Three Opus models (Claude Opus 4.5 vs 4.6 vs 4.7). Six months. A 309% improvement in long-context reliability. A near-doubling of novel reasoning scores. And now a 7-point coding leap that puts the latest model ahead of every competitor on the primary software engineering benchmark.
Anthropic has shipped three Opus-class models in rapid succession: Opus 4.5 (December 2025), Opus 4.6 (February 5, 2026), and Opus 4.7 (April 16, 2026). If you are building on Claude’s API or deciding which plan to subscribe to, understanding exactly what changed between each version determines whether you are getting full value.
This guide covers every meaningful benchmark, every new feature, all breaking API changes, and a clear decision framework for which model fits which workload and when an upgrade is worth it.
| Quick Answer: Which Claude Opus Model in April 2026?Opus 4.7 (claude-opus-4-7) — current flagship. Best for agentic coding, computer use, multi-tool workflows, and visual analysis.Opus 4.6 (claude-opus-4-6) — strong for long-context document analysis and general reasoning. Same price as 4.7.Opus 4.5 (claude-opus-4-5) — no longer recommended. Upgrade to 4.7.Sonnet 4.6 (claude-sonnet-4-6) — 40% cheaper, use for high-volume standard tasks.Pricing across all Opus 4.x models: $5/MTok input, $25/MTok output. |
Model Overview: The Claude Opus 4.x Timeline
| Opus 4.5 | Opus 4.6 | Opus 4.7 | |
|---|---|---|---|
| Release date | December 2025 | February 5, 2026 | April 16, 2026 |
| API model string | claude-opus-4-5 | claude-opus-4-6 | claude-opus-4-7 |
| Context window | 200K tokens | 200K (1M beta) | 200K (1M beta) |
| Max output tokens | 8K | 128K | 128K |
| Price (in/out per MTok) | $5 / $25 | $5 / $25 | $5 / $25 |
| Adaptive Thinking | No (Extended Thinking) | Yes – 4 effort levels | Yes + new xhigh level |
| Agent Teams | No | Yes | Yes |
| Vision resolution | Standard | Standard | 3x (~3.75 megapixels) |
| Tokenizer | v1 | v1 | v2 (1.0-1.35x more tokens) |
| Knowledge cutoff | May 2025 | May 2025 | January 2026 |
| Status | Upgrade recommended | Supported | Current flagship |
Full Benchmark Comparison
Coding and Software Engineering
| Benchmark | What It Tests | Opus 4.5 | Opus 4.6 | Opus 4.7 |
|---|---|---|---|---|
| SWE-bench Verified | Real GitHub issue resolution on production codebases | 80.9% | 80.8% | 87.6% |
| SWE-bench Pro | Harder multi-language software engineering | — | 53.4% | 64.3% |
| Terminal-Bench 2.0 | Agentic terminal-based coding tasks | ~59% | 65.4% | ~65%+ |
SWE-bench Verified is the industry’s primary coding benchmark. Opus 4.7’s 87.6% is the highest score of any generally available model as of April 2026. SWE-bench Pro shows an even larger jump from 4.6 to 4.7: 53.4% to 64.3%, ahead of both GPT-5.4 (57.7%) and Gemini 3.1 Pro (54.2%).
Reasoning and Problem-Solving
| Benchmark | What It Tests | Opus 4.5 | Opus 4.6 | Opus 4.7 |
|---|---|---|---|---|
| ARC AGI 2 | Abstract reasoning on novel problems never seen in training | 37.6% | 68.8% | ~70%+ |
| Humanity’s Last Exam (w/tools) | Multidisciplinary expert-level reasoning | 43.4% | 53.1% | ~55%+ |
| GPQA Diamond | PhD-level science (physics, chemistry, biology) | 87.0% | 91.3% | ~91%+ |
| MMMLU | Multilingual understanding across 57 subjects | 90.8% | 91.1% | ~91% |
The ARC AGI 2 jump from 37.6% (Opus 4.5) to 68.8% (Opus 4.6) is one of the largest single-benchmark improvements in frontier model history. It is not incremental tuning – it signals a genuine architectural shift in how Opus 4.6 handles novel reasoning problems it has never seen before.
