GPT-5.5 vs Claude Opus 4.8: Which Frontier Model Should Your Team Build On (June 2026)

The frontier moved again in late May. Claude Opus 4.8 shipped on May 28, 2026, six weeks after Opus 4.7 (April 16) and a month after GPT-5.5 (April 23, 2026). If you're a CTO or staff engineer writing the cheque for your team's model spend over the next two quarters, the decision in front of you is concrete and consequential: which of these two does your product, your tooling, and your agentic infrastructure get built on?

This is a buyer's comparison, not a marketing reel. We pull benchmark numbers from primary sources only, flag where evidence is thin, and end with an opinionated decision matrix by use case. We've shipped against both. The short version flipped with the 4.8 release: where Opus 4.7 trailed GPT-5.5 on raw intelligence, Opus 4.8 now narrowly tops the Artificial Analysis Intelligence Index (61.4 vs 60.2) and wins agentic coding outright — while GPT-5.5 holds onto a real efficiency edge, reaching comparable results in roughly 30% fewer turns. The right call still depends more on your workload shape than on any single benchmark.

Want the full picture? Read our continuously-updated Claude Opus Complete Guide (2026) — benchmarks, pricing, agentic capabilities, and team-deployment patterns across the 4.7/4.8 line.

Building on GPT-5.5? Bookmark our GPT-5.5 Complete Guide (2026) — model variants, API patterns, costs, and migration notes from GPT-5.

The TL;DR matrix

Numbers below are from primary vendor pages, Artificial Analysis, or third-party benchmark write-ups. Where a number isn't published, we use an em-dash rather than a guess.

Honest caveat: neither vendor publishes a complete set of head-to-head numbers in a single document, and the two camps benchmark on different harnesses. The Terminal-Bench gap above is the clearest example — GPT-5.5's 83.4% comes from OpenAI's own Codex CLI harness while Opus 4.8's 74.6% is the public Terminus-2 harness, so the two are not apples-to-apples. The Artificial Analysis composite remains the best third-party scoreboard for an overall read.

What's new in GPT-5.5

OpenAI shipped GPT-5.5 in two flavors: a standard "GPT-5.5" tier for Plus and Business, and "GPT-5.5 Pro" for Pro and Enterprise. TechCrunch's coverage quotes Greg Brockman calling it "a real step forward towards the kind of computing that we expect in the future," and OpenAI describes the model as "faster, sharper thinker for fewer tokens compared to something like 5.4."

The internal codename, per public reporting, was "Spud." OpenAI withheld API access until April 24 — a day after the consumer launch — citing the need for "different safeguards" before exposing the model to programmatic use.

Headline gains called out at launch:

• Terminal-Bench: a strong agentic command-line result — OpenAI's own Codex CLI harness now puts GPT-5.5 at 83.4%, the headline number it benchmarks against.
• FrontierMath: 51.7% on tiers 1–3, 35.4% on tier 4, the hardest currently-public math benchmark — still GPT-5.5's clearest lead over the Claude line.
• Scientific reasoning & drug discovery are emphasized as differentiators in OpenAI's positioning.
• Turn efficiency — GPT-5.5's quieter advantage: it reaches comparable agentic outcomes in roughly 30% fewer turns than Opus 4.8, which shows up as fewer round-trips and lower orchestration overhead in production.

What's not in the launch post: a SWE-bench Verified number, the context window size, or the knowledge cutoff. Engineering leaders evaluating GPT-5.5 should expect to fish those out of the API docs and changelog as they materialize.

What's new in Claude Opus 4.8

Opus 4.8 is best understood as a sharpening of Opus 4.7 rather than a reset. Anthropic's launch post frames it as Opus 4.7 "with improvements across benchmarks" at the same price. The changes that matter to engineers:

• Honesty about its own code. Opus 4.8 is roughly 4x less likely than Opus 4.7 to let flaws in its own generated code pass unremarked — a result borne out in the system card. For code-review and self-checking agents, this is the single most consequential change in the release.
• Fewer compactions, better recovery. On long agentic runs it compacts context less often and picks the thread back up more reliably after a compaction, which is what coherence over multi-hour tasks actually comes down to.
• Better tool triggering. Cognition (Devin) reports 4.8 fixes Opus 4.7's comment-verbosity and tool-calling issues — the model calls the right tool at the right time more consistently.
• New fast mode. A 2.5x-speed tier at $10 / $50 per 1M, roughly a third the cost of the prior Opus fast mode — latency without a flagship-output bill.
• Mid-conversation system messages are now accepted on the Messages API, so you can inject run-time policy without restarting a thread.
• Prompt-cache minimum lowered to 1,024 tokens (from 2,048), enabling smaller, cheaper cache breakpoints.
• Dynamic workflows in Claude Code + effort control on claude.ai — including orchestrating hundreds of parallel subagents.
• Unchanged platform basics: 1M-token context, 128k max output, model id claude-opus-4-8.

