Two of the most capable AI models available right now both landed in April 2026 within days of each other. OpenAI released GPT-5.5 on April 23, 2026, pitching it as a model for agentic coding, computer use, and long-horizon knowledge work. Anthropic released Claude Opus 4.7 on April 16, 2026, with a focus on autonomous software engineering, near-perfect vision, and structured enterprise tasks. If you are choosing between these two for your workflow, this comparison covers pricing, benchmarks, features, and who each model actually serves best.
Quick verdict: GPT-5.5 leads on agentic computer use and broad task generalization; Claude Opus 4.7 leads on coding accuracy, high-resolution vision, and predictable enterprise-grade outputs.
What Is GPT-5.5?
GPT-5.5 is OpenAI's latest flagship model, released on April 23, 2026 under the internal codename "Spud." It arrived seven weeks after GPT-5.4 and is OpenAI's fastest-ever iteration cadence within a model family. The model is designed around a core idea: give it a messy, multi-part task and let it plan, use tools, check its own work, and keep going until the job is done with minimal hand-holding.
GPT-5.5 ships in two consumer variants — GPT-5.5 Thinking and GPT-5.5 Pro. Thinking is available to all paid ChatGPT tiers (Plus at $20/month, Pro at $200/month, Business, and Enterprise). GPT-5.5 Pro, designed for harder questions and higher-accuracy work, is limited to Pro, Business, and Enterprise subscribers. In Codex, it supports a 400K context window across Plus, Pro, Business, Enterprise, Edu, and Go plans. API pricing is set at $5 per million input tokens and $30 per million output tokens, with a 1M context window. A premium GPT-5.5 Pro API tier is available at $30 per million input tokens and $180 per million output tokens.
OpenAI's benchmark framing is confident. According to Artificial Analysis, GPT-5.5 tops the AI Intelligence Index by three points at launch, breaking a previous three-way tie with Anthropic and Google. It scores 82.7% on Terminal-Bench 2.0 and 51.7% on FrontierMath (levels 1–3).
What Is Claude Opus 4.7?
Claude Opus 4.7 is Anthropic's most capable generally available model, released on April 16, 2026. It is a direct upgrade to Opus 4.6 at the same sticker price — $5 per million input tokens and $25 per million output tokens — but ships with meaningfully different performance, particularly on coding and vision tasks. Anthropic describes it as "highly autonomous" and built for long-horizon agentic work, knowledge tasks, and high-fidelity image analysis.
The headline benchmark improvement is significant: 87.6% on SWE-bench Verified, up from 80.8% on Opus 4.6. Vision accuracy jumped from 54.5% to 98.5%, and the model now supports images up to 2,576 pixels on the long edge — more than 3x the resolution ceiling of prior Claude models. Opus 4.7 also introduces a new xhigh effort level, task budgets in public beta, and an /ultrareview command in Claude Code for deeper automated review passes.
Opus 4.7 is available through the Anthropic API, Amazon Bedrock, Google Cloud Vertex AI, Microsoft Foundry, GitHub Copilot, and Claude's consumer apps. One practical caveat: the model uses an updated tokenizer that can produce up to 35% more tokens from the same input text compared to Opus 4.6, meaning real-world costs may be higher than the list price suggests despite the unchanged rate card.
Feature Comparison
| Feature | GPT-5.5 | Claude Opus 4.7 |
|---|---|---|
| Release Date | April 23, 2026 | April 16, 2026 |
| API Input Price | $5 / MTok | $5 / MTok |
| API Output Price | $30 / MTok | $25 / MTok |
| Context Window | 1M tokens (API); 400K (Codex) | 1M tokens |
| Free Plan | No (paid tiers only) | No (paid tiers only) |
| Best For | Agentic computer use, broad task execution | Agentic coding, vision, enterprise workflows |
| Coding Benchmark | 82.7% Terminal-Bench 2.0 | 87.6% SWE-bench Verified |
| Vision Support | Multimodal (standard resolution) | High-res up to 3.75MP (98.5% visual-acuity) |
| Agentic Features | Workspace agents via Codex; computer use | Task budgets, xhigh effort, /ultrareview, file-system memory |
| Output Verification | Self-checks during task execution | Proactively writes tests and verifies before reporting |
| Integrations | ChatGPT, Codex, API, Cursor, Windsurf, Vercel | Anthropic API, Bedrock, Vertex AI, Foundry, GitHub Copilot |
| Learning Curve | Moderate — prompt-flexible, tool-heavy | Moderate — literal instruction-following requires precise prompts |
| Mobile App | Yes (ChatGPT iOS & Android) | Yes (Claude iOS & Android) |
Pricing in Detail
Both models share a $5 per million input token rate at the API level, but they diverge on output: GPT-5.5 charges $30 per million output tokens versus Claude Opus 4.7's $25. For output-heavy workflows — long documents, detailed code reviews, agentic loops — that $5 per million difference compounds quickly at scale.
