Claude Opus 4.7: Anthropic's Most Autonomous Flagship Yet

Anthropic has released Claude Opus 4.7, the newest version of its flagship model and—by the numbers in the announcement—its most autonomous one yet. The headline improvements are concentrated where teams actually feel them: long-running coding work, vision at real-world resolutions, instruction following, and resistance to prompt injection. Opus 4.7 ships at the same price as Opus 4.6 ($5 input / $25 output per million tokens), so this is a strict capability uplift for existing Opus users.

The release also brings a new xhigh effort level, a public beta of Task Budgets, and a /ultrareview slash command in Claude Code. Opus 4.7 is generally available today across Claude, the Claude API, Amazon Bedrock, Google Cloud Vertex AI, and Microsoft Foundry under the model ID claude-opus-4-7.

What’s new in Opus 4.7

Software engineering that finishes the job

Anthropic is framing Opus 4.7 as the first Opus you can comfortably point at a long, messy task and walk away from. The numbers back that framing:

  • 13% improvement on coding benchmarks versus Opus 4.6
  • 3x more production tasks resolved than its predecessor
  • 21% fewer errors on document reasoning
  • 98.5% vision accuracy on autonomous tasks, up from 54.3%

The behavioral change matters as much as the benchmarks. Opus 4.7 verifies its own outputs before reporting completion, writes fewer wrapper functions, and self-corrects more aggressively during planning. Early partners describe it as a “step change” in visual acuity for autonomous agents and the “best model in the world for building dashboards.”

Vision at real resolutions

The multimodal pipeline now accepts images up to 2,576 pixels on the long edge—about 3.75 megapixels, over 3x the previous capacity. That’s the difference between “a screenshot your agent can kind of see” and “a full design mock or technical diagram your agent can actually read.” Chemical structures, schematics, and complex dashboards now pass through without punishing downscaling.

xhigh effort level

Opus 4.7 adds an “extra high” tier on top of the existing low/medium/high/max effort controls. xhigh trades more latency for deeper reasoning and is aimed at the long-tail of genuinely hard problems—architecture decisions, subtle concurrency bugs, multi-hop research—where you’d rather wait than re-run.

Task Budgets (public beta)

Task Budgets is a new beta feature that lets you guide how Opus 4.7 spends tokens across a multi-step operation. Instead of tuning a single budget_tokens per request, you hand the model a budget for the whole task and let it allocate across planning, tool calls, and verification. This is a practical lever for agentic workloads where a single task can span dozens of turns.

/ultrareview in Claude Code

Claude Code gains a dedicated /ultrareview slash command—a code-review session that uses extra reasoning budget to hunt for the bugs a normal review misses. Pro and Max subscribers get three free ultrareviews, and auto mode is now extended to all Max users.

Instruction following is more literal

One migration note worth flagging: Opus 4.7 follows instructions more literally than Opus 4.6. That’s a win for deterministic agent harnesses, but it means prompts that relied on the model “helpfully interpreting” vague instructions may now do exactly what they say. If you’re upgrading a production system, reassess your prompts and system messages before swapping model IDs.

The tokenizer has also been updated. Expect the same input to map to 1.0–1.35x more tokens depending on content. Anthropic reports net token usage was still favorable in their testing, but it’s worth budgeting for.

Safety and alignment

Opus 4.7 holds roughly the same safety baseline as Opus 4.6 with measurable improvements in honesty, resistance to prompt injection, and lower rates of deception and sycophancy. Anthropic describes the model as “largely well-aligned and trustworthy.” The Mythos Preview model remains the most-aligned model in Anthropic’s lineup.

Under Project Glasswing, cybersecurity capabilities are intentionally reduced relative to Mythos Preview, with automated safeguards that detect and block high-risk cyber requests. Legitimate security professionals can apply to the Cyber Verification Program for appropriate access.

Pricing and availability

Pricing (unchanged from Opus 4.6):

  • Input: $5 per million tokens
  • Output: $25 per million tokens

Availability:

  • Claude.ai and the Claude API as claude-opus-4-7
  • Amazon Bedrock
  • Google Cloud Vertex AI
  • Microsoft Foundry

Getting started

API

from anthropic import Anthropic

client = Anthropic()

response = client.messages.create(
    model="claude-opus-4-7",
    max_tokens=8192,
    messages=[{
        "role": "user",
        "content": "Audit this service for race conditions and fix them."
    }]
)

Using the xhigh effort level

response = client.messages.create(
    model="claude-opus-4-7",
    max_tokens=16384,
    temperature=1,
    thinking={
        "type": "enabled",
        "effort": "xhigh"
    },
    messages=[{
        "role": "user",
        "content": "Propose a migration plan from our monolith to services, "
                   "with a dependency graph and rollout order."
    }]
)

Claude Code

# Update to the latest Claude Code
npm install -g @anthropic-ai/claude-code

# Launch with Opus 4.7
claude --model claude-opus-4-7

# Inside a session:
# /ultrareview   — run a deep code review pass

Should you upgrade?

For anyone already on Opus 4.6, the answer is boringly simple: yes. Same price, better coding, meaningfully better vision, stricter instruction following, and new controls (xhigh, Task Budgets) that are useful the moment you turn them on. The one thing to watch is the tokenizer change and the more literal instruction-following behavior—smoke-test your agent harnesses against claude-opus-4-7 before flipping production traffic.

For teams still evaluating frontier models, Opus 4.7 pushes the bar on the two things that matter most for real work: completing long tasks without hand-holding and seeing the inputs you actually have. The “3x more production tasks resolved” claim is the one to internalize—benchmarks tell you how smart a model is, but that number tells you how often it finishes.

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