Model Migration Guide

How to move existing code to newer Claude models. Covers breaking changes, deprecated parameters, and drop-in replacements for retired models.

For the latest, authoritative version (with code samples in every supported language), WebFetch the Migration Guide URL from shared/live-sources.md. Use this file for the consolidated, skill-resident reference; fall back to the live docs whenever a model launch or breaking change may have shifted the picture.

This file is large. Use the section names below to jump (or Grep this file for the heading text). Read Step 0 and Step 1 first — they apply to every migration. Then read only the per-target section for the model you are migrating to.

TL;DR: Change the model ID string. If you were using budget_tokens, switch to thinking: {type: "adaptive"}. If you were using assistant prefills, they 400 on both Opus 4.6 and Sonnet 4.6 — switch to one of the prefill replacements (most often output_config.format; see the table in Breaking Changes by Source Model). If you're moving from Sonnet 4.5 to Sonnet 4.6, set effort explicitly — 4.6 defaults to high. Remove the effort-2025-11-24 and fine-grained-tool-streaming-2025-05-14 beta headers (GA on 4.6); remove interleaved-thinking-2025-05-14 once you're on adaptive thinking (keep it only while using the transitional budget_tokens escape hatch). Then drop back from client.beta.messages.create to client.messages.create. Dial back any aggressive "CRITICAL: YOU MUST" tool instructions; 4.6 follows the system prompt much more closely.

Step 0: Confirm the migration scope

Before any Write, Edit, or MultiEdit call, confirm the scope. If the user's request does not explicitly name a single file, a specific directory, or an explicit file list, ask first — do not start editing. This is non-negotiable: even imperative-sounding requests like "migrate my codebase", "move my project to X", "upgrade to Sonnet 4.6", or bare "migrate to Opus 4.7" leave the scope ambiguous and require a clarifying question. Phrases like "my project", "my code", "my codebase", "the whole thing", "everywhere", or "across the repo" are ambiguous, not directive — they tell you *what* to do but not *where*. Ask before doing.

Offer the common scopes explicitly and wait for the answer before touching any file:

1. The entire working directory
2. A specific subdirectory (e.g. src/, app/, services/billing/)
3. A specific file or a list of files

Surface this as a single clarifying question so the user can answer in one turn. Proceed without asking only when the scope is already unambiguous — the user named an exact file ("migrate extract.py to Sonnet 4.6"), pointed at a specific directory ("migrate everything under services/billing/ to Opus 4.6"), listed specific files ("update a.py and b.py"), or already answered the scope question in an earlier turn. If you can answer the question "which files is this change going to touch?" with a precise list from the prompt alone, proceed. If not, ask.

Worked example. If the user says *"Move my project to Opus 4.6. I want adaptive thinking everywhere it makes sense."* you do not know whether "my project" means the whole working directory, just src/, just the production code, or something else — the everywhere makes the intent clear (update every call site *within scope*) but the scope itself is still not defined. Do not start editing. Respond with:

Before I start editing, can you confirm the scope? I can migrate:

1. Every .py file in the working directory
2. Just the files under src/ (production code)
3. A specific subdirectory or list of files you name

>

Which one?

Then wait for the answer. The same applies to *"Migrate to Opus 4.7"* and bare *"Help me upgrade to Sonnet 4.6"* — ask before editing.

Sizing the scope question (large repos). Before asking, get a per-directory count so the user can pick concretely:

rg -l "<old-model-id>" --type-not md | cut -d/ -f1 | sort | uniq -c | sort -rn

Present the breakdown in your scope question (e.g. *"Found 217 references across 3 directories: api/ (130), api-go/ (62), routing/ (25). Which to migrate?"*). Also confirm git status is clean before surveying — unexpected modifications mean a concurrent process; stop and investigate before proceeding.

Step 1: Classify each file

Not every file that contains the old model ID is a caller of the API. Before editing, classify each file into one of these buckets — the right action differs:

Bucket 3 sub-cases — before swapping a string reference, check:

• Capability gate (e.g. if 'opus-4-6' in model_id: enables a feature) → add the new ID alongside, don't replace. The old model is still served and still has the capability, so replacing would silently disable the feature for any old-model traffic that still flows through. If you know no old-model traffic will hit this gate (single-caller codebase fully migrating), replacing is fine; if unsure, add alongside.
• Registry-assert test (e.g. assert "claude-X" in supported_models, test_X_has_N_clusters) → add an assertion for the new model alongside; keep the old one. The old model is still served, so its assertion stays valid — but the registry should also include the new model, so assert that too. Heuristic: if the test references multiple model versions in a list, it's a registry test; if one model in a struct compared only to itself, it's a generic fixture.
• Frozen / generated snapshot → regenerate, don't hand-edit.
• Coupled to a definer (e.g. an integration test that passes model authorization via a shared conftest seed list, or asserts on a billing-tier / rate-limit-group enum or a generated SKU/pricing catalog) → verify the definer has a new-model entry first. If not, add a seed entry (reusing the nearest existing tier as a placeholder); if you can't confidently do that, ask the user how to populate the definer. Do not skip the test. Swapping without populating the definer will make the test fail at runtime.

When migrating tests specifically: breaking parameters (temperature, top_p, budget_tokens) are usually absent — test fixtures rarely set sampling params on placeholder models. The breaking-change scan is still required, but expect mostly clean results.

Find intentionally-flagged sync points first. Many codebases tag spots that must change at every model launch with comment markers like MODEL LAUNCH, KEEP IN SYNC, @model-update, or similar. Grep for whatever convention the repo uses *before* the broad model-ID grep — those markers point at the load-bearing changes.

