Anthropic released Claude Opus 4.7 on April 16, 2026, just two months after Opus 4.6 hit the market. The new model delivers a massive 6.8-point jump on SWE-bench Verified (87.6% vs 80.8%), introduces 3.75-megapixel vision, and adds adaptive thinking. But it also ships with a new tokenizer that silently raises your API bill by 10–35%.
If you are choosing between Claude Opus 4.7, Opus 4.6, and Sonnet 4.6 right now, the decision is more nuanced than ever. All three models excel at different things, and none of them is the obvious default for every workload. This guide breaks down the benchmarks, pricing, strengths, and weaknesses of each model so you can pick the right one for your specific needs.
Quick Comparison Overview
| Feature | Claude Sonnet 4.6 | Claude Opus 4.6 | Claude Opus 4.7 |
|---|---|---|---|
| Release Date | Feb 17, 2026 | Feb 5, 2026 | Apr 16, 2026 |
| SWE-bench Verified | 79.6% | 80.8% | 87.6% |
| SWE-bench Pro | ~53% | 53.4% | 64.3% |
| GPQA Diamond | 74.1% | 91.3% | 94.2% |
| CursorBench | N/A | 58% | 70% |
| Visual Acuity (XBOW) | N/A | 54.5% | 98.5% |
| Input Price / MTok | 5 | ||
| Output Price / MTok | 5 | 5 | 5 |
| Context Window | 200K | 1M (beta) | 1M |
| Max Output Tokens | 64K | 64K | 128K |
| Agent Teams | No | Yes | Yes |
| Extended Thinking | No | Yes (budget mode) | Yes (adaptive only) |
| Vision Resolution | ~1568px | ~1568px | 2576px (3.75 MP) |
Benchmark Deep Dive
Software Engineering (SWE-bench)
This is the benchmark that matters most for developers. It measures a model’s ability to resolve real GitHub issues end-to-end, from understanding the bug to writing and validating the fix.
Opus 4.7 is the clear winner. Its 87.6% on SWE-bench Verified and 64.3% on SWE-bench Pro represent the highest scores of any publicly available model. The 10.9-point jump on SWE-bench Pro from Opus 4.6 means Opus 4.7 now solves roughly 3x more production coding tasks than its predecessor. CursorBench (which measures IDE-integrated coding quality) jumped from 58% to 70%.
Sonnet 4.6’s 79.6% is only 1.2 points behind Opus 4.6, but the gap between Sonnet and Opus 4.7 is a substantial 8 points. For teams using Claude Code or Cursor, Opus 4.7 is the single biggest quality-of-life upgrade since Claude 4 launched.
Science and Expert Reasoning (GPQA Diamond)
GPQA Diamond tests PhD-level science reasoning across physics, chemistry, and biology. This is where the model hierarchy is most clear.
- Opus 4.7: 94.2% — the highest score of any model
- Opus 4.6: 91.3% — still exceptional
- Sonnet 4.6: 74.1% — a 17+ point gap from Opus 4.7
If your work involves advanced scientific reasoning, medical or legal analysis, or complex multi-hop domain questions, Opus 4.7 is in a fundamentally different league. Sonnet 4.6 is not competitive here.
Vision and Multimodal
The visual improvement in Opus 4.7 is dramatic. Visual acuity jumped from 54.5% to 98.5% — nearly perfect. The model now supports images up to 2,576 pixels on the long edge (3.75 megapixels, up from 1.15 MP), with 1:1 pixel coordinate mapping.
This means screenshot analysis, document OCR, UI testing, computer-use workflows, and chart/data extraction get substantially better. If you rely on Claude for visual tasks, Opus 4.7 is the only viable choice.
Long Context Retrieval — The Caveat
There is one area where Opus 4.7 regressed significantly: long context retrieval. The MRCR v2 benchmark (which tests information retrieval within a 1M-token context) dropped from 78.3% on Opus 4.6 to just 32.2% on Opus 4.7. This is a massive backward step.