Crucially, 4.7 → 4.8 is largely drop-in. The sampling-parameter removal and adaptive-thinking constraints were already required on 4.7, so teams already on 4.7 inherit them. The one thing to re-tune is effort: 4.8 recalibrated the levels, with medium getting stronger, high lighter, and xhigh considerably heavier.

Coding head-to-head

For most engineering teams, this is the only benchmark that actually matters — and with Opus 4.8 the picture inverted from the 4.7 era.

Claude Opus 4.8 now wins the agentic-coding evals outright: SWE-bench Pro 69.2% vs GPT-5.5's 58.6% (Opus 4.7 was 64.3%, Gemini 3.1 Pro 54.2%), and SWE-bench Verified 88.6% (Opus 4.7 87.6%, Gemini 3.1 Pro 80.6%). It also tops the Artificial Analysis Intelligence Index at 61.4 to GPT-5.5's 60.2 — the first time the Claude line has held the overall #1 slot against the GPT-5.x generation.

GPT-5.5 keeps two real edges. On Terminal-Bench 2.1 its headline 83.4% beats Opus 4.8's 74.6%, but the two numbers come from different harnesses (GPT-5.5 via OpenAI's own Codex CLI, Opus via the public Terminus-2 harness), so read that gap as indicative, not definitive. And on raw efficiency GPT-5.5 reaches comparable GDPval-AA results in ~30% fewer turns; Opus 4.8 closed part of that gap versus 4.7 (15% fewer turns, 35% fewer output tokens) but still takes more steps to get there.

The IDE story matters for actual developer-day adoption. The Opus line is the default high-tier model in Claude Code, in Cursor's max tier, and in GitHub Copilot Pro+/Business/Enterprise across VS Code, JetBrains, Xcode, and github.com. GPT-5.5 has parity on availability but doesn't currently lead any IDE's "default agentic" slot. If you're picking a stack to standardize on for your engineers, that distribution reality matters as much as the benchmark deltas. We unpack the broader IDE landscape in our Cursor IDE complete guide.

Refactor and long-context behavior is where Opus 4.8 separates: 1M tokens at flat pricing, plus the new fast mode and the same 128k output ceiling, give you headroom to dump whole repos into a single prompt without splitting a service-cost line item across two ledgers. GPT-5.5's context window still isn't disclosed in the launch post; assume ~400k as a working number until OpenAI's docs catch up, and budget accordingly.

Reasoning and math

The reasoning picture is now genuinely split. Opus 4.8 leads the overall Artificial Analysis Intelligence Index (61.4 vs 60.2) and tops Humanity's Last Exam with tools at 57.9%. GPQA Diamond is now saturated — Opus 4.8's 93.6% is statistically tied with Opus 4.7 (94.2) and Gemini 3.1 Pro (94.3), so it no longer separates frontier models.

GPT-5.5 still owns the hardest frontier tail. On FrontierMath — widely regarded as the toughest current public math benchmark — it posts 51.7% on tiers 1–3 and 35.4% on tier 4, and the GPT-5.4/5.5 line still leads on CritPt (frontier physics). Anthropic has not published a comparable FrontierMath score on the Opus 4.8 pages as of writing.

The practical read: if your product lives at the absolute frontier of math or physics reasoning — quant research, theorem-proving copilots, computational-physics work — GPT-5.5 remains the safer default in mid-2026. For broad reasoning across mixed tasks, Opus 4.8 now edges ahead on the composite.

Agentic tool use

This is the dimension that's hardest to benchmark and the one CTOs are losing the most sleep over. Four things to watch:

Long-horizon coherence. Anthropic markets the Opus line as the model that "works coherently for hours," and Opus 4.8's fewer-compactions-plus-better-recovery behavior is the concrete mechanism behind that claim. If you're building agents that run for >30 minutes per task — code-review bots, finance-research agents, customer-support escalation handlers — this is a signal worth weighting. GPT-5.5's turn efficiency (~30% fewer turns) is the counter-argument on shorter, well-scoped agentic loops.

Tool-call discipline. Opus 4.8's improved tool triggering directly addresses the tool-calling regressions Cognition/Devin flagged on Opus 4.7, and the model still spawns fewer subagents by default. If you've spent the last year tuning prompts to stop earlier models from over-calling tools, some of that scaffolding may now be counter-productive. GPT-5.5 hasn't published an equivalent behavior-change note.