Per-SDK Syntax Reference

Code examples in this guide are Python. The same fields exist in every official Anthropic SDK — Stainless generates all 7 from the same OpenAPI spec, so JSON field names map 1:1 with only case-convention differences. Use the rows below to translate the Python examples to the SDK you are migrating.

Verify type and method names against the SDK source before writing them into customer code. WebFetch the relevant repository from the SDK source-code table in shared/live-sources.md (one row per SDK) and confirm the exact symbol — particularly for typed SDKs (Go, Java, C#) where union/builder names can differ from the JSON shape. Do not guess type names that aren't in the table below or in <lang>/claude-api/README.md.

thinking — budget_tokens → adaptive

Sampling parameters — temperature / top_p / top_k

(Remove the field entirely on Opus 4.7; on Claude 4.x keep at most one of temperature or top_p.)

Prefill replacement — structured outputs via output_config.format

thinking.display — opt back into summarized reasoning (Opus 4.7)

For any field not in these tables, the JSON key in the Python example translates directly: snake_case for Python/TypeScript/Ruby, camelCase named args for PHP, PascalCase struct fields for Go/C#, camelCase builder methods for Java.

Explain every change you make

Migration edits often look arbitrary to a user who hasn't read the release notes — a removed temperature, a deleted prefill, a rewritten system-prompt sentence. For each edit, tell the user what you changed and why, tied to the specific API or behavioral change that motivates it. Do this in your summary as you work, not just at the end.

Be especially explicit about system-prompt edits. Users are rightly protective of their prompts, and prompt-tuning changes are judgment calls (not hard API requirements). For any prompt edit:

• Quote the before and after text.
• State the behavioral shift that motivates it (e.g. *"Opus 4.7 calibrates response length to task complexity, so I added an explicit length instruction"*, or *"4.6 follows instructions more literally, so 'CRITICAL: YOU MUST use the search tool' will now overtrigger — softened to 'Use the search tool when…'"*).
• Make clear which prompt edits are optional tuning (tone, length, subagent guidance) versus which code edits are required to avoid a 400 (sampling params, budget_tokens, prefills). Never present an optional prompt change as mandatory.

If you're applying several prompt-tuning edits at once, offer them as a short list the user can accept or decline item-by-item rather than silently rewriting their system prompt.

Before You Migrate

1. Confirm the target model ID. Use only the exact strings from shared/models.md — do not append date suffixes to aliases (claude-opus-4-6, not claude-opus-4-6-20251101). Guessing an ID will 404.
2. Check which features your code uses with this checklist:

• thinking: {type: "enabled", budget_tokens: N} → migrate to adaptive thinking on Opus 4.6 / Sonnet 4.6 (still functional but deprecated)
• Assistant-turn prefills (messages ending with role: "assistant") → must change on Opus 4.6 / Sonnet 4.6 (returns 400)
• output_format parameter on messages.create() → must change on all models (deprecated API-wide)
• max_tokens > ~16000 → must stream on any model (above ~16K risks SDK HTTP timeouts). When streaming, Sonnet 4.6 / Haiku 4.5 cap at 64K and Opus 4.6 caps at 128K
• Beta headers effort-2025-11-24, fine-grained-tool-streaming-2025-05-14, interleaved-thinking-2025-05-14 → GA on 4.6, remove them and switch from client.beta.messages.create to client.messages.create
• Moving Sonnet 4.5 → Sonnet 4.6 with no effort set → 4.6 defaults to high, which may change your latency/cost profile
• System prompts with CRITICAL, MUST, If in doubt, use X language → likely to overtrigger on 4.6 (see Prompt-Behavior Changes)
• Coming from 3.x / 4.0 / 4.1: also check sampling params (temperature + top_p), tool versions (text_editor_20250728), refusal + model_context_window_exceeded stop reasons, trailing-newline tool-param handling

1. Test on a single request first. Run one call against the new model, inspect the response, then roll out.

Destination Models (recommended targets)

Default to the latest Opus for the caller's tier unless they explicitly chose otherwise. If you're moving from Opus 4.5 or older directly to Opus 4.7, apply the 4.6 migration first, then layer the Opus 4.7 changes on top (see Migrating to Opus 4.7 below).

Retired Model Replacements

These models return 404 — update immediately:

Deprecated Models (retiring soon)

Breaking Changes by Source Model

Migrating from Sonnet 4.5 to Sonnet 4.6 (effort default change)

Sonnet 4.5 had no effort parameter; Sonnet 4.6 defaults to high. If you just switch the model string and do nothing else, you may see noticeably higher latency and token usage. Set effort explicitly.

Recommended starting points:

For non-thinking chat workloads specifically:

client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=8192,
    thinking={"type": "disabled"},
    output_config={"effort": "low"},
    messages=[{"role": "user", "content": "..."}],
)

When to use Opus 4.6 instead: hardest and longest-horizon problems — large code migrations, deep research, extended autonomous work. Sonnet 4.6 wins on fast turnaround and cost efficiency.

Migrating to Opus 4.6 / Sonnet 4.6 (from any older model)

1. Manual extended thinking is deprecated — use adaptive thinking.

thinking: {type: "enabled", budget_tokens: N} (manual extended thinking with a fixed token budget) is deprecated on Opus 4.6 and Sonnet 4.6. Replace it with thinking: {type: "adaptive"}, which lets Claude decide when and how much to think. Adaptive thinking also enables interleaved thinking automatically (no beta header needed).

# Old (still works on older models, deprecated on 4.6)
response = client.messages.create(
    model="claude-sonnet-4-5",
    max_tokens=16000,
    thinking={"type": "enabled", "budget_tokens": 8000},
    messages=[...]
)

# New (Opus 4.6 / Sonnet 4.6)
response = client.messages.create(
    model="claude-opus-4-6",  # or "claude-sonnet-4-6"
    max_tokens=16000,
    thinking={"type": "adaptive"},
    output_config={"effort": "high"},  # optional: low | medium | high | max
    messages=[...]
)

Adaptive thinking is the long-term target, and on internal evaluations it outperforms manual extended thinking. Move when you can.

