GPT-5.5 vs Claude 4.7 vs DeepSeek V4: Tested

Three flagship AI models dropped within a week of each other in April 2026. GPT-5.5 landed on April 23rd. Claude Opus 4.7 followed shortly after. DeepSeek V4 hit with a 2,000-point Hacker News thread. The AI community is buzzing, and everyone's asking the same question: which one should I actually use?

Benchmarks won't answer that. They tell you which model scores highest on graduate-level physics problems — not which one will write your quarterly report, debug your Python script, or summarize 40 pages of research without hallucinating. So I ran all three through the tasks regular people actually do, tracked the results, and put together this comparison so you don't have to burn $50 in API credits figuring it out yourself.

What's Actually New in Each Model

Before the head-to-head, here's what each release brings to the table:

GPT-5.5 is OpenAI's largest model yet — a unified architecture that merges the old reasoning/non-reasoning split. It handles chain-of-thought natively without needing to choose between GPT and o-series models. The context window sits at 1M tokens, and OpenAI claims a 40% improvement in instruction-following over GPT-5.4. It also ships with improved computer-use capabilities baked in, building on the desktop control features from 5.4.

Claude Opus 4.7 is Anthropic's top-of-line update, extending the Opus 4 family with a 1M context window, stronger agentic tool use, and noticeably better creative writing. Anthropic has focused heavily on reliability — fewer refusals on legitimate tasks, more consistent output formatting, and improved performance on long-horizon coding projects where the model needs to hold architectural context across many files.

DeepSeek V4 is the open-weight wildcard. Running on a mixture-of-experts architecture, it delivers performance that rivals the closed-source giants at a fraction of the cost. The V4 release improves multilingual support, math reasoning, and code generation. You can run it locally on high-end hardware or use it through DeepSeek's API at prices that undercut OpenAI and Anthropic by 5-8x.

The Testing Setup

I tested each model through its primary API (OpenAI, Anthropic, DeepSeek) using the same prompts, same temperature settings (0.7 for creative tasks, 0 for code/factual), and same system instructions. For agentic tasks, I used each provider's native tool-use implementation. All tests ran in the last week of April 2026.

Five categories. Real tasks. No cherry-picking.

Coding: Who Writes Better Software

I gave each model three coding challenges: refactor a 400-line Express.js middleware into clean modules, write a Python data pipeline with error handling and retry logic, and debug a subtle React state management bug I pulled from a real project.

Claude Opus 4.7 dominated here. The Express refactor came back with sensible module boundaries, proper TypeScript types, and it even caught a potential memory leak in the original code I hadn't flagged. The React debugging was surgical — it identified the stale closure issue in four sentences and provided a minimal fix rather than rewriting the whole component.

GPT-5.5 was close behind. Its code was correct and well-structured, but it had a tendency to over-engineer. The Express refactor included an abstract factory pattern that nobody asked for. The Python pipeline worked perfectly but came with 60% more code than necessary, including configuration options for scenarios that weren't in the requirements.

DeepSeek V4 surprised me on the Python pipeline — clean, idiomatic code with smart use of tenacity for retries. But the React debugging missed the root cause on the first pass, identifying a symptom instead. After a follow-up prompt, it nailed it. The Express refactor was functional but less polished than the other two.

Bottom line: Claude 4.7 for coding, especially debugging and refactoring. GPT-5.5 if you want thorough (sometimes too thorough) implementations. DeepSeek V4 is genuinely competitive for Python and data work.

Writing: From Emails to Long-Form Content

Three writing tests: a tricky client email delivering bad news, a 1,500-word blog post about renewable energy policy, and rewriting a dense academic paragraph for a general audience.

GPT-5.5 produced the best email — professional, empathetic, and direct without being blunt. The tone management was excellent, something OpenAI has clearly prioritized. The blog post was solid but had that unmistakable "AI polish" — every paragraph perfectly structured, every transition smooth to the point of blandness.

Claude Opus 4.7 wrote the strongest long-form content. The blog post read like it had a point of view. It made an actual argument about policy tradeoffs rather than presenting a balanced-to-the-point-of-saying-nothing overview. The academic rewrite was the clearest of the three — it found the core insight buried in jargon and led with it.

DeepSeek V4 was serviceable but noticeably behind on English prose. The email was a bit stiff. The blog post was accurate but read like a Wikipedia article — informative, well-organized, low personality. Where DeepSeek shines is multilingual writing; if you need content in Mandarin, Korean, or Japanese, it's arguably the best of the three.

Bottom line: GPT-5.5 for polished professional communication. Claude 4.7 for content with personality and substance. DeepSeek V4 for multilingual work.

Research and Analysis: Handling Complex Information

I fed each model the same 85-page PDF — a World Bank climate finance report — and asked for: a structured summary, three non-obvious insights, and a list of claims that seemed weakly supported by the data presented.

GPT-5.5 produced the most thorough summary, with clear section-by-section breakdowns and accurate page references. The "non-obvious insights" were genuinely interesting — it connected a funding gap mentioned on page 34 with a contradictory projection on page 71 that I'd missed myself.

