Claude Opus 4.7 just dropped — and we ran it through the full benchmark gauntlet against GPT-5.4 and Gemini 3.1 Pro. The result? No single model wins everything. Claude leads on coding and agents. GPT-5.4 owns web research. Gemini wins on cost and context length.
On April 16, 2026, Anthropic released Claude Opus 4.7, its most capable generally available model to date. The timing is deliberate — it lands squarely in a three-way frontier race with OpenAI’s GPT-5.4 and Google’s Gemini 3.1 Pro, all released within a six-week window.
The instinct is to ask: which one wins? But that’s the wrong question. After running through the full benchmark landscape, the honest answer is that each model dominates a different category — and the smartest move is understanding exactly which one to reach for on which task.
This comparison cuts through the marketing noise. Real benchmark numbers, honest gaps, and a use-case decision matrix at the end so you know exactly when to use which model.
Meet the Three Contenders
Claude Opus 4.7 — The Coding and Agentic Specialist
Released April 16, 2026, Opus 4.7 is Anthropic’s most capable model yet. The headline improvements are in software engineering (+13% on internal coding benchmarks over Opus 4.6), a dramatic vision upgrade (3x higher resolution at 3.75 megapixels), and a new self-verification behaviour — the model checks its own outputs before reporting back. Pricing stays flat at $5/$25 per million input/output tokens.
GPT-5.4 — The Knowledge Work and Web Research Leader
OpenAI’s GPT-5.4, released March 5, 2026, made its name by becoming the first frontier model to surpass human expert performance on autonomous desktop tasks (OSWorld: 75%, human baseline: 72.4%). It also leads on GDPval, a benchmark that measures performance across 44 professional occupations spanning finance, legal, medicine, and knowledge work. It’s positioned as the professional-grade AI.
Gemini 3.1 Pro — The Value Play with a Context Window Lead
Google’s Gemini 3.1 Pro punches above its price point. At $2/$12 per million tokens — roughly 60% cheaper than Claude and 20% cheaper than GPT-5.4 — it offers a 2 million token context window, strong multilingual performance, and competitive reasoning scores. For cost-sensitive, high-volume, or long-document workloads, it’s a serious contender.
The Full Benchmark Breakdown
Here is how the three models compare across the benchmarks that matter most as of April 2026:
| Benchmark | Claude Opus 4.7 | GPT-5.4 | Gemini 3.1 Pro | Winner |
| SWE-bench Pro (Coding) | 64.3% | 57.7% | 54.2% | 🏆 Claude |
| SWE-bench Verified (Coding) | 87.6% | N/A | 80.6% | 🏆 Claude |
| GPQA Diamond (Reasoning) | 94.2% | 94.4% | 94.3% | ≈ Tie |
| CursorBench (AI Coding Editor) | 70.0% | N/A | N/A | 🏆 Claude |
| BrowseComp (Web Research) | 79.3% | 89.3% | 85.9% | 🏆 GPT-5.4 |
| OSWorld (Computer Use) | 78.0% | 75.0% | N/A | 🏆 Claude |
| MCP-Atlas (Tool Use/Agents) | 77.3% | 68.1% | 73.9% | 🏆 Claude |
| MMMLU (Multilingual) | 91.5% | N/A | 92.6% | 🏆 Gemini |
| CharXiv (Scientific Vision) | +13pts vs prev | — | — | 🏆 Claude |
| Context Window | 1M tokens | ~200K | 2M tokens | 🏆 Gemini |
| Price (Input/Output per 1M) | $5 / $25 | $2.50 / $15 | $2 / $12 | 🏆 Gemini |
Note: N/A indicates the benchmark result was not publicly reported for that model. All scores from official system cards and independent evaluations, April 2026.
Where Each Model Wins — Category by Category
Coding: Claude Wins Clearly
This is the most consequential category for most developers and AI builders, and Claude Opus 4.7 leads convincingly. On SWE-bench Pro — which tests a model’s ability to resolve real GitHub issues across production codebases — Opus 4.7 scores 64.3%, well ahead of GPT-5.4 at 57.7% and Gemini at 54.2%. On SWE-bench Verified, Opus 4.7 hits 87.6% versus Gemini’s 80.6%. GPT-5.4 has no comparable published score.