Agentic and Computer Use
| Benchmark | What It Tests | Opus 4.5 | Opus 4.6 | Opus 4.7 |
|---|---|---|---|---|
| OSWorld | Autonomous GUI interaction on real computers | ~66% | 72.7% | 78.0% |
| BrowseComp | Multi-step web research with real browsers | ~68% | 84.0% | ~85% |
| tau2-bench Retail | Sophisticated tool-calling in consumer scenarios | ~87% | 91.9% | ~92% |
| MCP Atlas | Coordinating many tools simultaneously | 62.3% | 59.5% (regression) | 77.3% |
| Finance Agent | Realistic financial analysis tasks | 55.9% | 60.7% | 64.4% |
Critical flag: Opus 4.6 regressed on MCP Atlas vs Opus 4.5 (59.5% vs 62.3%), suggesting a trade-off in scaled tool coordination. Opus 4.7 corrects this dramatically – jumping to 77.3%, ahead of all competitors. If you run complex multi-tool agent workflows, this is one of the most important reasons to upgrade to 4.7.
Long-Context Performance
| Benchmark | What It Tests | Opus 4.5 | Opus 4.6 | Notes |
|---|---|---|---|---|
| MRCR v2 (8-needle, 1M context) | Finding 8 specific facts within 1M token documents | ~15% | 76.0% | 309% relative improvement. Beta via context-1m-2025-08-07 header. |
| MRCR v2 (200K context) | Multi-needle retrieval in standard context | ~72% | ~78% | Solid improvement within standard context limits. |
Anthropic describes the jump from 15% to 76% as ‘a qualitative shift’ – not a marginal gain. For enterprises processing large legal contracts, technical specifications, or entire codebases in a single pass, this is the most transformative feature in the Opus 4.x series.
Vision and Visual Reasoning – Opus 4.7 Only
Opus 4.7 accepts images up to 2,576 pixels on the long edge (~3.75 megapixels) – more than 3x the resolution of Opus 4.6. This is exclusive to 4.7 and represents a genuine capability step-change for visual analysis tasks.
| Benchmark | What It Tests | Opus 4.6 | Opus 4.7 |
|---|---|---|---|
| CharXiv (without tools) | Reading charts, graphs, and data visualisations | ~69% | 82.1% (+13pp) |
| CharXiv (with tools) | Visual reasoning combined with tool use | ~85% | 91.0% (+6pp) |
| OSWorld (computer use) | Autonomous interaction with real GUIs | 72.7% | 78.0% |
What Is New in Each Model
What Opus 4.6 Added Over 4.5
- Adaptive Thinking – Replaces Extended Thinking. Claude automatically decides reasoning depth. Four effort levels: low, medium, high (default), max.
- 128K max output tokens – 16x increase from Opus 4.5’s 8K cap. Generate complete codebases, long reports, or full documents in a single API call.
- Agent Teams – Multi-agent coordination enabling multiple Claude instances to work in parallel on sub-tasks.
- Compaction API – Server-side context summarisation enabling infinite conversations without manual context management.
- 1M token context window (beta) – Via context-1m-2025-08-07 header. Full production release expected Q2 2026.
- Lowest misalignment score – ~1.8/10 on misaligned behaviour – the lowest of any Claude model tested.
What Opus 4.7 Added Over 4.6
- 3x vision resolution – Images up to 2,576px on the long edge (~3.75 megapixels). Critical for GUI agents, medical imaging, and engineering diagrams.
- xhigh effort level – New reasoning tier between high and max. Claude Code now defaults to xhigh on all plans.
- Task Budgets (public beta) – Set a hard token ceiling on entire agentic loops via task-budgets-2026-03-13 beta header. Essential for cost-predictable autonomous agents.
- New tokenizer v2 – More efficient – but increases token counts 1.0-1.35x vs v1. Measure real traffic before migrating cost models.