Claude Opus 4.7 was slightly less detailed in the summary but excelled at the critical analysis. Its list of weakly-supported claims was the most precise, citing specific figures and explaining why the evidence was insufficient. This is where the 1M context window paired with strong reasoning really pays off.

DeepSeek V4 handled the summary competently but struggled with the critical analysis task. Two of its "weakly supported claims" were actually well-supported — it had misread a table. The non-obvious insights were surface-level compared to the other two.

Bottom line: GPT-5.5 and Claude 4.7 are both excellent for research. GPT-5.5 edges ahead on synthesis; Claude 4.7 on critical analysis. DeepSeek V4 is a tier below for complex document work.

Agentic Tasks: Running Multi-Step Workflows

This is where things get interesting — and where the safety conversation matters. I set up a task chain: search a codebase for security vulnerabilities, create GitHub issues for each finding, and draft fix PRs with explanations. All three models had access to the same tools (file read, GitHub API, shell execution in a sandbox).

Claude Opus 4.7 was the most reliable agent. It completed all five steps without intervention, created well-categorized issues, and its PRs included clear commit messages. Importantly, it asked for confirmation before executing shell commands that could modify state — a safety pattern Anthropic has clearly emphasized since the database deletion incident that made headlines earlier this year.

GPT-5.5 completed the workflow but got stuck twice, requiring manual nudges when the GitHub API returned rate-limit errors. It recovered after prompting but didn't implement retry logic on its own. The PRs were good quality. The computer-use features are powerful but feel like they need more guardrails — it tried to open a browser at one point when a simple API call would've worked.

DeepSeek V4 struggled the most with multi-step agency. It completed 3 of 5 steps autonomously before losing track of its plan. The individual actions were fine — its code analysis was thorough — but maintaining coherent state across a long chain of tool calls is where you feel the gap versus the closed-source models. For shorter, 2-3 step workflows, it performs well.

Bottom line: Claude 4.7 for agentic reliability, especially where safety matters. GPT-5.5 is capable but rougher around the edges. DeepSeek V4 works for simpler agent tasks.

Speed and Cost: The Numbers That Matter

Model	Input (per 1M tokens)	Output (per 1M tokens)	Avg Response Time (500-token reply)	Context Window
GPT-5.5	$12.00	$36.00	~3.2s	1M tokens
Claude Opus 4.7	$15.00	$75.00	~3.8s	1M tokens
DeepSeek V4	$2.00	$8.00	~4.1s	256K tokens

DeepSeek V4 is dramatically cheaper — roughly 5-8x less than the competition depending on the task. For bulk processing, data extraction, or any workflow where you're running thousands of calls, the cost difference is massive. We're talking $16 vs $100+ for the same batch job.

Claude Opus 4.7 is the most expensive, especially on output tokens. If your use case generates long responses (code generation, long-form writing), the costs add up fast. That said, if you need fewer iterations to get the right answer, the per-task cost can actually be lower despite the higher token price.

GPT-5.5 sits in the middle on pricing and is the fastest of the three. For latency-sensitive applications — chatbots, real-time assistants — that speed advantage matters.

The Open-Source Factor

DeepSeek V4's weights are available under a permissive license. This changes the calculus entirely for certain use cases. If you have the hardware (or cloud GPU budget), you can run V4 with zero API costs, full data privacy, and no rate limits. Fine-tuning is possible. Custom deployments are possible. None of that applies to GPT-5.5 or Claude 4.7.

For enterprises worried about data leaving their infrastructure, or startups that need to control costs as they scale, DeepSeek V4 deserves serious consideration even where its raw capability falls slightly short. The 80/20 math often works out in its favor: 85-90% of the capability at 15% of the cost.

Which Model Should You Actually Use

There's no single winner. Here's how I'd allocate them:

• For coding and development: Claude Opus 4.7. Best debugging, cleanest refactors, most reliable agentic coding workflows. Worth the premium if code quality saves you review cycles.
• For professional communication and polished writing: GPT-5.5. The tone control and instruction-following make it the most dependable for client-facing content, emails, and reports.
• For research and critical analysis: Either GPT-5.5 or Claude 4.7, depending on whether you prioritize synthesis (GPT) or critique (Claude).
• For cost-sensitive bulk work: DeepSeek V4. Summarization, extraction, translation, classification — any task where you're processing volume and 90% accuracy is fine.
• For privacy-sensitive deployments: DeepSeek V4 (self-hosted). It's the only option where your data never leaves your servers.
• For long-running agent workflows: Claude Opus 4.7. Most consistent at maintaining state and plan coherence across 10+ tool calls, with the best safety defaults.

The Smart Play: Use More Than One

The best practitioners I know aren't loyal to a single model. They route tasks to the right model the way a carpenter picks the right tool from the belt. A typical workflow might use DeepSeek V4 for initial data processing, Claude 4.7 for analysis and code generation, and GPT-5.5 for drafting the final client deliverable.

API routing tools like LiteLLM and OpenRouter make this trivial — same interface, different backends, automatic fallbacks. If you're still locked into a single provider, April 2026 is the month to break that habit. The gap between these models isn't about one being universally better. It's about each being specifically better at the things it's best at.

Pick the right model for the task. Your output quality goes up, your costs go down, and you stop arguing about which AI is "the best" — because the answer is always "depends on what you're doing with it."