Claude Opus 4.7 vs GPT-5.4 vs Gemini 3.1 Pro Compared

Two days ago, Anthropic dropped Claude Opus 4.7. That means all three major AI labs now have fresh flagship models out in the wild — Opus 4.7, GPT-5.4, and Gemini 3.1 Pro. If you’re trying to figure out which one deserves your $20/month subscription or your API budget, the answer isn’t straightforward. Each model has a clear lead in specific areas, and picking the wrong one for your workflow means you’re paying for capability you’re not using.

I’ve been running all three since Opus 4.7 hit on April 16. Here’s where things actually stand.

The Quick Verdict (If You’re in a Hurry)

Coding and agentic work: Claude Opus 4.7 wins, and it’s not close on the hard stuff.

Browse-heavy research and synthesis: GPT-5.4 Pro still has the edge.

Massive context processing and Google ecosystem: Gemini 3.1 Pro is your pick.

Tightest budget: GPT-5.4 Mini gets you surprisingly far at a fraction of the cost.

But those one-liners don’t tell the whole story. The gaps between these models are narrower than any previous generation, and the right choice depends heavily on what you’re actually doing with them.

Coding: Opus 4.7 Takes the Crown

This is where the Opus 4.7 launch matters most. Anthropic has been steadily pulling ahead on coding benchmarks for the past year, and 4.7 extends that lead significantly.

On SWE-bench Pro — the harder variant that tests real-world software engineering tasks — Opus 4.7 scores 64.3%. That’s a massive jump from Opus 4.6’s 53.4%, and it sits well ahead of GPT-5.4 at 57.7% and Gemini 3.1 Pro at 54.2%. On the standard SWE-bench Verified, Opus 4.7 hits 87.6%, nearly seven points above its predecessor.

What does that mean in practice? If you’re using AI for multi-step coding tasks — debugging across files, refactoring large codebases, building features that touch multiple systems — Opus 4.7 handles the complexity better. It loses the thread less often on long-horizon tasks and makes fewer assumptions that force you to course-correct.

GPT-5.4 isn’t bad at coding. Its unified architecture (which absorbed the old Codex line) means you don’t need a separate coding model anymore. For straightforward tasks — writing functions, explaining code, generating boilerplate — you won’t notice a meaningful difference between the three. The gap shows up when tasks get complex and require sustained reasoning across multiple tool calls.

Gemini 3.1 Pro falls behind on coding benchmarks, but it has a trick the others don’t: a 2M token context window. If you need to feed an entire codebase into a single prompt and ask questions about it, Gemini can handle roughly twice what the other two can. That’s a legitimate advantage for code comprehension tasks, even if it’s weaker at code generation.

Writing and Analysis: The Most Subjective Category

Benchmarks can tell you about coding ability, but writing quality is harder to pin down with numbers. Here’s my take after using all three extensively.

Claude Opus 4.7 produces the most natural-sounding prose. It follows complex instructions more reliably — if you give it a detailed style guide or a specific voice to match, it sticks to it better than the competition. For long-form content, analysis, and anything where tone matters, it’s my default.

GPT-5.4 is the strongest at synthesis. Give it a pile of documents, research papers, or web pages and ask it to pull together a coherent analysis, and it does a better job of identifying the signal in the noise. Its BrowseComp score (89.3% for the Pro variant) reflects this — it’s better at finding and connecting information across sources. If your workflow involves heavy research, GPT-5.4 has an edge.

Gemini 3.1 Pro does well with structured outputs and data transformation. Need to convert a messy data dump into a clean table? Reformat documents between different schemas? Gemini handles that kind of mechanical text work reliably. It’s also the strongest for multilingual tasks if you need to work across languages within a single conversation.

None of these models produce embarrassingly bad writing anymore. The differences are real but subtle — you’d notice them if you switched between models daily, but a casual user might not.

Context Windows: Bigger Isn’t Always Better

All three models now handle at least a million tokens of context. But the details matter.

Gemini 3.1 Pro leads with a 2M token context window, plus up to 65K tokens of output. You can process 900-page PDFs, 8+ hours of audio, or an hour of video in a single prompt. If your work involves massive documents, this is a genuine differentiator.

GPT-5.4 offers a 1.05M token context window across all its variants. There’s a catch, though: prompts over 272K input tokens get hit with 2x input pricing and 1.5x output pricing. So while you can use the full window, doing so regularly gets expensive fast.

Claude Opus 4.7 provides a 1M token context window. Anthropic doesn’t charge extra for using the full window, which makes it simpler to predict costs if you regularly work with large contexts.

Here’s the thing most people miss about context windows: for 90% of use cases, you’re not hitting anywhere near these limits. The context window matters if you’re processing entire codebases, lengthy legal documents, or building applications that need long conversation histories. For typical chat-based usage, all three are more than adequate.

Pricing: Where It Gets Interesting

This is where the comparison gets complicated, because these models have very different pricing structures.

API Pricing (per million tokens)

On raw price-per-token, Gemini 3.1 Pro is the cheapest flagship and GPT-5.4 Standard undercuts Claude significantly. Opus 4.7 is the most expensive standard-tier option at $5/$25.

