Claude Opus 4.7 vs DeepSeek V4: Which Model Should You Use?

If you're deciding between Claude Opus 4.7 and DeepSeek V4 for your next project, the choice comes down to a real tradeoff: Anthropic's closed, polished flagship versus DeepSeek's open-weight, aggressively priced challenger. Both arrived within days of each other in April 2026, and both claim near-frontier performance on agentic coding and long-context reasoning.

What makes this comparison interesting is that DeepSeek V4 is the first open-weight model to credibly sit in the same conversation as Opus 4.7 on agentic benchmarks. At the same time, Opus 4.7 ships with features like task budgets, an xhigh effort level, and a new /ultrareview command in Claude Code that DeepSeek simply doesn't have equivalents for yet.

In this article, I'll compare Claude Opus 4.7 and DeepSeek V4 across five key dimensions: coding and agentic workflows, reasoning and knowledge tasks, multimodal and tool use, pricing, and open-weight access. You can also see our standalone guides to DeepSeek V4 and Claude Opus 4.7 for deeper dives into each model.

What Is Claude Opus 4.7?

Claude Opus 4.7 is Anthropic's latest flagship model, released on April 16, 2026. It's designed for complex, long-running agentic workflows, with particular emphasis on software engineering and high-resolution vision tasks. The model accepts images up to 2,576 pixels on the long edge, roughly 3.75 megapixels, which is more than three times the resolution supported by prior Claude models.

The release introduces a new xhigh effort level sitting between high and max, task budgets in public beta for controlling token spend across long runs, and a /ultrareview slash command in Claude Code for dedicated code review sessions. Anthropic also notes that Opus 4.7 is the first model to ship with real-time cyber safeguards as part of their Project Glasswing initiative, making it a test vehicle for safety features ahead of a broader Mythos-class release.

To see Opus 4.7 in action, check out our Claude Opus 4.7 Practical Benchmark Tutorial, which tests whether Opus 4.7's self-critique memory improves coding performance, and our Claude Opus 4.7 API Tutorial that guides you through building a digitizer app using the Anthropic API. You can also see how it stacks up against other flagship models in our comparison pieces with Gemini 3.1 Pro and GPT-5.5.

What Is DeepSeek V4?

DeepSeek V4 is a preview release from the Chinese AI lab DeepSeek, launched on April 24, 2026. It comes in two variants: V4-Pro, with 1.6 trillion total parameters and 49 billion active parameters, and V4-Flash, with 284 billion total and 13 billion active. Both use a Mixture of Experts architecture and ship with a 1-million-token context window as the default across all services.

The headline claim is structural efficiency. DeepSeek says V4-Pro requires only 27% of the single-token inference FLOPs and 10% of the KV cache compared to its predecessor, V3.2, in a 1M-token context scenario. Both models are open-weight under the MIT License, available on Hugging Face. The API supports both OpenAI and Anthropic API formats, and both models offer thinking and non-thinking modes.

For a full breakdown of DeepSeek V4's architecture, benchmarks, and access options, see our DeepSeek V4 guide. Also, make sure to read our comparison of DeepSeek V4 vs GPT-5.5.

Claude Opus 4.7 vs DeepSeek V4: Head-to-Head Comparison

Here's a quick reference before we get into the details. The table covers the most decision-relevant dimensions across both models.

Coding and agentic workflows

Agentic coding is the dimension where the gap between the two models is most visible. On SWE-bench Pro, which tests the resolution of real GitHub issues in open-source Python repositories, Opus 4.7 scores 64.3% against DeepSeek V4-Pro's 55.4%. That's a nearly 9-point gap on a benchmark that's widely used as a proxy for production-level coding ability.

On Terminal-Bench 2.0, the picture is closer. Opus 4.7 scores 69.4% and DeepSeek V4-Pro scores 67.9%, a gap of about 1.5 points. Both models are meaningfully behind GPT-5.5's 82.7% on this benchmark, which is the clear leader here.

Opus 4.7 also ships with dedicated agentic tooling that DeepSeek V4 doesn't match yet. The xhigh effort level, task budgets for controlling token spend, and /ultrareview in Claude Code are all production-facing features. DeepSeek V4 claims integration with Claude Code, OpenClaw, and OpenCode, and DeepSeek says it's already running V4-Pro for its own in-house agentic coding. But the ecosystem around Opus 4.7 is more mature for teams already using Claude Code.