The gap is not just in raw scores. Opus 4.7 introduces self-verification — it catches its own logical faults during the planning phase and validates outputs before presenting them. For complex, multi-step coding tasks where a single error can cascade, this behaviour change is more valuable than marginal benchmark points.
Reasoning: A Three-Way Tie
GPQA Diamond, the graduate-level reasoning benchmark covering physics, chemistry, and biology, has effectively been saturated at the frontier. Opus 4.7 scores 94.2%, GPT-5.4 scores 94.4%, Gemini 3.1 Pro scores 94.3%. The 0.2-point spread is within run-to-run noise. If you need a model for complex analytical reasoning, any of the three will do the job.
Web Research: GPT-5.4 Takes This One
BrowseComp, which measures a model’s ability to synthesise information across multiple web pages, favours GPT-5.4 at 89.3%, versus Gemini at 85.9% and Claude at 79.3%. This is actually a regression for Claude — Opus 4.6 scored 83.7% on the same benchmark. If your primary use case involves heavy web research, multi-source synthesis, or research agent workflows, GPT-5.4 is the stronger choice.
Vision and Image Understanding: Claude’s Biggest Upgrade
The most underreported story in the Opus 4.7 release is the vision upgrade. Maximum image resolution jumped from 1.15 megapixels (Opus 4.6) to 3.75 megapixels — more than three times the visual capacity. On CharXiv, which tests scientific figure interpretation, Opus 4.7 improved by 13 points versus its predecessor. On XBOW visual acuity, it went from 54.5% to 98.5%.
Gemini 3.1 Pro remains competitive on video understanding — Google has invested heavily in multimodal capabilities. But for static image analysis, dense diagram reading, and document processing, Opus 4.7’s upgrade is significant.
Context Window: Gemini’s Structural Advantage
Gemini 3.1 Pro’s 2 million token context window — double what Claude offers in beta (1M) and far ahead of GPT-5.4’s ~200K — is a real structural advantage for specific workloads. If you are processing entire codebases, large document collections, or lengthy research archives in a single pass, Gemini’s context window is genuinely useful rather than a marketing number.
Cost: Gemini Wins by a Wide Margin
At $2 per million input tokens and $12 per million output tokens, Gemini 3.1 Pro is dramatically more affordable than both competitors. Claude Opus 4.7 at $5/$25 costs roughly 2.5x more per input token and 2x more per output token. GPT-5.4 sits in between at $2.50/$15. For production applications running millions of tokens per day, this pricing gap translates directly into operating costs.
The Model Behind the Curtain: Claude Mythos
Any honest comparison of frontier models in April 2026 has to acknowledge the elephant in the room. Anthropic has built a model — Claude Mythos Preview — that it has not released to the public. Mythos scores 77.8% on SWE-bench Pro versus Opus 4.7’s 64.3%. On most benchmarks, it outperforms everything discussed in this article.
Anthropic is holding Mythos back due to safety concerns, specifically around cybersecurity capabilities. The company found that Mythos could find and exploit software vulnerabilities at a level rivalling skilled security researchers. Opus 4.7 is being used as a testbed for new cybersecurity safeguards before Mythos-class capabilities are deployed more broadly.
The practical implication: the frontier is further ahead than what any consumer or enterprise buyer can currently access. Anthropic is running at a $30 billion annualised revenue rate, has attracted investor attention at roughly $800 billion, and is reportedly in early IPO talks. Opus 4.7 is the commercial model that has to justify those numbers while the more powerful model waits in the wings.
Three Practical Considerations Nobody Mentions
1. The Claude Tokenizer Change Will Raise Your Bill
Opus 4.7 ships with an updated tokenizer. The same input now maps to 1.0–1.35 times more tokens depending on content type. Code and structured data see the larger end of that range. Anthropic kept per-token pricing flat, but if your workflows are code-heavy, expect a 10–35% increase in actual token consumption. Budget accordingly before migrating.