Transitional escape hatch: manual extended thinking is still *functional* on Opus 4.6 and Sonnet 4.6 (deprecated, will be removed in a future release). If you need a hard ceiling while migrating — for example, to bound token spend on a runaway workload before you've tuned effort — you can keep budget_tokens around alongside an explicit effort value, then remove it in a follow-up. budget_tokens must be strictly less than max_tokens:

# Transitional only — deprecated, plan to remove
client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=16384,
    thinking={"type": "enabled", "budget_tokens": 8192},  # must be < max_tokens
    output_config={"effort": "medium"},
    messages=[...],
)

If the user asks for a "thinking budget" on 4.6, the preferred answer is effort — use low, medium, high, or max (Opus-tier only — not Sonnet or Haiku) rather than a token count.

2. Effort parameter (Opus 4.5, Opus 4.6, Sonnet 4.6 only).

Controls thinking depth and overall token spend. Goes inside output_config, not top-level. Default is high. max is Opus-tier only (Opus 4.6 and later — not Sonnet or Haiku). Errors on Sonnet 4.5 and Haiku 4.5.

output_config={"effort": "medium"}  # often the best cost / quality balance

Migrating to the 4.6 family (Opus 4.6 and Sonnet 4.6)

3. Assistant-turn prefills return 400 (Opus 4.6 and Sonnet 4.6).

Prefilled responses on the final assistant turn are no longer supported on either Opus 4.6 or Sonnet 4.6 — both return a 400. Adding assistant messages *elsewhere* in the conversation (e.g., for few-shot examples) still works. Pick the replacement that matches what the prefill was doing:

# Old (fails on Opus 4.6 / Sonnet 4.6) — prefill forcing JSON shape
messages=[
    {"role": "user", "content": "Extract the name."},
    {"role": "assistant", "content": "{\"name\": \""},
]

# New — structured outputs replace the prefill
response = client.messages.create(
    model="claude-opus-4-6",
    max_tokens=1024,
    output_config={"format": {"type": "json_schema", "schema": {...}}},
    messages=[{"role": "user", "content": "Extract the name."}],
)

4. Stream for max_tokens > ~16K (all models); Opus 4.6 alone reaches 128K.

Non-streaming requests hit SDK HTTP timeouts at high max_tokens, regardless of model — stream for anything above ~16K output. The streamable ceiling differs by model: Sonnet 4.6 and Haiku 4.5 cap at 64K, and Opus 4.6 alone goes up to 128K.

with client.messages.stream(model="claude-opus-4-6", max_tokens=64000, ...) as stream:
    message = stream.get_final_message()

5. Tool-call JSON escaping may differ (Opus 4.6 and Sonnet 4.6).

Both 4.6 models can produce tool call input fields with Unicode or forward-slash escaping. Always parse with json.loads() / JSON.parse() — never raw-string-match the serialized input.

All models

6. output_format → output_config.format (API-wide).

The old top-level output_format parameter on messages.create() is deprecated. Use output_config.format instead. This is not 4.6-specific — applies to every model.

Beta Headers to Remove on 4.6

Several beta headers that were required on 4.5 are now GA on 4.6 and should be removed. Leaving them in is harmless but misleading; removing them also lets you move from client.beta.messages.create(...) back to client.messages.create(...).

Once you remove all of these and finish moving to adaptive thinking, you can switch the SDK call site from the beta namespace back to the regular one:

# Before
response = client.beta.messages.create(
    model="claude-opus-4-5",
    betas=["interleaved-thinking-2025-05-14", "effort-2025-11-24"],
    ...
)

# After
response = client.messages.create(
    model="claude-opus-4-6",
    thinking={"type": "adaptive"},
    output_config={"effort": "high"},
    ...
)

Additional Changes When Coming from 3.x / 4.0 / 4.1 → 4.6

If you're jumping from Opus 4.1, Sonnet 4, Sonnet 3.7, or an older Claude 3.x model directly to 4.6, apply everything above *plus* the items in this section. Users already on Opus 4.5 / Sonnet 4.5 can skip this.

1. Sampling parameters: temperature OR top_p, not both.

Passing both will error on every Claude 4+ model:

# Old (3.x only — errors on 4+)
client.messages.create(temperature=0.7, top_p=0.9, ...)

# New
client.messages.create(temperature=0.7, ...)  # or top_p, not both

2. Update tool versions.

Legacy tool versions are not supported on 4+. Both the type and the name field change — text_editor_20250728 and str_replace_based_edit_tool are a pair; updating one without the other 400s. Also remove the undo_edit command from your text-editor integration:

# Before
tools = [{"type": "text_editor_20250124", "name": "str_replace_editor"}]

# After — BOTH fields change
tools = [{"type": "text_editor_20250728", "name": "str_replace_based_edit_tool"}]

3. Handle the refusal stop reason.

Claude 4+ can return stop_reason: "refusal" on the response. If your code only handles end_turn / tool_use / max_tokens, add a branch:

if response.stop_reason == "refusal":
    # Surface the refusal to the user; do not retry with the same prompt
    ...

4. Handle the model_context_window_exceeded stop reason (4.5+).

Distinct from max_tokens: it means the model hit the *context window* limit, not the requested output cap. Handle both:

if response.stop_reason == "model_context_window_exceeded":
    # Context window exhausted — compact or split the conversation
    ...
elif response.stop_reason == "max_tokens":
    # Requested output cap hit — retry with higher max_tokens or stream
    ...

