⚖️ Comparisons | Apr 24, 2026 | 14 min read

By AI Tool Briefing Team

GPT-5.5 vs Claude Opus 4.7: Who Wins Coding?

Seven days. That’s the gap between Anthropic declaring Claude Opus 4.7 the new coding leader and OpenAI dropping GPT-5.5 on April 23, 2026 — in two variants (standard and Pro), rolling out simultaneously to Plus, Pro, Business, and Enterprise tiers. Six weeks after GPT-5.4. A cadence that isn’t competitive pressure so much as open warfare.

The interesting part isn’t that GPT-5.5 shipped. It’s that the benchmarks split the verdict in a way you can’t hand-wave past. Claude wins one tier of real-world coding. GPT-5.5 wins a different one. On math reasoning, it’s not even close. If you were hoping one of these models would just be “better,” this post is going to disappoint you — and save you a lot of money if you read it carefully.

Quick Verdict

Aspect GPT-5.5 Claude Opus 4.7
Best For Terminal workflows, agent orchestration, math-heavy reasoning Multi-file refactors, SWE-bench Pro–style tickets, tool orchestration
SWE-bench Verified 88.7% 87.6%
SWE-bench Pro 58.6% 64.3%
Terminal-Bench 2.0 82.7% 69.4%
FrontierMath Tier 4 39.6% (Pro) 22.9%
MCP-Atlas tool orchestration 75.3% 79.1%
API Pricing $5 / $30 per 1M tokens $5 / $25 per 1M tokens

Bottom line: GPT-5.5 wins terminals and math. Opus 4.7 wins harder real-world engineering tickets and tool orchestration. Neither model wins the whole board — and the split maps cleanly to how you work.

Aspect	GPT-5.5	Claude Opus 4.7
Best For	Terminal workflows, agent orchestration, math-heavy reasoning	Multi-file refactors, SWE-bench Pro–style tickets, tool orchestration
SWE-bench Verified	88.7%	87.6%
SWE-bench Pro	58.6%	64.3%
Terminal-Bench 2.0	82.7%	69.4%
FrontierMath Tier 4	39.6% (Pro)	22.9%
MCP-Atlas tool orchestration	75.3%	79.1%
API Pricing	$5 / $30 per 1M tokens	$5 / $25 per 1M tokens

The Short Version (If You’re in a Hurry)

Use GPT-5.5 when you need:

Terminal-heavy work: shell scripts, CLI workflows, devops automation, anything command-line native
Math-heavy reasoning: theorem proving, formal verification, quant research, scientific computing
Long-horizon agent runs where you can’t babysit the loop
Computer-use or browser automation where the OSWorld score (78.7%) actually matters

Use Claude Opus 4.7 when you need:

Real GitHub-ticket-style refactors spanning multiple files
Tool orchestration across MCP servers (the MCP protocol ecosystem is still Anthropic’s home turf)
Long-context analysis — the 1M token window is default, not a beta flag
Cost-sensitive production pipelines (output tokens are $5 cheaper per million)

The one-liner: GPT-5.5 is better at the terminal. Opus 4.7 is better at the repository. Most coding work involves both.

What Actually Shipped on April 23

OpenAI announced GPT-5.5 in two variants. Worth being specific about what each one is for:

GPT-5.5 — The standard variant. Matches GPT-5.4’s per-token latency despite being measurably more capable. This is the default swap-in for existing workloads.
GPT-5.5 Pro — The top variant, available to Pro, Business, and Enterprise tiers. This is where the headline numbers come from: 39.6% on FrontierMath Tier 4 and 73.1% on OpenAI’s internal Expert-SWE benchmark (which tests against 20-hour human tasks).

The rollout hit Plus, Pro, Business, and Enterprise simultaneously, which is a departure from OpenAI’s usual staggered approach. According to VentureBeat’s coverage, the positioning is explicit: OpenAI is claiming the top of the leaderboard on Terminal-Bench 2.0 for the first time since Anthropic pushed past them in late 2025.

Pricing is worth flagging. The API costs $5 per million input tokens and $30 per million output tokens. Anthropic is $5 / $25 on Opus 4.7. That $5 output delta compounds fast at scale. On a million-token output pipeline running 500 times a day, GPT-5.5 costs roughly $75,000 more per year than Opus 4.7 at the same volume. Not nothing.