For repository-level engineering work, Opus 4.7 is the stronger choice. The SWE-bench Pro gap is real, and the agentic tooling around it is more developed. DeepSeek V4-Pro is competitive on terminal tasks, but it doesn't close the gap on the harder coding benchmark.

Reasoning and knowledge tasks

On GPQA Diamond, which tests graduate-level reasoning across science and mathematics, Opus 4.7 scores 94.2% and DeepSeek V4-Pro scores 90.1%. Both are strong, but the 4-point gap is notable given that GPQA Diamond is increasingly saturated at the frontier. Gemini 3.1 Pro scores 94.3% on the same benchmark, so Opus 4.7 and Gemini are essentially tied while DeepSeek trails slightly.

On MMLU-Pro, DeepSeek V4-Pro-Max scores 87.5%, which is competitive with older frontier models. On GSM8K for math, it scores 92.6%. These are strong numbers for an open-weight model, though Anthropic doesn't publish Opus 4.7's MMLU-Pro score in the release notes, making a direct comparison difficult.

Opus 4.7 really shines on Humanity's Last Exam, a collection of graduate-level questions across science, mathematics, and humanities: it scores 46.9% without tools and 54.7% with tools. It takes the first spot in the leaderboard without tools, and ranks second behind GPT-5.5's Pro variant (58.7%) with tool use. DeepSeek V4 Pro is significantly, but not too far behind, with 48.2% in the tool use version.

It's safe to say that Opus 4.7 is the better choice for the hardest reasoning tasks.

Tool use and computer interaction

Opus 4.7 leads on both major tool-use benchmarks in the comparison. On MCP-Atlas, which tests performance across complex multi-tool workflows, Opus 4.7 scores 77.3%, the highest of any model. DeepSeek V4 Pro scores 73.6%, which comes surprisingly close and is the best score for open-weight models, putting GLM-5.1 Thinking (71.8%) in second place.

On OSWorld-Verified, which measures a model's ability to complete tasks by controlling a computer interface, Opus 4.7 scores 78.0%, up from 72.7% in Opus 4.6 and on par with GPT-5.5 (78.7%). 

DeepSeek V4 doesn't publish scores on OSWorld in its release notes. The official announcement notes that V4-Flash performs on par with V4-Pro on simple agent tasks, and that V4-Pro is the open-source state of the art on agentic coding benchmarks. But without published numbers on computer use, it's hard to make a direct comparison on this dimension.

One surprising result was that DeepSeek V4 Pro actually leads in agentic search: its BrowseComp score of 83.4% beats Opus 4.7 (79.3%) and is just one percentage point short of the leader, GPT-5.5 (84.4%).

If your workflow depends on multi-tool orchestration or computer use agents, Opus 4.7 is the better-evidenced choice. For use cases specialized on agentic search, however, DeepSeek V4 Pro is the better choice, not only because of but especially considering its much lower price.

Multimodal capabilities

Opus 4.7 made a significant jump in vision. It now accepts images up to 2,576 pixels on the long edge, roughly 3.75 megapixels, which is more than three times the resolution of prior Claude models. On CharXiv Reasoning, which tests visual reasoning over charts and figures, Opus 4.7 scores 82.1% without tools and 91.0% with tools, up from 69.1% and 84.7% in Opus 4.6.

DeepSeek V4's release notes don't include multimodal benchmark scores or detailed image input capabilities. The official announcement focuses on text-based agentic coding and long-context efficiency. For workflows that depend on high-resolution image analysis, dense chart reading, or computer-use agents that need to parse screenshots, Opus 4.7 is the clear choice based on available evidence.

Pricing

This is where DeepSeek V4 makes its strongest case. DeepSeek V4-Pro costs $1.74 per million input tokens and $3.48 per million output tokens. Opus 4.7 costs $5.00 per million input tokens and $25.00 per million output tokens. On output tokens alone, Opus 4.7 is more than 7 times more expensive than V4-Pro.

DeepSeek V4-Flash is even cheaper: $0.14 per million input tokens and $0.28 per million output tokens. For high-volume workloads where V4-Flash's reasoning capabilities are sufficient, the cost difference versus Opus 4.7 is dramatic. Our DeepSeek V4 guide notes that V4-Flash significantly undercuts even small models like GPT-5.4 Nano on price.

There's one important caveat on Opus 4.7 pricing. The model ships with a new tokenizer that maps the same input to roughly 1.0 to 1.35 times more tokens than Opus 4.6, depending on content type. At higher effort levels, it also produces more output tokens. Anthropic recommends measuring actual token usage on real traffic before assuming the per-token price translates directly to cost.

