Claude Opus 4.7 Review: Benchmarks, Pricing, GPT-5 Comparison

What exactly changed from Claude Opus 4.6 to 4.7?

Anthropic released Opus 4.7 roughly five months after Opus 4.6. The version number looks like a minor 0.1 update, but the benchmark profile and API changes make it feel closer to a generation shift for coding agents. The tradeoff is that long-context retrieval and creative-writing quality have visible regressions. This post puts the release notes, benchmarks, pricing, competing models, and limitations into one practical decision guide.

Official release on April 16, 2026

Anthropic announced Opus 4.7 on Thursday, April 16, 2026 (source: Anthropic News). Claude.ai and the API opened the same day, with AWS Bedrock, Vertex AI, Microsoft Foundry, and Snowflake Cortex following within about 48 hours (source: Help Net Security). The internal code name is widely reported as Neptune v2, and the visible improvements seem driven more by post-training than raw training-token scale. The model keeps an ASL-3 safety posture with additional mitigations for bio, chemical, and cyber risks.

GitHub Copilot also enabled Opus 4.7 for Pro+, Business, and Enterprise users at launch. That means Cursor, Windsurf, Zed, and other third-party editors can usually adopt it through a model dropdown. Pairing this with the workflow covered in Claude Code Review gives teams a cleaner adoption path.

Model IDs and supported platforms

The official API model ID is claude-opus-4-7 (source: Claude Platform Docs). Vertex AI uses claude-opus-4-7@20260416, while Bedrock uses anthropic.claude-opus-4-7-20260416-v1:0. The knowledge cutoff is listed as late January 2026, and the default max_tokens is 8192.

The most important API change: temperature, top_p, and top_k now behave as read-only defaults. If you send non-default values, the API can return a 400 error. Old Opus 4.6 wrappers need a quick cleanup before production rollout.

One-sentence summary

Opus 4.7 is stronger for coding agents, computer use, and MCP tool calling. The list price stayed flat, but the tokenizer change raises real cost. Long-context retrieval and creative use cases need extra caution.

How strong is Opus 4.7 in benchmarks?

The headline number is SWE-bench Pro 64.3%. In the same benchmark, GPT-5.4 is reported at 57.7% and Gemini 3.1 Pro at 54.2%, putting Opus 4.7 6-10 points ahead of the strongest frontier alternatives for this specific software-engineering task (source: Vellum AI).

SWE-bench Verified 87.6%, Pro 64.3%

On SWE-bench Verified, Opus 4.7 reaches 87.6%, up from 80.8% for 4.6 (source: The Next Web). SWE-bench Pro matters more for real-world signal because it uses paid repositories and is considered less contaminated than the public Verified split. Opus 4.7’s 64.3% Pro score is the public high-water mark at the time of writing (source: Anthropic).

Anthropic also reports CursorBench at 70%, up from 58% on 4.6. Rakuten’s internal test says the model ran autonomously for more than 24 hours, split a task into 23 parts, and wrote unit tests. Replit reported a 12-point improvement in code-generation accuracy after switching its agent stack to Opus 4.7.

OSWorld 78% and tool-calling gains

OSWorld-Verified measures whether an agent can complete tasks in a real OS with mouse and keyboard actions. Opus 4.7 scores 78.0%, above Opus 4.6 at 72.7% and GPT-5.4 at 75.0% (source: The Next Web). This is meaningful because computer-use agents must read screenshots, choose click targets, and maintain state across long sessions.

MCP-Atlas is also strong at 77.3%, about 9 points above GPT-5.4’s 68.1%. If your agent relies on many Model Context Protocol tools, Opus 4.7 becomes the default model to test first.

Benchmark table

Artificial Analysis ranks Opus 4.7 at 57 on its aggregate index, first among 133 evaluated models (source: Artificial Analysis). Still, aggregate scores can hide sharp domain differences. The MRCR regression below is the reason I would not route every workload to 4.7 automatically.

Why can the bill rise 1.35x if the listed price did not change?

Within a day of launch, developers started calling Opus 4.7 a hidden price increase. The price table did not move. The tokenizer did.