GPT-5.5 also counters that with a token-efficiency argument. OpenAI claims the model completes many tasks using fewer tokens than GPT-5.4, meaning the higher per-token rate does not always translate to a higher bill. Claude Opus 4.7 faces the inverse problem: its new tokenizer can inflate token counts by up to 35% on identical input compared to Opus 4.6, making effective cost harder to predict without real-traffic testing.
| Plan / Tier | GPT-5.5 | Claude Opus 4.7 |
|---|---|---|
| Consumer Subscription | ChatGPT Plus ($20/mo), Pro ($200/mo), Business, Enterprise | Claude Pro ($20/mo), Team, Enterprise |
| Model Access | GPT-5.5 Thinking: Plus+; GPT-5.5 Pro: Pro+ | Opus 4.7: all paid plans via model picker |
| API Input (standard) | $5.00 / MTok | $5.00 / MTok |
| API Output (standard) | $30.00 / MTok | $25.00 / MTok |
| Premium API Tier | GPT-5.5 Pro: $30 input / $180 output per MTok | Claude Mythos Preview (separate, not Opus 4.7) |
| Batch Discount | 50% off standard API rate | 50% off with batch processing |
| Caching Discount | Cached input at $0.50 / MTok | Up to 90% savings with prompt caching |
| Fast Mode | 1.5x faster at 2.5x standard cost (Codex) | Fast mode available on Opus 4.6 (not yet 4.7) |
Coding Performance
This is where the comparison gets most practically useful. Both models are explicitly designed for agentic coding, but they approach it differently. Claude Opus 4.7 scores 87.6% on SWE-bench Verified and 70% on CursorBench — a coding-specific benchmark that measures real task completion in IDE environments. Anthropic notes that Opus 4.7 "devises ways to verify its own outputs before reporting back," writing tests and running sanity checks proactively rather than declaring a task done.
GPT-5.5 scores 82.7% on Terminal-Bench 2.0 and integrates deeply with Codex, OpenAI's cloud coding agent. It handles browser interaction, file operations, and multi-step refactors as part of its Codex deployment, with workspace agents that run in the cloud and continue working when the user is away. OpenAI claims approximately 40% fewer output tokens per Codex task compared to GPT-5.4, making it more practical for long coding sessions at scale.
For pure coding accuracy on hard software engineering tasks, Opus 4.7's SWE-bench lead is meaningful. For teams already embedded in the OpenAI ecosystem or building against Codex's agentic computer-use infrastructure, GPT-5.5 is the more natural fit.
Vision and Multimodal Capabilities
Claude Opus 4.7 made a substantial leap in this category. It now supports images up to 2,576 pixels on the long edge (approximately 3.75 megapixels), more than three times the resolution limit of Opus 4.6. XBOW's benchmark data puts Opus 4.7's visual-acuity at 98.5%, compared to 54.5% for its predecessor. This opens practical workflows that were previously unreliable: reading small text in dense screenshots, analyzing detailed architecture diagrams, parsing high-resolution UI mockups, and extracting data from contract scans.
GPT-5.5 supports multimodal inputs and performs well on standard vision tasks, but does not match Opus 4.7's combination of resolution ceiling and accuracy score. If high-resolution image analysis is a primary requirement — particularly for automated document processing or visual pen-testing — Opus 4.7 holds a clear advantage here as of April 2026.
