upgrade, React router migration, or Python packaging modernization—where the model benefits from seeing representative usage patterns across the codebase.
- Bulk refactor with consistent conventions: Include style guides, lint config, and examples of “good” patterns so generated changes align with house rules.
Practical guardrails:
- Prefer prompts that demand structured outputs (file-by-file plan, risk list, test plan).
- Require the model to emit searchable anchors (file paths, symbols, and “before/after” signatures) to make review feasible.
Key technical specs
- Context window: 1,000,000 tokens
- Capabilities: reasoning, long-context, code-generation
- Weights: closed
- Release: 2026-02-16
3) Qwen3.5-397B-A17B (Alibaba)
What makes it notable
The name signals a very large Qwen 3.5 variant—397B with A17B indicating some form of routed/MoE-style compute (as implied by the naming). Regardless of internal architecture specifics, the practical message is: this is aimed at high-end reasoning and generation, with a still-huge 262K token context.
For migration teams, 262K is often the “sweet spot” where you can include:
- an entire module or service,
- its major call chains,
- critical tests,
- plus design constraints— without paying the full complexity cost of 1M-token prompts.
How it could help with migration/modernization
This model looks well suited to deep, correctness-sensitive transformations, where you want strong reasoning and strong code output:
- Strangler-fig migrations: Generate an adapter layer that preserves legacy interfaces while routing to new services incrementally.
- Language migrations for bounded contexts: Move a subsystem from, say, Java to Kotlin or Python to Rust—keeping APIs stable and porting behavior with tests.
- Data-access layer rewrites: Convert custom SQL builders to a modern ORM or vice versa, ensuring query semantics match and edge cases are preserved.
Suggested workflow for teams:
- Use the model to produce a migration design + invariants list (what must not change).
- Have it generate diffs in small, testable batches (even if it can do more).
- Run automated checks (typecheck/lint/unit tests) and feed failures back as constraints.
Key technical specs
- Context window: 262,144 tokens
- Capabilities: reasoning, long-context, code-generation
- Weights: closed
- Release: 2026-02-16
What This Means for Migration Teams
1) “Paste the repo” is becoming real—so your process must mature
Long-context models reduce retrieval engineering, but they don’t remove the need for discipline. Expect best results when you pair them with:
- explicit invariants (public APIs, performance budgets, security constraints),
- test-first or test-parity goals,
- and artifact-based prompting (actual configs, actual error logs, actual interfaces—not paraphrases).
2) The migration unit of work shifts from “file” to “subsystem”
Historically, LLM refactors were constrained by context: you refactored a file, then hoped the rest would compile. With 262K–1M contexts, you can operate on:
- a complete module,
- a service + its clients,
- or a vertical slice spanning multiple repos.
That enables better transformations (consistent naming, consistent error handling, consistent APIs), but it also raises the bar for review. Teams should standardize:
- diff segmentation (smaller PRs even if generated together),
- traceability (link each change to a cited call site or failing test),
- rollback safety (feature flags, adapters, compatibility layers).
3) Tool-use is the difference between “draft” and “deliverable”
For modernization, the “last mile” is almost always:
- build breaks,
- config mismatches,
- subtle runtime assumptions,
- and test instability.
Models that can reliably participate in a tool loop—run tests, read compiler errors, apply targeted fixes—are more valuable than models that only produce a clean-looking patch. Gemini 3.1 Pro Preview’s tool-use emphasis is a notable signal here.
4) Long-context amplifies both signal and noise
If you dump an entire repo, you also dump:
- deprecated modules,
- dead code,
- generated code,
- copy-pasted patterns you don’t want replicated.
Migration teams should curate inputs: exclude generated artifacts, prioritize “golden path” implementations, and provide a short “do/don’t” convention list.
Closing: Big Context, Bigger Responsibility
This week’s releases make a clear point: modernization is shifting from prompt engineering to systems engineering. With Gemini 3.1 Pro Preview and Qwen 3.5’s long-context variants, you can keep more of the codebase—and more of the real-world constraints—in scope at once, which is exactly what migrations demand.
Next week’s question isn’t “Can the model refactor this file?” It’s “Can our workflow turn model output into verified, reviewable, test-passing change?” Teams that invest now in tool loops, invariant-driven prompting, and disciplined PR slicing will be the ones who convert 1M tokens of context into 1M tokens of shipping software.