5. Trailing newlines preserved in tool call string parameters (4.5+).

4.5 and 4.6 preserve trailing newlines that older models stripped. If your tool implementations do exact string matching against tool-call input values (e.g., if name == "foo"), verify they still match when the model sends "foo\n". Normalizing with .rstrip() on the receiving side is usually the simplest fix.

6. Haiku: rate limits reset between generations.

Haiku 4.5 has its own rate-limit pool separate from Haiku 3 / 3.5. If you're ramping traffic as you migrate, check your tier's Haiku 4.5 limits at API rate limits — a quota that comfortably served Haiku 3.5 traffic may need a tier bump for the same volume on 4.5.

Prompt-Behavior Changes (Opus 4.5 / 4.6, Sonnet 4.6)

These don't break your code, but prompts that worked on 4.5-and-earlier may over- or under-trigger on 4.6. Tune as needed.

1. Aggressive instructions cause overtriggering. Opus 4.5 and 4.6 follow the system prompt much more closely than earlier models. Prompts written to *overcome* the old reluctance are now too aggressive:

If the model is now overtriggering a tool or skill, the fix is almost always to dial back the language, not to add more guardrails.

2. Overthinking and excessive exploration (Opus 4.6). At higher effort settings, Opus 4.6 explores more before answering. If that burns too many thinking tokens, lower effort first (medium is often the sweet spot) before adding prose instructions to constrain reasoning.

3. Overeager subagent spawning (Opus 4.6). Opus 4.6 has a strong preference for delegating to subagents. If you see it spawning a subagent for something a direct grep or read would solve, add guidance: *"Use subagents only for parallel or independent workstreams. For single-file reads or sequential operations, work directly."*

4. Overengineering (Opus 4.5 / 4.6). Both models may add extra files, abstractions, or defensive error handling beyond what was asked. If you want minimal changes, prompt for it explicitly: *"Only make changes directly requested. Don't add helpers, abstractions, or error handling for scenarios that can't happen."*

5. LaTeX math output (Opus 4.6). Opus 4.6 defaults to LaTeX (\frac{}{}, $...$) for math and technical content. If you need plain text, instruct it explicitly: *"Format all math as plain text — no LaTeX, no $, no \frac{}{}. Use / for division and ^ for exponents."*

6. Skipped verbal summaries (4.6 family). The 4.6 models are more concise and may skip the summary paragraph after a tool call, jumping straight to the next action. If you rely on those summaries for visibility, add: *"After completing a task that involves tool use, provide a brief summary of what you did."*

7. "Think" as a trigger word (Opus 4.5 with thinking disabled). When thinking is off, Opus 4.5 is particularly sensitive to the word *think* and may reason more than you want. Use consider, evaluate, or reason through instead.

Model-ID Rename Quick Reference

Older aliases (claude-opus-4-5, claude-sonnet-4-5, claude-opus-4-1, etc.) are still active and can be pinned if you need time before upgrading — see shared/models.md for the full legacy list.

Migration Checklist

Every item is tagged: [BLOCKS] items cause a 400 error, infinite loop, silent timeout, or wrong tool selection if missed — apply these as code edits, not as suggestions. [TUNE] items are quality/cost adjustments.

For each file that calls messages.create() / equivalent SDK method:

• [ ] [BLOCKS] Update the model= string to the new alias
• [ ] [BLOCKS] Replace budget_tokens with thinking={"type": "adaptive"} (deprecated on Opus 4.6 / Sonnet 4.6)
• [ ] [BLOCKS] Move format from top-level output_format into output_config.format
• [ ] [BLOCKS] Remove any assistant-turn prefills if targeting Opus 4.6 or Sonnet 4.6 (see the prefill replacement table)
• [ ] [BLOCKS] Switch to streaming if max_tokens > ~16000 (otherwise SDK HTTP timeout)
• [ ] [TUNE] Set output_config={"effort": "..."} explicitly — especially when moving Sonnet 4.5 → Sonnet 4.6 (4.6 defaults to high)
• [ ] [TUNE] Remove GA beta headers: effort-2025-11-24, fine-grained-tool-streaming-2025-05-14, token-efficient-tools-2025-02-19, output-128k-2025-02-19; remove interleaved-thinking-2025-05-14 once on adaptive thinking
• [ ] [TUNE] Switch client.beta.messages.create(...) → client.messages.create(...) once all betas are removed
• [ ] [TUNE] Review system prompt for aggressive tool language (CRITICAL:, MUST, If in doubt) and dial it back

Extra items when coming from 3.x / 4.0 / 4.1:

• [ ] [BLOCKS] Remove either temperature or top_p (passing both 400s on Claude 4+)
• [ ] [BLOCKS] Update text-editor tool type to text_editor_20250728
• [ ] [BLOCKS] Update text-editor tool name to str_replace_based_edit_tool — changing only the type and keeping name: "str_replace_editor" returns a 400
• [ ] [BLOCKS] Update code-execution tool to code_execution_20250825
• [ ] [BLOCKS] Delete any undo_edit command call sites
• [ ] [TUNE] Add handling for stop_reason == "refusal"
• [ ] [TUNE] Add handling for stop_reason == "model_context_window_exceeded" (4.5+)
• [ ] [TUNE] Verify tool-param string matching tolerates trailing newlines (preserved on 4.5+)
• [ ] [TUNE] If moving to Haiku 4.5: review rate-limit tier (separate pool from Haiku 3.x)

Verification:

• [ ] Run one test request and inspect response.stop_reason, response.usage, and whether tool-use / thinking behavior matches expectations

For cached prompts: the render order and hash inputs did not change, so existing cache_control breakpoints keep working. However, changing the model string invalidates the existing cache — the first request on the new model will write the cache fresh.