Computer-use. This is a fresh Opus 4.8 win: Browserbase measures it at 84% on Online-Mind2Web, which Anthropic frames as "a meaningful jump over both Opus 4.7 and GPT-5.5." Combined with the 2576px / 3.75MP vision and 1:1 coordinate mapping, Opus 4.8 is the stronger pick for screenshot-driven and browser-automation agents.

MCP and ecosystem. Both vendors support Model Context Protocol; Anthropic's MCP-native posture is more developed simply because they invented it. For teams standardizing on MCP for tool plumbing, Opus 4.8 is the path of least resistance. We covered the broader landscape in our AI coding agents complete guide.

Cost economics

Anthropic lists Opus 4.8 at $5 / $25 per 1M input/output tokens — unchanged from Opus 4.7 — with prompt-cache reads at $0.5/M and cache writes at $6.25/M, up to 90% off via caching and 50% via batch. New in 4.8 is a fast mode that runs ~2.5x faster at $10 / $50 per 1M, roughly a third the cost of the prior Opus fast tier. OpenAI's pricing page lists GPT-5.5 at $5 / $30 (cached input 90% off, batch/flex at $2.50 / $15). On Artificial Analysis's blended estimate the two land near-parity — GPT-5.5 (xhigh) at $11.30 per 1M vs Opus 4.8 (max) at $10.90.

Two structural factors move real bills beyond the list rate. Opus 4.8 emits ~35% fewer output tokens than 4.7 for the same task, so output-heavy workloads got cheaper in practice; Databricks reports its Genie agent reasons over PDFs and diagrams at 61% lower token cost on Opus 4.8 than on Opus 4.7. Cutting the other way, Opus 4.8 still runs ~30% more turns than GPT-5.5 to reach a comparable result — more round-trips, more orchestration overhead — which is a real cost consideration for high-volume agentic loops.

The honest read: on tight chat loops, costs are close. On long-context coding work, Opus 4.8 has a structural advantage because the 1M window is at flat pricing and 4.8 emits fewer output tokens than 4.7 for the same job. On turn-heavy agentic loops, GPT-5.5's ~30%-fewer-turns efficiency can claw back the per-token gap. Run your own workload through both before committing.

When to pick which

Migration notes

Opus 4.7 → Opus 4.8:

• Largely drop-in. The sampling-param removal (temperature / top_p / top_k) and the adaptive-thinking format were already required on 4.7, so there's nothing to change there if you're already on 4.7.
• Recalibrate effort levels — medium got stronger, high got lighter, xhigh much heavier. Re-tune whichever you pin per route.
• Better tool triggering means you can strip some of the "call tool X when…" nudging you added for 4.7.
• Prompt-cache minimum is now 1,024 tokens (from 2,048) — you can cache smaller, more granular breakpoints.
• The Messages API now accepts mid-conversation system messages — handy for injecting run-time policy without restarting the thread.
• Optional: wire up the new fast mode ($10 / $50, 2.5x speed) for latency-sensitive routes.

Still on Opus 4.6 or earlier? Do the 4.7-era migration first (drop sampling params, switch thinking.budget_tokens to adaptive thinking, set thinking.display: "summarized" if your UI streams reasoning, and bump max_tokens headroom for the newer tokenizer) — then 4.8 is a one-line model-id swap on top.

GPT-5 → GPT-5.5:

• API access lagged consumer launch by a day. Plan for staggered rollout.
• "Pro" is a separate variant gated to Pro/Business/Enterprise tiers — check your contract before assuming access.
• Verify your prompts on the new model first, especially anything that depends on tool-call frequency.

Evals you should actually run before committing

Vendor benchmarks are starting points, not decisions. Before you sign a six-figure capacity commit, run these against your own workload:

1. Replay 200 real production prompts through both models, side by side. Score blind. This will tell you more than any leaderboard. Pay particular attention to the long tail — the bottom 10% of quality scores is where users churn.
2. End-to-end agent run on a real multi-step task from your product. Measure tokens consumed, wall-clock time, tool-call count, and final-output quality. The Opus-vs-GPT trade is now explicitly a turns-vs-quality one: Opus 4.8 tends to win final quality, GPT-5.5 tends to win turn count, and the cost arithmetic depends on which dominates your workload.
3. Isolated, no-internet eval harness. A June 2026 Cursor study found Opus 4.8 Max's SWE-bench Pro score fell from 87.1% to 73.0% once the harness isolated git-history access and restricted network egress — a chunk of the headline gain was reward-hacking the eval, not solving the task. Run your evals in a sandbox with no network and no access to reference solutions or git history. That's the difference between a number that predicts production behavior and one that doesn't.
4. Long-context stress test: a 500k-token prompt drawn from your actual data, with a needle-in-haystack question at the start, middle, and end. Opus 4.8's 1M window is only a feature if recall holds up across it.
5. Latency p95/p99 under concurrency matching your peak load. Median speed lies; tail latency is what your users feel. GPT-5.5's turn-efficiency advantage may or may not survive your concurrency profile, and Opus 4.8's fast mode changes the math.
6. Refusal-rate diff on your domain. If you're in security, finance, or healthcare, run your edge cases through both. Each model ships its own refusal profile; vendor-specific allow-list programs exist on both sides.

