Three frontier AI models released within six weeks of each other — and none of them wins everything. Here's the honest breakdown of where each model dominates, where it falls short, and which one you should actually use.
April 2026 is shaping up to be the most consequential month in AI model releases since the original GPT-4 launch. OpenAI dropped GPT-5.4 on March 5, Google followed with Gemini 3.1 Pro, and Anthropic fired back with Claude Opus 4.7 on April 16. Meanwhile, Anthropic previewed Claude Mythos — a 10-trillion-parameter behemoth still in limited access.
If you're trying to decide which model to build on, the answer isn't one winner. It depends entirely on what you're doing. We cut through the marketing to break down the real numbers.
The Three Contenders at a Glance
| Claude Opus 4.7 | GPT-5.4 | Gemini 3.1 Pro | |
|---|---|---|---|
| Released | April 16, 2026 | March 5, 2026 | March 2026 |
| Price (in/out per 1M tokens) | $5 / $25 | $2.50 / $15 | $2 / $12 |
| Context Window | 1M tokens | ~200K tokens | 2M tokens |
| Best For | Coding, agents, vision | Web research, knowledge work | Cost efficiency, long documents |
Coding and Software Engineering: Claude Leads
For developers, this is the category that matters most. Claude Opus 4.7 dominates across every coding benchmark:
- SWE-bench Pro: Claude 64.3% vs GPT-5.4 at 57.7% vs Gemini at 54.2%
- SWE-bench Verified: Claude 87.6% vs Gemini 80.6% (GPT-5.4 unreported)
- CursorBench (AI coding editor): Claude 70.0% — the only model with a published score
The difference isn't just raw benchmark numbers. Opus 4.7 introduces self-verification: the model checks its own outputs before presenting results. For multi-step coding tasks where one error cascades into a dozen, this behavior change matters more than a few percentage points.
Winner: Claude Opus 4.7
Reasoning: Effective Three-Way Tie
On GPQA Diamond (graduate-level physics, chemistry, biology reasoning), all three models cluster within a 0.2-point margin:
- GPT-5.4: 94.4%
- Gemini 3.1 Pro: 94.3%
- Claude Opus 4.7: 94.2%
That spread is within run-to-run variance. For complex analytical reasoning, any of these three gets the job done.
Winner: Tie
Web Research: GPT-5.4 Surges Ahead
BrowseComp measures how well a model synthesizes information across multiple web sources:
- GPT-5.4: 89.3%
- Gemini 3.1 Pro: 85.9%
- Claude Opus 4.7: 79.3%
Notably, Claude actually regressed here — Opus 4.6 scored 83.7% on the same benchmark. If your workflow involves heavy web research, multi-source synthesis, or research agent pipelines, GPT-5.4 is the clear choice.
Winner: GPT-5.4
Vision: Claude's Quiet Massive Upgrade
The most underreported improvement 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 — over 3x the detail. On CharXiv (scientific document understanding), Opus 4.7 gains 13 points over the previous generation.
For document analysis, chart reading, and image-heavy workflows, this matters more than raw text benchmarks.
Winner: Claude Opus 4.7
Tool Use and Agentic Tasks
On MCP-Atlas (measuring tool use and multi-step agent workflows):
- Claude Opus 4.7: 77.3%
- Gemini 3.1 Pro: 73.9%
- GPT-5.4: 68.1%
Combined with its computer-use capabilities (OSWorld: 78.0%), Claude is the strongest choice for building autonomous agents and tool-calling workflows.
Winner: Claude Opus 4.7
Cost Efficiency: Gemini Runs Away With It
For production applications processing millions of tokens daily, the price gap is significant:
- Gemini 3.1 Pro at $2/$12 is roughly 60% cheaper than Claude and 20% cheaper than GPT-5.4
- Add in the 2-million-token context window and Gemini becomes the obvious choice for high-volume, long-document workloads
- Claude Mythos (preview only) is priced at $25/$125 — 5x above Opus — positioning it firmly as an enterprise/cybersecurity specialist, not a general-purpose model
Winner: Gemini 3.1 Pro
Multilingual Performance
On MMMLU (multilingual reasoning):
- Gemini 3.1 Pro: 92.6%
- Claude Opus 4.7: 91.5%
- GPT-5.4: Not reported
Winner: Gemini 3.1 Pro
Decision Matrix: Which Model to Use When
| Use Case | Best Model | Why |
|---|---|---|
| Software engineering, code review | Claude Opus 4.7 | Best coding benchmarks + self-verification |
| Building AI agents / tool use | Claude Opus 4.7 | Highest MCP-Atlas score, strong computer use |
| Web research, synthesis | GPT-5.4 | BrowseComp leader by wide margin |
| Document/image analysis | Claude Opus 4.7 | 3.75MP vision, strong scientific understanding |
| High-volume production APIs | Gemini 3.1 Pro | 60% cheaper, 2M context window |
| Multilingual applications | Gemini 3.1 Pro | Best MMMLU score |
| General reasoning | Any of the three | Within 0.2 points on GPQA Diamond |
The Bigger Picture: Mythos on the Horizon
While these three models compete for the general-purpose crown, Anthropic's Claude Mythos preview looms. With 10 trillion parameters and a reported ability to identify thousands of zero-day vulnerabilities across major operating systems (Project Glasswing), Mythos targets enterprise cybersecurity and high-stakes reasoning. At $25/$125 per million tokens, it's not competing with the models above — it's a different category entirely.
What Mythos signals is that the frontier model race isn't slowing down. Six weeks, three major releases, and a fourth in preview. The right strategy isn't picking one winner — it's building flexible infrastructure that can swap models based on the task.
Bottom Line
- Building software? Claude Opus 4.7
- Research agent? GPT-5.4
- Watching costs or going long-context? Gemini 3.1 Pro
- Need everything? Use all three — each dominates its lane