Migrating to Opus 4.7

Model ID claude-opus-4-7 is authoritative as written here. When the user asks to migrate to Opus 4.7, write model="claude-opus-4-7" exactly. Do not WebFetch to verify — this guide is the source of truth for migration target IDs. The corresponding entry exists in shared/models.md.

Claude Opus 4.7 is our most capable generally available model to date. It is highly autonomous and performs exceptionally well on long-horizon agentic work, knowledge work, vision tasks, and memory tasks. This section summarizes everything new at launch. It is layered on top of the 4.6 migration above — if the caller is jumping from Opus 4.5 or older, apply the 4.6 changes first, then apply this section.

TL;DR for someone already on Opus 4.6: update the model ID to claude-opus-4-7, strip any remaining budget_tokens and sampling parameters (both 400 on Opus 4.7), give max_tokens extra headroom and re-baseline with count_tokens() against the new model, opt back into thinking.display: "summarized" if reasoning is surfaced to users, and re-tune effort — it matters more on 4.7 than on any prior Opus.

Breaking changes (will 400 on Opus 4.7)

Extended thinking removed.

thinking: {type: "enabled", budget_tokens: N} is no longer supported on Claude Opus 4.7 or later models and returns a 400 error. Switch to adaptive thinking (thinking: {type: "adaptive"}) and use the effort parameter to control thinking depth. Adaptive thinking is off by default on Claude Opus 4.7: requests with no thinking field run without thinking, matching Opus 4.6 behavior. Set thinking: {type: "adaptive"} explicitly to enable it.

# Before (Opus 4.6)
client.messages.create(
    model="claude-opus-4-6",
    max_tokens=64000,
    thinking={"type": "enabled", "budget_tokens": 32000},
    messages=[{"role": "user", "content": "..."}],
)

# After (Opus 4.7)
client.messages.create(
    model="claude-opus-4-7",
    max_tokens=64000,
    thinking={"type": "adaptive"},
    output_config={"effort": "high"},  # or "max", "xhigh", "medium", "low"
    messages=[{"role": "user", "content": "..."}],
)

If the caller wasn't using extended thinking, no change is required — thinking is off by default, or can be set explicitly with thinking={"type": "disabled"}.

Delete budget_tokens plumbing entirely. For the replacement effort value, see Choosing an effort level on Opus 4.7 below — there is no exact 1:1 mapping from budget_tokens.

Sampling parameters removed.

The temperature, top_p, and top_k parameters are no longer accepted on Claude Opus 4.7. Requests that include them return a 400 error. Remove these fields from your request payloads. Prompting is the recommended way to guide model behavior on Claude Opus 4.7. If you were using temperature = 0 for determinism, note that it never guaranteed identical outputs on prior models.

# Before — errors on Opus 4.7
client.messages.create(temperature=0.7, top_p=0.9, ...)

# After
client.messages.create(...)  # no sampling params

• If the intent was determinism — use effort: "low" with a tighter prompt.
• If the intent was creative variance — the prompt replacement depends on the use case; ask the user how they want variance elicited. If you can't ask, add a use-case-appropriate instruction along the lines of *"choose something off-distribution and interesting"* — e.g. for text generation, *"Vary your phrasing and structure across responses"*; for frontend/design, use the propose-4-directions approach under Design and frontend coding below.

Choosing an effort level on Opus 4.7

budget_tokens controlled how much to *think*; effort controls how much to think *and* act, so there is no exact 1:1 mapping. Use xhigh for best results in coding and agentic use cases, and a minimum of high for most intelligence-sensitive use cases. Experiment with other levels to further tune token usage and intelligence:

Silent default changes (no error, but behavior differs)

Thinking content omitted by default.

Thinking blocks still appear in the response stream on Claude Opus 4.7, but their thinking field is empty unless you explicitly opt in. This is a silent change from Claude Opus 4.6, where the default was to return summarized thinking text. To restore summarized thinking content on Claude Opus 4.7, set thinking.display to "summarized". The block-field name is unchanged — it is still block.thinking on a thinking-type block; do not rename it.

Detect this: any code that reads block.thinking (or equivalent) from a thinking-type block and renders it in a UI, log, or trace. The fix is the request parameter, not the response handling — add display: "summarized" to the thinking parameter:

thinking={"type": "adaptive", "display": "summarized"}  # "display" is new on Opus 4.7; values: "omitted" (default) | "summarized"

The default is "omitted" on Claude Opus 4.7. If thinking content was never surfaced anywhere, no change needed. If your product streams reasoning to users, the new default appears as a long pause before output begins; set display: "summarized" to restore visible progress during thinking.

Updated token counting.

Claude Opus 4.7 and Claude Opus 4.6 count tokens differently. The same input text produces a higher token count on Claude Opus 4.7 than on Claude Opus 4.6, and /v1/messages/count_tokens will return a different number of tokens for Claude Opus 4.7 than it did for Claude Opus 4.6. The token efficiency of Claude Opus 4.7 can vary by workload shape. Prompting interventions, task_budget, and effort can help control costs and ensure appropriate token usage. Keep in mind that these controls may trade off model intelligence. Update your max_tokens parameters to give additional headroom, including compaction triggers. Claude Opus 4.7 provides a 1M context window at standard API pricing with no long-context premium.

What else to check:

• Client-side token estimators (tiktoken-style approximations) calibrated against 4.6
• Cost calculators that multiply tokens by a fixed per-token rate
• Rate-limit retry thresholds keyed to measured token counts

Re-baseline by re-running client.messages.count_tokens() against claude-opus-4-7 on a representative sample of the caller's prompts. Do not apply a blanket multiplier. For cost-sensitive workloads, consider reducing effort by one level (e.g. high → medium). For agentic loops, consider adopting Task Budgets (below).