If your workflow involves analyzing 100+ page documents, massive codebases, or any task requiring reliable information retrieval across very long contexts, Opus 4.6 remains the better choice. Anthropic has acknowledged this trade-off as a consequence of the new tokenizer and adaptive thinking architecture.
Pricing: The Hidden Cost Increase
Anthropic announced that Opus 4.7 pricing is unchanged from Opus 4.6: per million input tokens, 5 per million output tokens. However, there is an important catch.
Opus 4.7 uses a new tokenizer that maps the same input to 1.0–1.35x more tokens. A prompt that used 10,000 tokens on Opus 4.6 now uses 10,000–13,500 tokens on Opus 4.7. Your effective cost increases by up to 35% even though the per-token rate is identical.
Here is what that looks like in practice for a medium workload (50K API calls per day):
- Monthly tokens on Opus 4.6: ~750M
- Monthly tokens on Opus 4.7: ~825M–1B
- Actual cost increase: 10–33%
To mitigate this, Anthropic introduced a new effort parameter with levels: low, medium, high, xhigh (new), and max. Setting effort to high instead of xhigh significantly reduces token usage while keeping quality above Opus 4.6 levels on most tasks.
Cost Comparison Across All Three Models
| Scenario (Monthly) | Sonnet 4.6 | Opus 4.6 | Opus 4.7 |
|---|---|---|---|
| Solo dev (3K requests/mo) | 78 | ,890 | 15–00 |
| Small team (15K requests/mo) | ,890 | ,450 | ,080–,500 |
| Startup (30K requests/mo) | ,780 | 8,900 | ,150–,000 |
| Enterprise (300K requests/mo) | 7,800 | 89,000 | 1,500–0,000 |
Sonnet 4.6 remains the most cost-effective option by a wide margin. Opus 4.7 is 5x cheaper than Opus 4.6 per-token, but the new tokenizer narrows the gap somewhat in real-world usage.
Pros and Cons of Each Model
Claude Sonnet 4.6
Pros:
- Best price-to-performance ratio in the Claude lineup
- 79.6% SWE-bench is competitive with every previous Opus model
- Fast response times ideal for interactive coding
- 72.5% on OSWorld-Verified matches Opus 4.6 (72.7%) for computer-use tasks at 1/5 the cost
- Strong instruction following — 70% of testers preferred it over Sonnet 4.5
- Available on all Claude plans including free tier
Cons:
- No Agent Teams support
- No extended thinking capability
- 74.1% GPQA Diamond — weak for expert-level scientific reasoning
- 200K context only (no 1M beta)
Claude Opus 4.6
Pros:
- Best long-context retrieval in the Claude family (78.3% MRCR v2)
- Agent Teams for parallel multi-agent workflows
- Extended thinking with configurable budget tokens
- 91.3% GPQA Diamond for expert reasoning
- Found 500+ unknown security vulnerabilities in pre-release testing
Cons:
- 5x more expensive than Sonnet 4.6 per token
- 80.8% SWE-bench is now significantly behind Opus 4.7 (87.6%)
- Lower max output (64K vs 128K in 4.7)
- Slower response times, especially with extended thinking
- 54.5% visual acuity is poor compared to Opus 4.7 (98.5%)
Claude Opus 4.7
Pros:
- Highest coding scores of any public model (87.6% SWE-bench, 70% CursorBench)
- Near-perfect vision (98.5% acuity, 3.75 MP resolution)
- Best expert reasoning (94.2% GPQA Diamond)
- 128K max output tokens — double the previous limit
- Adaptive thinking with fine-grained effort control (including new xhigh level)
- Task budgets for agentic loops (beta)
- Improved file-system memory for long-running agent sessions
- Only 1/3 the cost of Opus 4.6 per token
Cons:
- Catastrophic regression in long-context retrieval (32.2% vs 78.3%)
- New tokenizer increases real-world costs by 10–35%
- Breaking API changes (temperature, top_p, top_k, and thinking budgets all removed)
- Some users report less human writing style compared to 4.6
- No budget mode for extended thinking — adaptive only
Who Should Use Each Model?