For teams running high-volume agentic pipelines where the benchmark gap between Opus 4.7 and V4-Pro is acceptable, DeepSeek V4-Pro's pricing is a serious argument. The output token cost difference is large enough to change the economics of long-running agent workflows.

Open-weight access and deployment flexibility

DeepSeek V4 is open-weight under the MIT License. Both V4-Pro and V4-Flash weights are available on Hugging Face. V4-Pro is an 865GB download, which rules out consumer hardware, but for teams with the infrastructure to self-host, the MIT License means no API dependency and full control over deployment.

Opus 4.7 is closed. It's available via the Claude API, Amazon Bedrock, Google Cloud's Vertex AI, and Microsoft Foundry. There's no self-hosting option. For regulated industries or teams with strict data residency requirements, the cloud-only constraint is a real limitation, though the availability across three major cloud providers does give some flexibility on where the inference runs.

DeepSeek also supports both OpenAI and Anthropic API formats, which means migrating existing code to V4-Pro requires only a model parameter update. The legacy deepseek-chat and deepseek-reasoner endpoints are being retired on July 24, 2026, so teams using those should plan a migration to deepseek-v4-flash or deepseek-v4-pro.

When to Choose Claude Opus 4.7 vs DeepSeek V4

The decision mostly comes down to three factors: how much the benchmark gap on hard coding tasks matters to you, whether open-weight access is a requirement, and what your token budget looks like at scale.

Choose Claude Opus 4.7 if...

• Your work centers on hard software engineering tasks. The 8.9-point gap on SWE-bench Pro over V4-Pro is the largest single differentiator in this comparison, and it holds up across multiple third-party testers, including Cursor (70% vs 58% on CursorBench) and Rakuten (3x more production tasks resolved than Opus 4.6).
• You're building production agent systems that rely on computer use. Opus 4.7 leads MCP-Atlas at 77.3%, and scores strongly on OSWorld-Verified at 78.0%, where DeepSeek V4 doesn't publish any score.
• High-resolution vision is part of your pipeline. The jump to 3.75 megapixel support and the 13-point gain on CharXiv Reasoning opens up use cases like dense chart extraction and computer-use agents reading complex screenshots.
• You're already using Claude Code and want the full agentic tooling stack, including task budgets, xhigh effort, and /ultrareview.

Choose DeepSeek V4 if...

• Cost is a primary constraint. At $3.48 per million output tokens versus $25.00 for Opus 4.7, V4-Pro is dramatically cheaper for output-heavy workloads. V4-Flash at $0.28 per million output tokens is in a different cost tier entirely.
• You need self-hosted or air-gapped deployment. The MIT License and Hugging Face availability make V4 the only option here; Opus 4.7 is cloud-only.
• You want to fine-tune or modify the model weights. The MIT License permits this; Anthropic's terms do not.
• You're running high-volume pipelines where the economics of Opus 4.7 don't work at scale, and you're willing to accept some performance tradeoff on the hardest tasks.

Final Thoughts

If I had to pick one model for production agentic coding work without a budget constraint, I'd use Opus 4.7 (or GPT-5.5). The SWE-bench Pro gap is real, the tool-use benchmarks are the best in the comparison, and the agentic tooling around Claude Code is more developed. The vision improvements alone, going from 1.15MP to 3.75MP support with a 13-point gain on CharXiv, make it a meaningful upgrade for multimodal workflows.

That said, DeepSeek V4-Pro is the most credible open-weight challenger to a closed frontier model I've seen. The pricing argument is hard to ignore at scale: if you're running millions of tokens of output per day, the difference between $3.48 and $25.00 per million tokens changes the economics of what's viable. And the MIT License is genuinely valuable for teams that need deployment flexibility or want to fine-tune.

My practical recommendation: use Opus 4.7 for the hardest coding and agentic tasks where benchmark performance directly translates to fewer errors and less supervision. Use DeepSeek V4-Pro where cost matters and the task complexity is moderate. Use V4-Flash for high-volume, lower-stakes workloads where you need to keep costs minimal. The models aren't really competing for the same user in most cases.

If you want to get hands-on with these models and build real workflows, I'd recommend starting with our AI Agent Fundamentals skill track, which covers how to build and deploy agentic systems using frontier models. For prompt engineering that works across both Opus 4.7 and DeepSeek V4, our Understanding Prompt Engineering course is a good starting point.