New feature: Task Budgets (beta)

Opus 4.7 introduces task budgets — tell Claude how many tokens it has for a full agentic loop (thinking + tool calls + final output). The model sees a running countdown and uses it to prioritize work and wrap up gracefully as the budget is consumed.

This is a suggestion the model is aware of, not a hard cap. It is distinct from max_tokens, which remains the enforced per-response limit and is *not* surfaced to the model. Use task_budget when you want the model to self-moderate; use max_tokens as a hard ceiling to cap usage.

Requires beta header task-budgets-2026-03-13:

client.beta.messages.create(
    betas=["task-budgets-2026-03-13"],
    model="claude-opus-4-7",
    max_tokens=64000,
    thinking={"type": "adaptive"},
    output_config={
        "effort": "high",
        "task_budget": {"type": "tokens", "total": 128000},
    },
    messages=[...],
)

Set a generous budget for open-ended agentic tasks and tighten it for latency-sensitive ones. Minimum task_budget.total is 20,000 tokens. If the budget is too restrictive for the task, the model may complete it less thoroughly, referencing its budget as the constraint. Do not add task_budget during a migration unless you are sure the budget value is right — if you can run the workload and measure, do so; otherwise ask the user for the value rather than guessing. This is the primary lever for offsetting the token-counting shift on agentic workloads.

Capability improvements

High-resolution vision. Opus 4.7 is the first Claude model with high-resolution image support. Maximum image resolution is 2576 pixels on the long edge (up from 1568px on Opus 4.6 and prior). This unlocks gains on vision-heavy workloads, especially computer use and screenshot/artifact/document understanding. Coordinates returned by the model now map 1:1 to actual image pixels, so no scale-factor math is needed.

High-res support is automatic on Opus 4.7 — no beta header, no client-side opt-in required. The model accepts larger inputs and returns pixel-accurate coordinates out of the box.

Token cost. Full-resolution images on Opus 4.7 can use up to ~3× more image tokens than on prior models (up to ~4784 tokens per image, vs. the previous ~1,600-token cap). If the extra fidelity isn't needed, downsample client-side before sending to control cost — but do not add downsampling by default during a migration. If you're not sure whether the pipeline needs the fidelity, ask the user rather than guessing. Use count_tokens() on representative images on Opus 4.7 to re-baseline before reacting to any measured cost shift.

Beyond resolution, Opus 4.7 also improves on low-level perception (pointing, measuring, counting) and natural-image bounding-box localization and detection.

Knowledge work. Meaningful gains on tasks where the model visually verifies its own output — .docx redlining, .pptx editing, and programmatic chart/figure analysis (e.g. pixel-level data transcription via image-processing libraries). If prompts have scaffolding like *"double-check the slide layout before returning"*, try removing it and re-baselining.

Memory. Opus 4.7 is better at writing and using file-system-based memory. If an agent maintains a scratchpad, notes file, or structured memory store across turns, that agent should improve at jotting down notes to itself and leveraging its notes in future tasks.

User-facing progress updates. Opus 4.7 provides more regular, higher-quality interim updates during long agentic traces. If the system prompt has scaffolding like *"After every 3 tool calls, summarize progress"*, try removing it to avoid excessive user-facing text. If the length or contents of Opus 4.7's updates are not well-calibrated to your use case, explicitly describe what these updates should look like in the prompt and provide examples.

Real-time cybersecurity safeguards

Requests that involve prohibited or high-risk topics may lead to refusals.

Fast Mode: not available on Opus 4.7

Opus 4.7 does not have a Fast Mode variant. Opus 4.6 Fast remains supported. Only surface this if the caller's code actually uses a Fast Mode model string (e.g. claude-opus-4-6-fast); if the word "fast" does not appear in the code, say nothing about Fast Mode.

When you see model="claude-opus-4-6-fast" (or similar), the migration edit is:

# Opus 4.7 has no Fast Mode — keeping on 4.6 Fast (caller's choice to switch to standard Opus 4.7).
model="claude-opus-4-6-fast",

That is: leave the model string unchanged, add the comment above it, and tell the user their two options — (a) stay on Opus 4.6 Fast, which remains supported, or (b) move latency-tolerant traffic to standard Opus 4.7 for the intelligence gain. Do not rewrite the model string to claude-opus-4-7 yourself; that silently trades latency for intelligence, which is the caller's decision.

Behavioral shifts (prompt-tunable)

These don't break anything, but prompts tuned for Opus 4.6 may land differently. Opus 4.7 is more steerable than 4.6, so small prompt nudges usually close the gap.

More literal instruction following. Claude Opus 4.7 interprets prompts more literally and explicitly than Claude Opus 4.6, particularly at lower effort levels. It will not silently generalize an instruction from one item to another, and it will not infer requests you didn't make. The upside of this literalism is precision and less thrash. It generally performs better for API use cases with carefully tuned prompts, structured extraction, and pipelines where you want predictable behavior. A prompt and harness review may be especially helpful for migration to Claude Opus 4.7.

Verbosity calibrates to task complexity. Opus 4.7 scales response length to how complex it judges the task to be, rather than defaulting to a fixed verbosity — shorter answers on simple lookups, much longer on open-ended analysis. If the product depends on a particular length or style, tune the prompt explicitly. To reduce verbosity:

*"Provide concise, focused responses. Skip non-essential context, and keep examples minimal."*

If you see specific kinds of over-verbosity (e.g. over-explaining), add instructions targeting those. Positive examples showing the desired level of concision tend to be more effective than negative examples or instructions telling the model what not to do. Do not assume existing "be concise" instructions should be removed — test first.