Choose Claude Sonnet 4.6 If:
- You do everyday coding — writing functions, fixing bugs, implementing features, writing tests. The 1.2-point SWE-bench gap from Opus 4.6 is imperceptible in daily work.
- You need computer use / GUI automation. At 72.5% vs 72.7%, Sonnet matches Opus 4.6 for a fraction of the cost.
- You are cost-sensitive. For high-volume API usage, Sonnet saves 50K+ annually at enterprise scale compared to Opus.
- You need fast, interactive responses. Sonnet’s speed advantage compounds across a full day of coding.
- You are a solo developer or small team where every dollar matters.
Choose Claude Opus 4.6 If:
- You rely on long-context analysis — processing 100+ page documents, massive codebases, or cross-referencing large volumes of information. The 78.3% MRCR v2 score is dramatically better than Opus 4.7’s 32.2%.
- You need Agent Teams for parallel multi-agent workflows (building complex systems with coordinated Claude instances).
- You do deep security audits requiring the model to trace vulnerabilities across entire codebases.
- You prefer budget-based extended thinking for fine control over reasoning depth.
Choose Claude Opus 4.7 If:
- You do complex software engineering — multi-file refactors, system-level development, and production bug fixing. The 87.6% SWE-bench and 64.3% SWE-bench Pro scores are unmatched.
- You work with Claude Code or Cursor for IDE-integrated coding. The 70% CursorBench score and new /ultrareview command make this a no-brainer.
- You need vision capabilities — screenshot analysis, UI testing, document OCR, chart reading. The 98.5% visual acuity and 3.75 MP resolution are transformative.
- You build AI agents. Task budgets, improved file-system memory, and 128K output tokens make Opus 4.7 the best Claude model for agentic workflows.
- You need expert-level reasoning for research, legal analysis, or financial modeling. The 94.2% GPQA Diamond is the highest score ever recorded.
The Smart Routing Strategy
The optimal approach in 2026 is not choosing one model. It is routing each task to the right model. Here is a practical routing framework:
- Default to Sonnet 4.6 for 80–90% of tasks (bug fixes, feature implementation, tests, computer use, content generation).
- Upgrade to Opus 4.7 for complex coding tasks, visual analysis, agent workflows, and anything requiring deep reasoning.
- Use Opus 4.6 only when you specifically need long-context retrieval or budget-based extended thinking.
A 90/10 routing split (90% Sonnet, 10% Opus 4.7) reduces your Claude API bill by roughly 70% compared to running everything on Opus 4.6, while capturing most of Opus 4.7’s quality gains where they matter most.
Migration Notes for Opus 4.7
If you are upgrading from Opus 4.6 to Opus 4.7, be aware of these breaking changes:
- Extended Thinking Budgets removed: Replace
thinking.budget_tokenswiththinking.type: adaptiveplus the neweffortparameter. - Sampling parameters removed:
temperature,top_p, andtop_know return 400 errors if set to non-default values. Use prompting to guide behavior instead. - Thinking content hidden by default: Add
thinking.display: summarizedif you need to see thinking blocks in streaming responses. - Update max_tokens by 35% to account for the new tokenizer.
Final Verdict
Claude Sonnet 4.6 remains the best default choice for most developers. It delivers 98% of Opus 4.6’s coding performance at one-fifth the cost, and its speed makes it ideal for interactive workflows.
Claude Opus 4.6 is now a niche tool — but an important one. Its unmatched long-context retrieval (78.3% MRCR v2) and Agent Teams capability make it indispensable for specific workloads that Opus 4.7 cannot handle.
Claude Opus 4.7 is the most capable Claude model ever released and the best coding model available from any provider. Its 87.6% SWE-bench, 98.5% visual acuity, and adaptive thinking make it the obvious choice for professional developers, AI agent builders, and anyone working on complex software engineering tasks. The hidden cost increase from the new tokenizer is real but manageable with the effort parameter.
The bottom line: use Sonnet 4.6 as your daily driver, upgrade to Opus 4.7 for hard problems, and keep Opus 4.6 in your back pocket for long-context tasks. This three-model routing strategy gives you the best balance of quality, cost, and capability available in 2026.