Where GPT-5.5 Wins

Three benchmarks tell the story. Each one maps to a specific workflow.

Terminal-Bench 2.0: 82.7% vs 69.4%

This is the largest gap in the comparison. Terminal-Bench 2.0 tests complex command-line workflows — multi-step shell pipelines, iterative debugging, tool coordination across CLIs. A 13-point lead is not a rounding error. It’s a structural advantage.

What this means in practice: if your AI-assisted workflow lives in a terminal (devops automation, log analysis, shell scripting, CI/CD debugging, anything that involves chaining commands and responding to their output), GPT-5.5 is measurably better at it. Users running Codex-style workloads have reported that the difference shows up on long command-chains where Opus 4.7 starts to drift.

Opus 4.7 is still strong here. It’s not a broken tool. But it’s not the right tool for this class of work anymore.

FrontierMath Tier 4: 39.6% vs 22.9%

FrontierMath is a benchmark built by working mathematicians to test research-level problem solving. Tier 4 is the hardest tier — problems that would take a graduate student days to solve. GPT-5.5 Pro hits 39.6% on it. Opus 4.7 sits at 22.9%.

Nearly double. That’s not a marginal improvement. That’s a different model class for math.

Who cares? Anyone doing quantitative research, formal verification, cryptography, theoretical CS, or anything where a proof or derivation is the actual output. Most software engineers don’t touch this work. For the ones who do, this is the single largest capability gap between the two models.

Expert-SWE: 73.1% vs (unreported)

OpenAI’s internal Expert-SWE benchmark tests against 20-hour human engineering tasks — the kind of work that takes a senior engineer two or three days. GPT-5.5 Pro scores 73.1%. Anthropic has not reported an Opus 4.7 score on this eval, which is telling in itself.

Keep this one in perspective. It’s an OpenAI-designed benchmark, not a third-party eval. The score is impressive, but it’s measuring against a test OpenAI chose and tuned for. Take the number, don’t worship it.

Where Claude Opus 4.7 Wins

Two benchmarks, same pattern. Each one maps to a different class of work.

SWE-bench Pro: 64.3% vs 58.6%

SWE-bench Pro is the harder, more realistic sibling of SWE-bench Verified. Where Verified gives models a clean, reviewed ticket with human-verified ground truth, Pro adds noise — ambiguous specs, incomplete repros, tests that don’t cleanly define success. It’s the benchmark that most resembles what production engineering actually looks like.

Opus 4.7 leads by 5.7 points. That’s the gap that matters if your use case is “agent reads a messy GitHub issue and makes a real PR.” Multi-file refactors, cross-module reasoning, ambiguous tickets where the hard part is figuring out what “done” means — Opus 4.7 handles them better.

The two models are nearly tied on SWE-bench Verified (88.7% GPT-5.5 vs 87.6% Opus 4.7). That one-point gap inverts on SWE-bench Pro to a 5.7-point lead for Claude. The cleaner the ticket, the more the models look alike. The messier the ticket, the more Opus 4.7 pulls ahead.

MCP-Atlas Tool Orchestration: 79.1% vs 75.3%

MCP-Atlas measures how reliably a model can orchestrate external tools through the Model Context Protocol — calling APIs, chaining tool outputs, recovering from tool errors. Opus 4.7 leads by 3.8 points.

This tracks with the ecosystem story. MCP is Anthropic’s protocol, even though it’s now open and supported broadly. Claude was trained on MCP-heavy workflows since the protocol launched. GPT-5.5 supports MCP and supports it well, but Anthropic’s head start is visible in the score.

For teams building agent pipelines that hit many external tools — CRM integrations, ticketing systems, internal APIs, data warehouses — Opus 4.7 is the less frustrating model to work with.

The Hallucination Story

One number that got flagged in OpenAI’s announcement deserves its own paragraph: a 60% hallucination reduction versus GPT-5.4.

That’s a big claim. It’s also hard to independently verify across use cases, because hallucination rate is task-dependent and measurement is inconsistent across evals. What we can say: third-party testing shows GPT-5.5 confabulates less in long-context code generation: fewer invented API signatures, fewer made-up library functions, fewer “confident but wrong” responses.