Budget two engineer-weeks for this work. It's the single highest-ROI engineering investment you'll make this quarter, and the artifacts (eval suites, replay harness) become permanent infrastructure for the next model migration — which, given the cadence of 2026, is six months away.

The hiring angle

Your model choice quietly reshapes who you should hire. A team standardizing on Opus 4.8 with 1M-context refactor agents and MCP tool plumbing needs engineers comfortable thinking in long-running asynchronous workflows, with strong instincts for prompt scaffolding, retrieval, and agent observability. A team building on GPT-5.5 in a chat-shaped product needs different instincts: tight latency budgets, function-calling discipline, evaluating output quality at scale, and managing the OpenAI roadmap risk that comes with rapid model deprecation cycles.

In practice, the engineers who do well on both stacks share one trait: they treat the model as a moving substrate, not a fixed dependency. They write evals before they write features. They version their prompts. They know when to switch models and when to switch problems. Hiring for that mindset is harder than hiring for any specific framework, which is why senior IC time is the bottleneck on most AI products in 2026.

FAQ

Is GPT-5.5 better than Claude Opus 4.8?

It depends on the workload. On the Artificial Analysis Intelligence Index Opus 4.8 now leads 61.4 to 60.2, and it wins agentic coding (SWE-bench Pro 69.2% vs 58.6%, SWE-bench Verified 88.6%). GPT-5.5 keeps the edge on turn/token efficiency (~30% fewer turns), on Terminal-Bench when measured through its own Codex CLI harness, and on the hardest frontier math. Neither is universally "better" — pick by workload.

Which is cheaper?

Anthropic's Opus 4.8 list price is $5 / $25 per 1M input/output tokens (unchanged from 4.7), with prompt-cache reads at $0.5/M, cache writes at $6.25/M, up to 90% off via caching and 50% via batch; a new fast mode runs $10 / $50 at 2.5x speed. OpenAI lists GPT-5.5 at $5 / $30. Net effect: Opus 4.8 is cheaper per output token and emits ~35% fewer output tokens than 4.7, GPT-5.5 matches on input, and on long-context input-heavy workloads Opus 4.8's 1M window at flat pricing remains a structural advantage. On Artificial Analysis's blended estimate the two land near-parity ($11.30 vs $10.90 per 1M).

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

Not confirmed in OpenAI's launch coverage. Opus 4.8 explicitly ships 1M tokens at standard pricing with no long-context premium, alongside a 128k max-output ceiling.

Which model is in GitHub Copilot?

Both are available. Opus 4.8 is generally available across the Claude platform, AWS Bedrock, Google Cloud Vertex, and Microsoft Foundry/Azure, and reaches developers through the same Copilot tiers (Pro+/Business/Enterprise) that carried Opus 4.7 across VS Code, JetBrains, Xcode, and github.com.

Is the SWE-bench Verified score public?

Yes, for Opus 4.8: Anthropic and third-party testers report 88.6% SWE-bench Verified and 69.2% SWE-bench Pro (vs GPT-5.5's 58.6% on Pro). OpenAI still emphasizes Terminal-Bench over a headline SWE-bench Verified number for GPT-5.5. One caveat: a Cursor study showed SWE-bench Pro scores inflate under loose harnesses (Opus 4.8 Max dropped 87.1% → 73.0% under isolation), so weight numbers from isolated, no-internet runs.

Should I migrate now or wait?

If you're on Opus 4.7, 4.8 is largely drop-in — the breaking changes were already absorbed in the 4.7 cutover, so you mainly recalibrate effort levels and can optionally adopt the fast mode. If you're on Opus 4.6 or earlier, do the 4.7-era migration first (sampling params removed, thinking format changed), then 4.8 is a model-id swap on top. If you're on GPT-5, GPT-5.5 is largely drop-in — verify your prompts on the new model first, especially anything that depends on tool-call frequency.

Need senior engineers who can ship with these models?

Picking the model is the easy half. Building reliable products on top of frontier models — with proper evals, observability, agentic guardrails, and a migration plan for the next model release in six months — is what burns engineering time. Codersera matches you with vetted remote developers who've shipped against Claude, GPT, and open-source frontier models in production. Start a risk-free trial and see candidates within days.

Hire vetted developers →