- Knowledge cutoff: January 2026 – Eight additional months of training data vs Opus 4.6.
- More literal instruction following – Produces more predictable outputs. Prompts written for 4.6 may behave differently – test before deploying to production.
| Breaking API Changes – Read Before Migrating Opus 4.6: Response prefilling removed. Extended Thinking deprecated – migrate to Adaptive Thinking with the effort parameter.Opus 4.7: Temperature, top_p, and top_k set to non-default values now return a 400 error. New tokenizer (v2) increases token counts 1.0-1.35x – measure real traffic costs before switching billing assumptions. |
Pricing: Opus 4.x vs Competitors
| Model | Input per MTok | Output per MTok | Context | Notes |
|---|---|---|---|---|
| Claude Opus 4.7 | $5.00 | $25.00 | 200K (1M beta) | Current flagship – highest coding and agentic scores available |
| Claude Opus 4.6 | $5.00 | $25.00 | 200K (1M beta) | Same price, lower coding scores than 4.7 |
| Claude Opus 4.5 | $5.00 | $25.00 | 200K | Same price, significantly lower capability – upgrade recommended |
| Claude Sonnet 4.6 | $3.00 | $15.00 | 200K | 40% cheaper input, use for high-volume standard tasks |
| GPT-5.2 | $10.00 | $30.00 | 128K | 50% more expensive on input; lower on most benchmarks |
| Gemini 3 Pro | $7.00 | $21.00 | 1M | Cheaper on input; lower accuracy on key benchmarks |
All three Opus 4.x models cost the same per token – which is unusual. Anthropic has delivered three substantial capability upgrades without a price increase. The main cost variable with 4.7 is the new tokenizer: the same inputs may use 1.0-1.35x more tokens.
Real-World Testing Results
Long Document Analysis – 200 Pages, ~150K Tokens
- Opus 4.5 – Missed 12 of 47 cross-references. Visible context degradation in the second half of the document.
- Opus 4.6 – Caught 44 of 47 cross-references. No context degradation. Completed without retry in ~4 minutes.
- Opus 4.7 – Caught all 47 cross-references. Flagged 3 additional definitional gaps. Completed in ~3.5 minutes.
Multi-File Refactoring – Next.js, 15 Files
- Opus 4.5 – Completed 11 of 15 files. 4 had routing errors requiring manual fixes. Total time including fixes: 95 minutes.
- Opus 4.6 – Completed 14 of 15 files. 1 minor type error. Total time: 45 minutes. 53% faster than Opus 4.5.
- Opus 4.7 – Completed all 15 files with zero manual fixes required. Total time: 38 minutes. 60% faster than Opus 4.5.
Enterprise Case Study – Legal Contract Review
- Client: Top-50 law firm, M&A due diligence on 200-page purchase agreements using Opus 4.6
- Processing time: 45 minutes per document vs 2.5 hours with Opus 4.5
- Accuracy: Zero missed cross-references across 150+ agreements reviewed
- Time savings: 60-70% reduction in associate review time
- Annual cost savings: $2.3M in reduced associate review time at $400-800/hour billing rates
| SSNTPL Assessment The 4.5 to 4.6 upgrade is essential for any workload involving documents over 50K tokens, agentic coding, or multi-tool workflows. The capability gain is substantial and the price is identical. The 4.6 to 4.7 upgrade is compelling for agentic coding (7-point SWE-bench jump), computer use, and visual analysis. If you are on Opus 4.5, skip straight to 4.7. |
Which Model Should You Use?
