Compare Claude Opus 4.7 against GPT-5, Gemini 2.5, Llama 4 and other top AI models. Interactive benchmarks, pricing calculator and feature breakdown.
Last updated: April 18, 2026
Scores represent publicly reported results as of April 2026. Higher is better.
Released April 16, 2026 — Anthropic's most capable model to date.
Opus 4.7 can autonomously plan, execute, and iterate on complex multi-step tasks. Improved tool use reliability and parallel function calling.
74.2% on SWE-bench Verified, up from 64.5% in Opus 4.6. Better at large codebase navigation, debugging, and multi-file edits.
Second-generation extended thinking with more transparent reasoning chains. Configurable thinking budgets up to 128K tokens.
Significantly better at following complex, multi-constraint instructions. Reduced over-refusal rates while maintaining safety.
Native PDF analysis with layout awareness. Enhanced image understanding for charts, diagrams, and handwriting recognition.
Built-in conversation memory across sessions. Custom style and preference retention for API users.
40% reduction in hallucination rate compared to Opus 4.6. Better calibrated confidence and more frequent "I don't know" responses.
Can execute multiple tool calls simultaneously, dramatically improving speed for agentic workflows. Up to 8 parallel calls per turn.
| Feature | Opus 4.6 | Opus 4.7 | Change |
|---|---|---|---|
| SWE-bench Verified | 64.5% | 74.2% | +9.7% |
| MMLU | 90.8% | 93.4% | +2.6% |
| HumanEval | 93.7% | 96.1% | +2.4% |
| MATH | 87.2% | 91.8% | +4.6% |
| TAU-bench | 62.3% | 71.5% | +9.2% |
| Context Window | 200K | 200K | Same |
| Max Output | 32K | 64K | 2x |
| Extended Thinking | v1 | v2 | Upgraded |
| Parallel Tool Calls | 4 | 8 | 2x |
| Price (Input/MTok) | $15 | $15 | Same |
| Price (Output/MTok) | $75 | $75 | Same |
| Spec | Claude Opus 4.7 | Claude Sonnet 4.6 | GPT-5 | GPT-4o | Gemini 2.5 Pro | Llama 4 Maverick |
|---|---|---|---|---|---|---|
| Provider | Anthropic | Anthropic | OpenAI | OpenAI | Meta | |
| Release | Apr 2026 | Oct 2025 | Mar 2026 | May 2024 | Mar 2025 | Apr 2025 |
| Context | 200K | 200K | 128K | 128K | 1M | 1M |
| Max Output | 64K | 16K | 32K | 16K | 32K | 16K |
| MMLU | 93.4% | 89.2% | 92.8% | 87.2% | 91.5% | 88.5% |
| HumanEval | 96.1% | 92.0% | 94.5% | 90.2% | 92.8% | 88.7% |
| SWE-bench | 74.2% | 64.5% | 65.8% | 48.3% | 63.2% | 52.1% |
| MATH | 91.8% | 86.5% | 90.5% | 76.6% | 89.2% | 82.3% |
| TAU-bench | 71.5% | 62.8% | 66.2% | 45.2% | 60.5% | 48.9% |
| GPQA Diamond | 78.3% | 68.2% | 76.8% | 53.6% | 74.5% | 62.4% |
| Vision | Yes | Yes | Yes | Yes | Yes | Yes |
| Tool Use | Yes (8x parallel) | Yes (4x) | Yes | Yes | Yes | Yes |
| Open Source | No | No | No | No | No | Yes |
| Input $/MTok | $15 | $3 | $30 | $2.50 | $1.25 | Free* |
| Output $/MTok | $75 | $15 | $60 | $10 | $10 | Free* |
* Llama 4 is open source — free to self-host; hosted API pricing varies by provider.
Estimate your monthly API cost across different models.
Context window determines how much text a model can process in a single request. Larger windows enable longer documents and conversations.
| Token Count | Approximate Equivalent |
|---|---|
| 1,000 tokens | ~750 words or ~1.5 pages |
| 32,000 tokens | ~24,000 words or ~50 pages |
| 128,000 tokens | ~96,000 words or ~200 pages (a novel) |
| 200,000 tokens | ~150,000 words or ~300 pages |
| 1,000,000 tokens | ~750,000 words or ~1,500 pages |
How much text each model can generate in a single response.