Opus 4.7 is also strong here but hasn’t been credited with a similar percentage reduction. On the hallucination-sensitivity axis, the two models are closer than the headline number suggests, but GPT-5.5 has meaningfully improved from its own prior generation.

For workflows where hallucinated code has real downstream cost — security-critical systems, production APIs, financial calculations — this matters. The less your model invents, the less you have to verify.

Pricing Comparison

The math matters, especially at scale.

Tier	GPT-5.5	Claude Opus 4.7
API input (per 1M tokens)	$5	$5
API output (per 1M tokens)	$30	$25
Consumer subscription	ChatGPT Plus $20/mo	Claude Pro $20/mo
Pro variant	ChatGPT Pro $200/mo	(Claude Max, varies)
Enterprise	Per-seat + usage	Per-seat + usage

At individual-subscription level, the two tiers are effectively identical. $20/month gets you frontline access on both sides.

At API scale, the output pricing gap is real. Opus 4.7 is 16.6% cheaper per output token. For workloads that are output-heavy — code generation, long-form writing, agent pipelines that produce lots of tool calls — Claude is the cheaper model. For input-heavy workloads (large-context analysis where the model mostly reads), the models cost the same.

The ChatGPT Pro tier at $200/month is where GPT-5.5 Pro lives. That’s the variant that hits the math numbers. If you need those capabilities, you’re in that pricing bracket whether you like it or not.

The Stuff Nobody Talks About

A few things the benchmark tables don’t capture.

Opus 4.7 has a 1M token context window by default. GPT-5.5 ships with 1M context on the API per OpenAI’s documentation. Same maximum, but behavior at the upper end differs — Claude has been running at 1M context in production longer and its retrieval quality at extreme depths is more battle-tested. For single-shot codebase-wide analysis, this matters.

GPT-5.5 is part of a super-app strategy. Per TechCrunch’s coverage, OpenAI is positioning GPT-5.5 as the engine for a unified ChatGPT + Codex + AI browser product. The model isn’t just a model — it’s infrastructure for OpenAI’s bigger platform bet. If you’re buying into ChatGPT as a workflow center, GPT-5.5 is the native fit.

Claude has the Mythos asterisk. Anthropic openly admits there’s a better internal model — Claude Mythos — that they’ve restricted to the Project Glasswing partner program for capability reasons. Opus 4.7 is Anthropic’s public ceiling, not their absolute ceiling. GPT-5.5 Pro is, as far as we know, OpenAI’s actual best. That’s a structural asymmetry worth being honest about.

Terminal-Bench is an unusually decisive benchmark. A 13-point gap is genuinely unusual in frontier-model comparisons at this point. Most head-to-heads are 2-5 point splits. When you see this kind of lead, it usually reflects a training choice rather than raw capability — OpenAI almost certainly spent significant compute specifically optimizing for terminal workflows. Which is fine. It still means the tool works better there.

How to Decide

Choose GPT-5.5 if:

Your coding work is terminal-native — shell, devops, CLI tooling
You’re doing math-heavy or research-grade quantitative work
You already live in the ChatGPT ecosystem (Codex, AI browser, Plus/Pro subscriptions)
You run long-horizon agent loops where drift has been a reliability problem

Choose Claude Opus 4.7 if:

Your AI workload is repository-scale — multi-file refactors, ambiguous tickets, real engineering work
You’ve built on MCP and have tool orchestration at the center of your pipeline
You’re output-heavy at API scale and the $5/1M token delta matters
Long-context analysis (200K+ tokens per call) is part of your daily work

Get Both if:

You run production agent pipelines and want to route work by task class — Claude for PR-writing, GPT for terminal automation
You have the budget for $40/month in individual subscriptions and want access to both ceilings
You’re actively benchmarking across models for your specific workload before committing to one vendor

Our Take

Neither model wins. That’s not fence-sitting — it’s the actual result. The benchmarks split cleanly along workflow lines, and the split is stable enough that either tool is a bad default for all use cases.

Here’s what I think is happening. OpenAI and Anthropic have started specializing. Both labs are trying to ship a frontier general-purpose model, but the training priorities diverge. Anthropic optimizes for real GitHub-style engineering work and tool orchestration because that’s what Claude Code users do. OpenAI optimizes for terminals and math because that’s where Codex and their enterprise pipeline lives. The scores reflect the choice.

