What's new in Claude Opus 4.7

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 page summarizes everything new at launch.

New model

Claude Opus 4.7 supports the 1M token context window, 128k max output tokens, adaptive thinking, and the same set of tools and platform features as Claude Opus 4.6.

For complete pricing and specs, see the models overview.

New features

High-resolution image support

Claude Opus 4.7 is our first Claude model with high-resolution image support. Maximum image resolution has increased to 2576px / 3.75MP (increased from our previous limit of 1568px / 1.15MP). This change should unlock performance gains on vision-heavy workloads, and is particularly important for computer use and screenshot/artifact/document understanding workflows.

Additionally, operations like mapping coordinates to images are now simpler — the model's coordinates are 1:1 with actual pixels, so there's no scale-factor math required.

High-res images use more tokens. If the additional image fidelity is unnecessary, downsample images before sending to Claude to avoid token-usage increases.

Beyond resolution, Claude Opus 4.7 also improves on:

• Low-level perception — pointing, measuring, counting, and similar tasks.
• Image localization — natural-image bounding-box localization and detection are improved.

See Images and vision for details.

New xhigh effort level

The effort parameter allows you to tune Claude's intelligence vs. token spend, trading off capability for faster speed and lower costs. Start with the new xhigh effort level for coding and agentic use cases, and use a minimum of high effort for most intelligence-sensitive use cases. See Recommended effort levels for Claude Opus 4.7 for per-level guidance. (Messages API only; Claude Managed Agents handles effort automatically.)

Task budgets (beta)

Claude Opus 4.7 introduces task budgets. A task budget gives Claude a rough estimate of how many tokens to target for a full agentic loop, including thinking, tool calls, tool results, and final output. The model sees a running countdown and uses it to prioritize work and finish the task gracefully as the budget is consumed. To use, set the beta header task-budgets-2026-03-13 and add the following to your output config:

response = client.beta.messages.create(
    model="claude-opus-4-7",
    max_tokens=128000,
    output_config={
        "effort": "high",
        "task_budget": {"type": "tokens", "total": 128000},
    },
    messages=[
        {"role": "user", "content": "Review the codebase and propose a refactor plan."}
    ],
    betas=["task-budgets-2026-03-13"],
)

You may need to experiment with different task budgets for your use case. If the model is given a task budget that is too restrictive for a given task, it may complete the task less thoroughly or refuse to do the task entirely.

For open-ended agentic tasks where quality matters more than speed, do not set a task budget; reserve task budgets for workloads where you need the model to scope its work to a token allowance. The minimum value for a task budget is 20k tokens.

This is not a hard cap; it's a suggestion that the model is aware of. This is distinct from max_tokens, which is a hard per-request cap on generated tokens (max_tokens is not passed to the model, and the model is not aware of it), while task_budget is an advisory cap across the full agentic loop. Use task_budget when you want the model to self-moderate, and max_tokens as a hard per-request ceiling to cap usage.

Breaking changes

Extended thinking budgets removed

Extended thinking budgets are removed in Claude Opus 4.7. Setting thinking: {"type": "enabled", "budget_tokens": N} will return a 400 error. Adaptive thinking is the only thinking-on mode, and in our internal evaluations it reliably outperforms extended thinking.

# Before (Opus 4.6)
thinking = {"type": "enabled", "budget_tokens": 32000}

# After (Opus 4.7)
thinking = {"type": "adaptive"}
output_config = {"effort": "high"}

Adaptive thinking is off by default on Claude Opus 4.7. Requests with no thinking field run without thinking. Set thinking: {type: "adaptive"} explicitly to enable it.

Sampling parameters removed

Starting with Claude Opus 4.7, setting temperature, top_p, or top_k to any non-default value will return a 400 error. The safest migration path is to omit these parameters entirely from requests, and to use prompting to guide the model's behavior. If you were using temperature = 0 for determinism, note that it never guaranteed identical outputs.

Thinking content omitted by default

Starting with Claude Opus 4.7, thinking content is omitted from the response by default. Thinking blocks still appear in the response stream, but their thinking field will be empty unless the caller explicitly opts in. This is a silent change — no error is raised — and response latency will be slightly improved. If reasoning outputs are needed, you can set display to "summarized" and opt back in with a one-line change:

thinking = {
    "type": "adaptive",
    "display": "summarized",  # or "omitted" (default)
}

Updated token counting

Claude Opus 4.7 uses a new tokenizer, contributing to its improved performance on a wide range of tasks. This new tokenizer may use roughly 1x to 1.35x as many tokens when processing text compared to previous models (up to ~35% more, varying by content), 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.

We suggest updating 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.

Capability improvements

Knowledge work

Claude Opus 4.7 shows meaningful gains on knowledge-worker tasks, particularly where the model needs to visually verify its own outputs:

• .docx redlining and .pptx editing — improved at producing and self-checking tracked changes and slide layouts.
• Charts and figure analysis — improved at programmatic tool-calling with image-processing libraries (e.g. PIL) to analyze charts and figures, including pixel-level data transcription.

If existing prompts have mitigations in these areas (e.g. "double-check the slide layout before returning"), try removing that scaffolding and re-baselining.

Memory

Claude 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. To give Claude a managed scratchpad without building your own, use the client-side memory tool.

Vision

See High-resolution image support above.

Behavior changes

These are not API breaking changes but may require prompt updates. See Migrating to Claude Opus 4.7 for full guidance.

• More literal instruction following, particularly at lower effort levels. The model will not silently generalize an instruction from one item to another, and will not infer requests you didn't make.
• Response length calibrates to perceived task complexity rather than defaulting to a fixed verbosity.
• Fewer tool calls by default, using reasoning more. Raising effort increases tool usage.
• More direct, opinionated tone with less validation-forward phrasing and fewer emoji than Claude Opus 4.6's warmer style.
• More regular progress updates to the user throughout long agentic traces. If you've added scaffolding to force interim status messages, try removing it.
• Fewer subagents spawned by default. Steerable through prompting.
• Real-time cybersecurity safeguards: requests that involve prohibited or high-risk topics may lead to refusals. For legitimate security work, apply to the Cyber Verification Program.

Migration guide

For step-by-step migration instructions and the full migration checklist, see Migrating to Claude Opus 4.7. If you use Claude Code or the Agent SDK, the Claude API skill can apply these migration steps to your codebase automatically.

Next steps