Listed price is the same as Opus 4.6

The official API price is $5 input and $25 output per 1M tokens, identical to Opus 4.6 (source: Anthropic News). Prompt cache write is $6.25 for 5 minutes, $10 for 1 hour, cache read is $0.50, and Batch API still gets a 50% discount. On the surface, this looks like price parity.

The new tokenizer can increase token count

Opus 4.7 uses a new tokenizer. Finout’s analysis says the same English text can become 1.0-1.15x tokens, while Korean, Japanese, and code-heavy mixed inputs can land around 1.25-1.35x (source: Finout). Anthropic frames this as more accurate segmentation, but billing is token-based, so the same prompt can cost more.

Practical cost scenarios

Assume 80% input tokens and 20% output tokens.

• Scenario A: $300/month, mostly English, 1.12x token expansion -> about $336
• Scenario B: $300/month, Korean plus code, 1.28x token expansion -> about $384
• Scenario C: $300/month, long context plus CJK text, 1.35x token expansion -> about $405

Finout reports that one large customer saw a 28% bill increase during the first week of rollout. The practical mitigations are aggressive prompt caching and Batch API for non-interactive workloads.

Opus 4.7 vs GPT-5.4 vs Gemini 3.1 Pro - which one should you use?

Once the benchmark and cost picture is clear, the real question is routing. Opus 4.7, GPT-5.4, Gemini 3.1 Pro, and Grok 4.20 now each have a distinct lane.

Coding agents: Opus 4.7 first

Opus 4.7 leads on SWE-bench Pro, OSWorld, and MCP-Atlas. Cursor’s model docs explicitly position it for autonomous long-session coding and multi-file refactors (source: Cursor Docs). The caveat is Terminal-Bench 2.0, where GPT-5.4 at 75.1% beats Opus 4.7 at 69.4%. For shell-heavy workloads, test GPT-5.4 too.

Web research and terminal work: GPT-5.4 remains strong

BrowseComp is GPT-5.4’s territory: 89.3% vs Opus 4.7’s 79.3%. Opus 4.7 even falls below Opus 4.6 on this benchmark. If your workflow is deep web research in a single browser session, GPT-5.4 is the safer default. GPQA Diamond is effectively tied, with GPT-5.4 at 94.4% and Opus 4.7 at 94.2%.

Multimodal value: Gemini 3.1 Pro

Gemini 3.1 Pro is cheaper at $2 input and $12 output per 1M tokens. It is the better fit for document, slide, image, and video analysis, especially when cost matters. Its SWE-bench Pro score, however, is weaker than Opus 4.7. If your team needs local open-source deployment, the Gemma 4 review is the better place to start.

How should you use xhigh mode and Task Budgets?

The most practical new knobs in Opus 4.7 are effort control and task-level token budgeting. Both can break old 4.6 code if you pass deprecated parameters.

When to enable xhigh effort

The effort levels are now low / medium / high / xhigh / max. Use them like this:

• high: default for most coding and refactor tasks
• xhigh: autonomous agents, large PR review, SWE-bench Pro-style tasks
• max: math proofs or research problems without tight time limits

import anthropic

client = anthropic.Anthropic()

message = client.messages.create(
    model="claude-opus-4-7",
    max_tokens=8000,
    thinking={"type": "enabled", "effort": "xhigh"},
    messages=[
        {
            "role": "user",
            "content": "Refactor this legacy module into a functional style and write unit tests for failure cases.",
        }
    ],
)

print(message.content)

thinking now accepts enabled or disabled. The old budget_tokens field is removed for Opus 4.7. The model decides the reasoning length adaptively.

Task Budgets beta for cost control

Autonomous agents need a hard ceiling. Task Budgets lets you set a token budget for one task, not just one response (source: Claude Platform Docs).

curl https://api.anthropic.com/v1/messages \
  -H "anthropic-version: 2023-06-01" \
  -H "anthropic-beta: task-budgets-2026-03-13" \
  -H "x-api-key: $ANTHROPIC_API_KEY" \
  -H "content-type: application/json" \
  -d '{
    "model": "claude-opus-4-7",
    "max_tokens": 20000,
    "metadata": {"task_budget": {"max_tokens": 50000}},
    "messages": [
      {"role": "user", "content": "Scan this repo and write a TypeScript migration plan."}
    ]
  }'

When the task budget is exhausted, the agent stops early and returns partial progress. This is the safety rail that prevents one runaway agent loop from burning hundreds of dollars.