If you’re one of those users who just wants one model for everything: use whichever one your team already uses. The marginal difference isn’t worth re-plumbing your stack. For most coding workflows, both models are above the “good enough” threshold, and the bottleneck is now how well you’ve integrated them into your actual process — not which one you picked.

If you’re architecting something new? Route by task. Put terminal work on GPT-5.5 and PR-writing on Opus 4.7. The API is standardized enough that multi-model routing is a Tuesday-afternoon project, and the accuracy improvements are worth more than the operational complexity.

The more interesting question is what happens in eight weeks, when Anthropic ships the next Opus and OpenAI ships whatever comes after GPT-5.5. This cadence isn’t sustainable for the industry, but it’s great for the buyer. Our best AI coding assistants guide will keep tracking the split as the benchmarks move.

Frequently Asked Questions

Is GPT-5.5 better than Claude Opus 4.7 for coding?

It depends on what kind of coding. GPT-5.5 leads on Terminal-Bench 2.0 (82.7% vs 69.4%) and is narrowly ahead on SWE-bench Verified (88.7% vs 87.6%). Claude Opus 4.7 leads on SWE-bench Pro (64.3% vs 58.6%) and MCP-Atlas tool orchestration (79.1% vs 75.3%). GPT-5.5 is the better choice for terminal workflows and agent loops; Opus 4.7 is the better choice for repository-scale refactors and tool-heavy pipelines.

When did GPT-5.5 launch?

GPT-5.5 launched on April 23, 2026, exactly seven days after Anthropic released Claude Opus 4.7. OpenAI released two variants simultaneously: standard GPT-5.5 and GPT-5.5 Pro, rolling out to Plus, Pro, Business, and Enterprise tiers in ChatGPT.

How much does GPT-5.5 cost?

GPT-5.5 API pricing is $5 per million input tokens and $30 per million output tokens. Consumer access is included in ChatGPT Plus at $20/month, with GPT-5.5 Pro restricted to Pro ($200/month), Business, and Enterprise tiers. Claude Opus 4.7 is priced at $5 / $25 per million tokens, making it 16.6% cheaper on output.

Which model is better at math reasoning?

GPT-5.5 Pro is substantially better at research-grade math. On FrontierMath Tier 4, GPT-5.5 Pro scores 39.6% versus Claude Opus 4.7’s 22.9% — nearly double. If your workload involves theorem proving, quantitative research, or formal verification, this is the largest single capability gap between the two models.

Is GPT-5.5 better than Claude Mythos?

Claude Mythos is Anthropic’s restricted internal model, accessible only via the Project Glasswing partner program. On the benchmarks Anthropic has published, Mythos scores 93.9% on SWE-bench Verified — higher than GPT-5.5’s 88.7%. But Mythos is not generally available for commercial use, so the practical comparison for most buyers is GPT-5.5 versus Opus 4.7, not versus Mythos.

What is Terminal-Bench 2.0 and why does it matter?

Terminal-Bench 2.0 is a benchmark that tests a model’s ability to complete complex command-line workflows — shell scripts, multi-step CLI pipelines, iterative debugging with tool outputs. It matters because it maps to devops and automation workflows that heavily use AI assistants. GPT-5.5’s 13-point lead here (82.7% vs 69.4%) is the largest benchmark gap in the comparison.

Should I switch from Claude to GPT-5.5?

Only if your primary workflow matches GPT-5.5’s strengths — terminal-heavy work, long-horizon agent runs, or math-heavy reasoning. For typical repository-scale engineering work, Claude Opus 4.7 remains competitive or better, especially on messy real-world tickets (SWE-bench Pro) and tool orchestration (MCP-Atlas). A better question is whether to run both models and route by task type.

Does GPT-5.5 have a 1M token context window?

Yes. GPT-5.5 supports 1M tokens of context on the API, matching Claude Opus 4.7’s default context window. Both models are capable at long-context reasoning, though retrieval quality at the upper end of the window is task-dependent and still imperfect across both models.

Last updated: April 24, 2026. Sources: OpenAI — Introducing GPT-5.5 · TechCrunch — OpenAI releases GPT-5.5 bringing company one step closer to an AI super app · VentureBeat — OpenAI’s GPT-5.5 narrowly beats Anthropic’s Claude Mythos on Terminal-Bench 2.0 · Anthropic — Claude Opus 4.7 announcement.