2. Opus 4.7 Follows Instructions More Literally
Where previous models interpreted ambiguous instructions loosely, Opus 4.7 takes them literally. This is genuinely better for precise tasks — it follows explicit instructions more reliably and skips steps less often. But it also means existing prompts built around implied context or loose phrasing may need to be rewritten. If you migrate from Opus 4.6 to 4.7, audit your most important prompts first.
3. The Reasoning Benchmark Saturation Problem
All three models have converged around 94% on GPQA Diamond, the benchmark that was the gold standard for measuring genuine multi-step scientific reasoning. Benchmarks get saturated. The competitive differentiation between frontier models has shifted away from raw reasoning scores and toward applied performance on complex, multi-step tasks in real environments. A benchmark score is an indicator, not a guarantee.
The Decision Matrix: Use This, Not That
The honest answer to ‘which model should I use?’ is: it depends on the task. Here is a practical routing guide:
| Use Case | Best Model | Runner-Up | Why |
| Complex coding / agentic dev work | Claude Opus 4.7 | GPT-5.4 | Best SWE-bench scores; self-verification catches own bugs |
| Web research & multi-source synthesis | GPT-5.4 | Gemini 3.1 Pro | BrowseComp lead; stronger at multi-page synthesis |
| Analysing images, screenshots, diagrams | Claude Opus 4.7 | Gemini 3.1 Pro | 3.75MP vision; 3x resolution jump over previous models |
| Large document processing (200K+ tokens) | Gemini 3.1 Pro | Claude Opus 4.7 | 2M context window; dramatically lower cost at scale |
| Everyday writing & content creation | Claude Opus 4.7 | GPT-5.4 | Consistently preferred in blind writing evaluations |
| Graduate-level reasoning & analysis | Any of the three | — | All converge at ~94% on GPQA Diamond; effectively tied |
| High-volume API usage on a budget | Gemini 3.1 Pro | GPT-5.4 | ~$2/$12 vs $5/$25; 60–75% cheaper than Claude |
| Autonomous multi-step agent pipelines | Claude Opus 4.7 | Gemini 3.1 Pro | MCP-Atlas lead; best tool-calling and self-correction |
| Multilingual tasks | Gemini 3.1 Pro | Claude Opus 4.7 | Slight multilingual edge; strong across non-English languages |
| Professional knowledge work (legal, finance) | GPT-5.4 | Claude Opus 4.7 | Leads on GDPval across 44 occupational categories |
Bottom Line
Claude Opus 4.7 is the strongest model for coding, agentic workflows, and visual document analysis. GPT-5.4 owns web research and professional knowledge work. Gemini 3.1 Pro is the best value, with the largest context window and the lowest price per token.
No single model dominates everything. The most useful mental model is not ‘which model is best?’ but ‘which model is best for this specific task?’ The frontier models have become differentiated specialists, not general hierarchies. The smartest teams in 2026 are already routing tasks to the right model rather than defaulting to one for everything.
FAQ Schema Block
Q: Is Claude Opus 4.7 better than GPT-5.4?
It depends on the task. Claude Opus 4.7 leads on coding benchmarks and agentic tool use. GPT-5.4 leads on web research and professional knowledge work. For reasoning, they are essentially tied.
Q: Why is Gemini 3.1 Pro so much cheaper than Claude?
Google prices Gemini 3.1 Pro at $2/$12 per million tokens versus Claude’s $5/$25, likely to drive adoption and capture market share. The cost gap is real, though Claude and GPT-5.4 lead on specific high-value benchmarks that may justify the premium.
Q: What is Claude Mythos and why is it not available?
Claude Mythos Preview is Anthropic’s most powerful model, significantly outperforming Opus 4.7 on most benchmarks. Anthropic has restricted its release due to concerns about its cybersecurity capabilities — the model can identify and exploit software vulnerabilities at a level rivalling skilled security researchers.
Q: What is the xhigh effort level in Claude Opus 4.7?
xhigh is a new reasoning effort tier in Opus 4.7, slotting between the existing high and max levels. It gives developers finer control over the tradeoff between reasoning depth and response latency on hard problems.
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