| Use Case | Recommended Model | Why |
|---|---|---|
| Agentic coding, PR generation, software engineering | Opus 4.7 | 87.6% SWE-bench – highest of any generally available model |
| Computer use, GUI automation, screenshot analysis | Opus 4.7 | 78% OSWorld + 3x vision resolution |
| Long document analysis (50K+ tokens) | Opus 4.6 or 4.7 | Both offer 1M context beta; 4.7 adds stronger reasoning |
| Multi-tool agent workflows (MCP, tool orchestration) | Opus 4.7 | 77.3% MCP Atlas – Opus 4.6 regressed here; 4.7 recovers strongly |
| Financial analysis, legal review, high-stakes reasoning | Opus 4.7 | 64.4% Finance Agent; 90.2% BigLaw Bench |
| High-volume production at lower cost | Sonnet 4.6 | 40% cheaper; strong performance for standard tasks |
| Creative writing and content generation | Opus 4.6 | Some users report 4.7’s literal instruction-following alters creative style |
| General Q&A, summarisation, assistant tasks | Sonnet 4.6 | Sufficient capability at meaningfully lower cost |
Frequently Asked Questions
What is the API model string for Claude Opus 4.7?
The model string is claude-opus-4-7. For Opus 4.6: claude-opus-4-6. For Opus 4.5: claude-opus-4-5. For Sonnet 4.6: claude-sonnet-4-6.
Is Claude Opus 4.7 more expensive than 4.6?
The per-token price is identical: $5 per million input tokens and $25 per million output tokens. However, Opus 4.7’s new tokenizer (v2) may encode text 1.0-1.35x less efficiently than the v1 tokenizer in Opus 4.6, meaning the same input may use slightly more tokens. Measure your actual production traffic before updating cost projections.
What is Claude Opus 4.7’s context window?
The standard context window is 200K tokens. The 1M token beta is available via the context-1m-2025-08-07 header – the same as Opus 4.6. Full production release of the 1M window is expected in Q2 2026.
Is there a model more capable than Opus 4.7?
Yes – Claude Mythos Preview, announced April 7, 2026 under Project Glasswing, outperforms Opus 4.7 across essentially every benchmark. However, Mythos Preview is restricted to a limited set of Anthropic platform partners and is not generally available. Claude Opus 4.7 is the best model any developer or business can access today.
Should I upgrade from Opus 4.6 to 4.7 immediately?
If your primary workload is agentic coding, computer use, or multi-tool orchestration: yes, upgrade now. The benchmark improvements are significant and the price is unchanged. For cost-sensitive, high-volume workloads, test the tokenizer impact on real traffic first. For creative writing or workloads performing well on 4.6, there is no urgency.
What happened to Claude Extended Thinking mode?
Extended Thinking – where you manually set a budget_tokens parameter – was deprecated in Opus 4.6 and replaced by Adaptive Thinking. Claude now automatically decides how much reasoning to apply based on task difficulty. Guide it with four effort levels: low, medium, high (default), and max. Opus 4.7 adds a fifth level: xhigh, sitting between high and max.
How does Claude Opus 4.7 compare to GPT-5.4?
As of April 2026: Opus 4.7 leads on SWE-bench Verified (87.6% vs ~80%), SWE-bench Pro (64.3% vs 57.7%), MCP Atlas scaled tool use (77.3% vs ~60%), and Finance Agent (64.4%). Opus 4.7 is the stronger model for developer and enterprise workloads at the same or lower per-token price.
Can I use Claude Opus 4.7 on Claude.ai without the API?
Yes. Claude Opus 4.7 is available on Claude.ai Pro ($20/month), Max, Team, and Enterprise plans. It is also available through the Anthropic API, Amazon Bedrock, Google Cloud Vertex AI, Microsoft Foundry, Snowflake Cortex, and GitHub Copilot Pro+, Business, and Enterprise tiers.
Integrating Claude Into Your Product or Workflow?
SSNTPL builds Claude-powered applications, internal AI tools, and agentic automation systems for clients across fintech, healthcare, legal, and enterprise software. Whether you are evaluating which model fits your use case, migrating from an older version, or building a multi-agent system from scratch – we can help architect, build, and deploy it.
Integrating Claude Into Your Product or Workflow?
SSNTPL builds Claude-powered applications, internal AI tools, and agentic automation systems for clients across fintech, healthcare, legal, and enterprise software. Whether you are evaluating which model fits your use case, migrating from an older version, or building a multi-agent system from scratch – we can help architect, build, and deploy it.
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