Tone and writing style. Opus 4.7 is more direct and opinionated, with less validation-forward phrasing and fewer emoji than Opus 4.6's warmer style. As with any new model, prose style on long-form writing may shift. If the product relies on a specific voice, re-evaluate style prompts against the new baseline. If a warmer or more conversational voice is wanted, specify it:

*"Use a warm, collaborative tone. Acknowledge the user's framing before answering."*

effort matters more than on any prior Opus. Opus 4.7 respects effort levels more strictly, especially at the low end. At low and medium it scopes work to what was asked rather than going above and beyond — good for latency and cost, but on moderate tasks at low there is some risk of under-thinking.

• If shallow reasoning shows up on complex problems, raise effort to high or xhigh rather than prompting around it.
• If effort must stay low for latency, add targeted guidance: *"This task involves multi-step reasoning. Think carefully through the problem before responding."*
• At xhigh or max, set a large max_tokens so the model has room to think and act across tool calls and subagents. Start at 64K and tune from there. (xhigh is a new effort level on Opus 4.7, between high and max.)

Adaptive-thinking triggering is also steerable. If the model thinks more often than wanted — which can happen with large or complex system prompts — add: *"Thinking adds latency and should only be used when it will meaningfully improve answer quality — typically for problems that require multi-step reasoning. When in doubt, respond directly."*

Uses tools less often by default. Opus 4.7 tends to use tools less often than 4.6 and to use reasoning more. This produces better results in most cases, but for products that rely on tools (search/retrieval, function-calling, computer-use steps), it can drop tool-use rate. Two levers:

• Raise effort — high or xhigh show substantially more tool usage in agentic search and coding, and are especially useful for knowledge work.
• Prompt for it — be explicit in tool descriptions or the system prompt about when and how to use the tool, and encourage the model to err on the side of using it more often:

*"When the answer depends on information not present in the conversation, you MUST call the search tool before answering — do not answer from prior knowledge."*

Fewer subagents by default. Opus 4.7 tends to spawn fewer subagents than 4.6. This is steerable — give explicit guidance on when delegation is desirable. For a coding agent, for example:

*"Do NOT spawn a subagent for work you can complete directly in a single response (e.g. refactoring a function you can already see). Spawn multiple subagents in the same turn when fanning out across items or reading multiple files."*

Design and frontend coding. Opus 4.7 has stronger design instincts than 4.6, with a consistent default house style: warm cream/off-white backgrounds (around #F4F1EA), serif display type (Georgia, Fraunces, Playfair), italic word-accents, and a terracotta/amber accent. This reads well for editorial, hospitality, and portfolio briefs, but will feel off for dashboards, dev tools, fintech, healthcare, or enterprise apps — and it appears in slide decks as well as web UIs.

The default is persistent. Generic instructions ("don't use cream," "make it clean and minimal") tend to shift the model to a different fixed palette rather than producing variety. Two approaches work reliably:

1. Specify a concrete alternative. The model follows explicit specs precisely — give exact hex values, typefaces, and layout constraints.
2. Have the model propose options before building. This breaks the default and gives the user control:

> *"Before building, propose 4 distinct visual directions tailored to this brief (each as: bg hex / accent hex / typeface — one-line rationale). Ask the user to pick one, then implement only that direction."*

If the caller previously relied on temperature for design variety, use approach (2) — it produces meaningfully different directions across runs.

Opus 4.7 also requires less frontend-design prompting than previous models to avoid generic "AI slop" aesthetics. Where earlier models needed a lengthy anti-slop snippet, Opus 4.7 generates distinctive, creative frontends with a much shorter nudge. This snippet works well alongside the variety approaches above:

*"NEVER use generic AI-generated aesthetics like overused font families (Inter, Roboto, Arial, system fonts), cliched color schemes (particularly purple gradients on white or dark backgrounds), predictable layouts and component patterns, and cookie-cutter design that lacks context-specific character. Use unique fonts, cohesive colors and themes, and animations for effects and micro-interactions."*

Interactive coding products. Opus 4.7's token usage and behavior can differ between autonomous, asynchronous coding agents with a single user turn and interactive, synchronous coding agents with multiple user turns. Specifically, it tends to use more tokens in interactive settings, primarily because it reasons more after user turns. This can improve long-horizon coherence, instruction following, and coding capabilities in long interactive coding sessions, but also comes with more token usage. To maximize both performance and token efficiency in coding products, use effort: "xhigh" or "high", add autonomous features (like an auto mode), and reduce the number of human interactions required from users.

When limiting required user interactions, specify the task, intent, and relevant constraints upfront in the first human turn. Well-specified, clear, and accurate task descriptions upfront help maximize autonomy and intelligence while minimizing extra token usage after user turns — because Opus 4.7 is more autonomous than prior models, this usage pattern helps to maximize performance. In contrast, ambiguous or underspecified prompts conveyed progressively over multiple user turns tend to reduce token efficiency and sometimes performance.

Code review. Opus 4.7 is meaningfully better at finding bugs than prior models, with both higher recall and precision. However, if a code-review harness was tuned for an earlier model, it may initially show *lower* recall — this is likely a harness effect, not a capability regression. When a review prompt says "only report high-severity issues," "be conservative," or "don't nitpick," Opus 4.7 follows that instruction more faithfully than earlier models did: it investigates just as thoroughly, identifies the bugs, and then declines to report findings it judges to be below the stated bar. Precision rises, but measured recall can fall even though underlying bug-finding has improved.

Recommended prompt language:

*"Report every issue you find, including ones you are uncertain about or consider low-severity. Do not filter for importance or confidence at this stage — a separate verification step will do that. Your goal here is coverage: it is better to surface a finding that later gets filtered out than to silently drop a bug. For each finding, include your confidence level and an estimated severity so a downstream filter can rank them."*

This can be used without an actual second step, but moving confidence filtering out of the finding step often helps. If the harness has a separate verification/dedup/ranking stage, tell the model explicitly that its job at the finding stage is coverage, not filtering. If single-pass self-filtering is wanted, be concrete about the bar rather than using qualitative terms like "important" — e.g. *"report any bugs that could cause incorrect behavior, a test failure, or a misleading result; only omit nits like pure style or naming preferences."* Iterate on prompts against a subset of evals to validate recall or F1 gains.