Agentic and Long-Horizon Task Execution
Both models are designed for multi-step autonomous work, and both make meaningful architectural commitments to it. GPT-5.5 slots into OpenAI's workspace agents infrastructure, introduced one day before the model launch. These Codex-powered cloud agents can take on long-running workflows, connect to apps, retain memory across sessions, and continue working without the user present. The model's computer-use capability — browsing, clicking, capturing screenshots, iterating — extends this further.
Claude Opus 4.7 takes a different approach. Task budgets give the model a token target for a full agentic loop, allowing it to prioritize and finish gracefully as the budget is consumed. File-system-based memory enables coherent multi-session work without re-establishing context each run. The xhigh effort level adds a new point on the quality-versus-latency curve, sitting between high and max for finer control. These are developer-facing controls that reward teams willing to tune their agent stack carefully.
Who Should Choose GPT-5.5
- Teams already using ChatGPT Plus, Pro, or Codex who want the best available OpenAI model without changing their stack
- Developers building agentic workflows that involve browser interaction and computer use
- Researchers or knowledge workers who need a model that can pursue multi-tool research loops with minimal prompting
- Organizations on OpenAI's Business or Enterprise plans who need GPT-5.5 Pro for the hardest tasks
- Cursor, Windsurf, or Vercel users who want access once GPT-5.5 lands on those platforms via the API
Who Should Choose Claude Opus 4.7
- Engineering teams running agentic coding pipelines where SWE-bench accuracy translates directly to fewer manual corrections
- Developers building document processing or visual analysis workflows that require high-resolution image support
- Organizations on AWS Bedrock, Google Vertex AI, or Microsoft Foundry who prefer Anthropic's enterprise cloud integrations
- GitHub Copilot Pro+ users who want access to the latest Claude model directly in their IDE
- Teams that value literal, predictable instruction-following over creative prompt interpretation
- Anyone optimizing for output token cost who would benefit from the $5/MTok lower output rate
Who This Is Not For
Neither model is a cost-effective choice for high-volume, lower-stakes tasks like content summarization, classification, or simple Q&A at scale. Both sit at the top of their respective pricing tiers — Claude Sonnet 4.6 ($3/$15 per MTok) and GPT-5.3 Codex offer far better economics for routine workloads. Neither model is available to free-tier users, so individuals looking to explore frontier AI capabilities without a subscription will need to look elsewhere. Teams that depend on fine-grained temperature or sampling parameter control should also note that Claude Opus 4.7 now returns a 400 error if temperature, top_p, or top_k are set to non-default values — a breaking change with no equivalent constraint in GPT-5.5.
Frequently Asked Questions
Is GPT-5.5 better than Claude Opus 4.7 overall?
Neither model is strictly better for every use case. GPT-5.5 leads on broad agentic task execution and computer use; Claude Opus 4.7 leads on coding accuracy and high-resolution vision. The right choice depends on your specific workflow and existing tooling.
How do GPT-5.5 and Claude Opus 4.7 compare on price?
Both models are priced at $5 per million input tokens via API. Claude Opus 4.7 charges $25 per million output tokens; GPT-5.5 charges $30. Claude's lower output rate is an advantage for verbose, output-heavy workflows — though Opus 4.7's new tokenizer can inflate effective token counts by up to 35%, so real-world cost comparison requires testing with actual traffic.
Can I use GPT-5.5 or Claude Opus 4.7 on a free plan?
No. Both models are restricted to paid tiers. GPT-5.5 requires at least a ChatGPT Plus subscription ($20/month); Claude Opus 4.7 requires a Claude Pro subscription (also $20/month) or API access.
Which model is better for coding agents?
Claude Opus 4.7 scores higher on SWE-bench Verified (87.6% vs 82.7% on Terminal-Bench 2.0 for GPT-5.5) and verifies its own outputs before reporting back, which reduces failure rates on complex multi-file tasks. GPT-5.5 in Codex has a tighter integration with OpenAI's cloud agent infrastructure and computer-use tools, making it the stronger choice if you need browser and OS-level task execution.
What is the context window for each model?
Claude Opus 4.7 supports a 1 million token context window at standard pricing with up to 128K output tokens. GPT-5.5 also supports a 1M context window via the API; in Codex, the context window is 400K tokens. Both are well beyond what most single-session workflows require.