Migration checklist

Before replacing 4.6 in production, check these three points:

• Remove temperature, top_p, and top_k overrides.
• Remove thinking.budget_tokens.
• Update any parser that expected raw thinking output; the default is summarized thinking.

What do 3.75MP vision and filesystem memory change?

Opus 4.7 is not only a better model; it is a bigger agent substrate.

2576px vision

Opus 4.6 accepted images up to about 1568px, roughly 1.15MP. Opus 4.7 raises that to 2576 x 2576px, around 3.75MP (source: Claude Platform Docs). That matters for dashboard screenshots, PCB photos, CAD drawings, and other images where details get lost after resizing. It also helps explain the CharXiv Reasoning and OfficeQA Pro jumps.

Filesystem-based memory

Anthropic introduced a filesystem-memory pattern where an agent saves and loads state across sessions. A refactor agent can write progress into a .claude/state/session-xyz.json file on Monday and continue from that state on Wednesday. Simon Willison analyzed how Opus 4.7 treats /memories/ as a filesystem-like pattern in its system prompt (source: Simon Willison).

Claude in PowerPoint and Claude Design with Canva

Anthropic also announced Claude in PowerPoint and a Claude Design partnership with Canva around the same launch window (source: The Next Web). The broader message is clear: Opus is moving out of the chat UI and into office, design, and development workflows.

Where did Opus 4.7 regress?

After the launch excitement, a more complicated story emerged. Opus 4.7 is not a straight upgrade for every task.

MRCR 78.3% -> 32.2%

MRCR, or Multi-needle Retrieval Context Recall, asks a model to find multiple specific facts inside a long context. Opus 4.7 scores 32.2%, down from 78.3% for Opus 4.6 (source: RoboRhythms). If your workflow is “put 1M tokens of documents into the prompt and find the exact quote,” Opus 4.6 may still be the better model.

NYT Connections also reportedly drops from 94.7% to 41%, which suggests weaker everyday language pattern matching in some contexts.

Creative writing and emotional support feel flatter

Reddit users report that Opus 4.7 can feel more clinical in emotional-support conversations and less imaginative in fiction or copywriting. A likely explanation is stronger factuality and safety tuning. For counseling-like conversation, fiction, and brand copy, Opus 4.6 or GPT-5.4 may still feel more natural.

Latency is slower than GPT-5.4

Artificial Analysis measures first-token latency around 3.8 seconds for Opus 4.7, roughly twice GPT-5.4’s 1.9 seconds (source: Artificial Analysis). Generation speed is around 52 tokens/sec, about half of Sonnet 4.6. For realtime chat UX, that delay is visible.

What is the community saying?

The reaction split into two narratives: “coding-agent breakthrough” and “hidden cost increase.”

Simon Willison highlighted the new /memories/ filesystem pattern as an interesting agent-implementation signal (source: Simon Willison). Cursor made Opus 4.7 a recommended coding model. Hacker News sent the launch thread to the front page with hundreds of comments (source: Hacker News).

Who should use Claude Opus 4.7?

Recommended for

• Full-codebase refactors, type migrations, and test generation
• Autonomous CI agents that review PRs and patch code
• Computer-use automation and RPA-like workflows
• MCP tool chains with many internal tools connected
• High-resolution screenshot, dashboard, CAD, and document-image analysis

Not recommended as the default for

• Deep web research: GPT-5.4 is stronger on BrowseComp.
• Exact quote retrieval from huge documents: keep Opus 4.6.
• Fiction, copywriting, and emotional-support chat: test 4.6 or GPT-5.4.
• Cost-sensitive chatbots: Sonnet 4.6 or Haiku 4.5 is far cheaper.

Pro/Max subscription vs API

For individuals, Claude Pro or Max can still be cheaper than direct API usage, but Opus 4.7 burns quota faster. For teams, direct API usage with prompt caching, Batch API, and budget alerts is easier to control. Larger organizations may prefer Bedrock or Vertex AI for procurement and compliance reasons.

Conclusion