Computer use. Computer use works across resolutions up to the new 2576px / 3.75MP maximum. Sending images at 1080p provides a good balance of performance and cost. For particularly cost-sensitive workloads, 720p or 1366×768 are lower-cost options with strong performance. Test to find the ideal settings for the use case; experimenting with effort can also help tune behavior.

Opus 4.7 Migration Checklist

Every item is tagged: [BLOCKS] items cause a 400 error, infinite loop, silent truncation, or empty output if missed — apply these as code edits, not as suggestions. [TUNE] items are quality/cost adjustments — surface them to the user as recommendations.

[BLOCKS] items prefixed with "If…" or "At…" are conditional. Before working through the list, scan the file for the conditions: does it surface thinking text to a UI/log? Does it set output_config.effort to "x-high" or "max"? Is it a security workload? Is it a multi-turn agentic loop? Apply only the items whose condition matches.

• [ ] [BLOCKS] Replace thinking: {type: "enabled", budget_tokens: N} with thinking: {type: "adaptive"} + output_config.effort; delete budget_tokens plumbing entirely
• [ ] [BLOCKS] Strip temperature, top_p, top_k from request construction
• [ ] [BLOCKS] If thinking content is surfaced to users or stored in logs: add thinking.display: "summarized" (otherwise the rendered text is empty)
• [ ] [BLOCKS] At output_config.effort of xhigh or max: set max_tokens ≥ 64000 (otherwise output truncates mid-thought)
• [ ] [TUNE] Give max_tokens and compaction triggers extra headroom; re-run count_tokens() against claude-opus-4-7 on representative prompts to re-baseline (no blanket multiplier)
• [ ] [TUNE] Re-baseline cost and rate-limit dashboards *before* reacting to measured shifts
• [ ] [TUNE] Re-evaluate effort per route — use xhigh for coding/agentic and a minimum of high for most intelligence-sensitive work; it matters more on 4.7 than any prior Opus
• [ ] [TUNE] Multi-turn agentic loops: adopt the API-native Task Budgets (output_config.task_budget, beta task-budgets-2026-03-13, minimum 20k tokens) — this is for capping *cumulative* spend across a loop; per-turn depth is effort
• [ ] [TUNE] Check for ambiguous or underspecified instructions that relied on 4.6 generalizing intent, and update them to be clearer or more precise — 4.7 follows them literally
• [ ] [TUNE] Tool-use workloads: add explicit when/how-to-use guidance to tool descriptions (4.7 reaches for tools less often)
• [ ] [TUNE] Verbosity: test existing length instructions before changing them — 4.7 calibrates length to task complexity, so tune for the desired output rather than assuming a direction
• [ ] [TUNE] Remove forced-progress-update scaffolding (*"after every N tool calls…"*)
• [ ] [TUNE] Remove knowledge-work verification scaffolding (*"double-check the slide layout…"*) and re-baseline
• [ ] [TUNE] Add tone instruction if a warmer / more conversational voice is needed; re-evaluate style prompts on writing-heavy routes
• [ ] [TUNE] Subagent tool present: add explicit spawn / don't-spawn guidance
• [ ] [TUNE] Frontend/design output: specify a concrete palette/typeface, or have the model propose 4 visual directions before building (the default cream/serif house style is persistent)
• [ ] [TUNE] Interactive coding products: use effort: "xhigh" or "high", add autonomous features (e.g. an auto mode) to reduce human interactions, and specify task/intent/constraints upfront in the first turn
• [ ] [TUNE] Code-review harnesses: remove or loosen "only report high-severity" / "be conservative" filters and have the model report every finding with confidence + severity; move filtering to a downstream step (4.7 follows severity filters more literally, which can depress measured recall)
• [ ] [TUNE] Vision-heavy pipelines (screenshots, charts, document understanding): leave images at native resolution up to 2576px long edge for the accuracy gain; remove any scale-factor math from coordinate handling (coords are now 1:1 with pixels). No beta header / opt-in needed — high-res is automatic on Opus 4.7.
• [ ] [TUNE] Computer-use pipelines: send screenshots at 1080p for a good performance/cost balance (720p or 1366×768 for cost-sensitive workloads); experiment with effort to tune behavior
• [ ] [TUNE] Cost-sensitive image pipelines: full-res images on 4.7 use up to ~4784 tokens vs ~1,600 on prior models (~3×). Downsampling client-side before upload avoids the increase, but do not downsample by default — if you're unsure whether fidelity is needed, ask the user. Re-baseline with count_tokens() on representative images before reacting to cost shifts.

Verify the Migration

After updating, spot-check that the new model is actually being used. Replace YOUR_TARGET_MODEL with the model string you migrated to (e.g. claude-opus-4-7, claude-opus-4-6, claude-sonnet-4-6, claude-haiku-4-5) and keep the assertion prefix in sync:

YOUR_TARGET_MODEL = "claude-opus-4-7"  # or "claude-opus-4-6", "claude-sonnet-4-6", "claude-haiku-4-5"
response = client.messages.create(model=YOUR_TARGET_MODEL, max_tokens=64, messages=[...])
assert response.model.startswith(YOUR_TARGET_MODEL), response.model

For rate-limit headroom changes, pricing, or capability deltas (vision, structured outputs, effort support), query the Models API:

m = client.models.retrieve(YOUR_TARGET_MODEL)
m.max_input_tokens, m.max_tokens
m.capabilities["effort"]["max"]["supported"]

See shared/models.md for the full capability lookup pattern.