But price-per-token is misleading if one model takes fewer tokens to get the job done. If Opus 4.7 solves a coding task in one pass where GPT-5.4 needs two attempts, the cheaper per-token rate doesn’t save you anything. This is especially relevant for agentic workflows where models make multiple tool calls — a model that gets it right the first time burns fewer total tokens.

OpenAI’s variant strategy deserves attention here. GPT-5.4 Mini scores 54.38% on SWE-bench Pro — remarkably close to the full model’s 57.7% — at roughly one-sixth the cost. For high-volume applications where you can tolerate slightly lower accuracy, Mini is hard to beat on value. Neither Anthropic nor Google offer a comparable “almost as good but way cheaper” variant of their flagship.

Subscription Pricing

If you’re using these through their consumer products rather than APIs:

All three standard tiers cost the same and give you access to the flagship model with usage caps. The premium tiers are where they diverge — Claude’s Max tiers offer 5x and 20x usage multipliers, ChatGPT Pro gives unlimited advanced reasoning, and Google AI Ultra is the priciest at $250/month.

For most individual users, the $20/month tier of whichever platform you prefer is fine. The premium tiers make sense if you’re a power user hitting rate limits daily or if you need specific features tied to one ecosystem.

New Features Worth Knowing About

Each model brought notable new capabilities this generation, beyond just being “smarter.”

Opus 4.7: Better Eyes and Finer Control

The 3x improvement in vision resolution is more practical than it sounds. Opus 4.7 generates noticeably better interfaces and documents when it can see reference images clearly. If you’re using AI to build UIs based on mockups or to analyze screenshots, this matters.

The new xhigh reasoning effort level slots between high and max, giving developers finer control over the reasoning-versus-speed tradeoff. Task budgets (currently in beta) let you set cost caps for longer agent runs — useful if you’re building autonomous workflows and don’t want a runaway task burning through your budget.

GPT-5.4: One Model to Rule Them All

OpenAI’s big move was consolidation. GPT-5.4 absorbed the Codex line, meaning one model handles coding, reasoning, computer use, and general conversation. No more picking between specialized models for different tasks. It also ships with computer use capabilities scoring 75% on OSWorld, making it viable for browser-based automation tasks.

The five-variant strategy (Standard, Thinking, Pro, Mini, Nano) gives developers a full spectrum from high-capability to low-cost, all with the same API interface. That’s genuinely useful for building applications where you route easy queries to Mini and hard ones to Standard or Pro.

Gemini 3.1 Pro: Context and Cost

Beyond the 2M context window, Gemini’s context caching can cut costs by up to 75% for applications that reuse large contexts frequently. If you’re building a product that sends the same base documents plus varying queries, Gemini’s caching makes it substantially cheaper to operate than the alternatives.

Google also tightened integration with Workspace — Gemini in Docs, Sheets, and Gmail uses the Pro model, which matters if your team lives in the Google ecosystem.

Real-World Scenarios: Just Pick One Already

Let me cut through the analysis and give you direct recommendations for common use cases.

You’re a developer using AI-assisted coding daily. Use Claude Opus 4.7 via Claude Code, Cursor, or the API. The SWE-bench Pro gap is real, and it compounds over a full day of coding. The $20/month Claude Pro subscription is the easiest way to try it.

You’re building AI-powered products and need API access. Start with GPT-5.4 Mini for anything that doesn’t require top-tier reasoning. Route complex tasks to GPT-5.4 Standard. This two-tier approach will likely be cheaper than using a single flagship model for everything.

You do a lot of research and writing. GPT-5.4 is strongest at pulling together information from multiple sources. Pair it with Claude for the final writing pass if tone matters. Yes, using two models sounds annoying, but the results are better than either alone.

You process long documents or large codebases. Gemini 3.1 Pro with its 2M context window. Nothing else comes close for raw context capacity, and the pricing is competitive for large-context workloads.

You want one subscription and don’t want to think about it. ChatGPT Plus at $20/month gives you the broadest feature set — GPT-5.4, DALL-E, browsing, computer use, and the Mini/Nano variants are all included. Claude Pro gives you better coding and writing but fewer bells and whistles. Your call.

You’re building agentic workflows with lots of tool calls. Opus 4.7. It wins 6 of 9 tool-use benchmarks against GPT-5.4, and the task budgets feature helps manage costs in production.

The Bigger Picture: Model Routing Is the Real Answer

Here’s what I think most comparison articles miss: the era of picking one AI model is ending. If you’re serious about using AI productively, you should be thinking about routing different tasks to different models.

The cost difference between GPT-5.4 Mini ($0.40/$1.60 per MTok) and Claude Opus 4.7 ($5/$25 per MTok) is roughly 12x. For straightforward tasks — summarizing text, formatting data, answering simple questions — Mini handles them fine. Save the expensive flagship models for tasks that actually need their capabilities.

Tools like OpenRouter already make this practical, letting you switch models with a single parameter change. And if you’re building applications, a simple classifier that routes queries based on complexity can cut your AI costs dramatically without hurting quality where it counts.

The models are converging on general capability. Where they differ is at the edges — hard coding tasks, massive context, research synthesis, cost efficiency. Match the model to the task, and you’ll get better results for less money than picking a single champion.

One thing to try this week: take your most common AI task and run it through all three models. Not a toy example — your actual workflow. The benchmarks give you a starting point, but nothing beats seeing which model clicks with how you work.