Claude Opus 4.8 vs GPT-5.5: benchmarks, pricing, and which to pick.

Anthropic shipped Claude Opus 4.8 on May 28, 2026 — the same day as this guide — posting 69.2% on SWE-bench Pro and 88.6% on SWE-bench Verified, according to the Anthropic system card. GPT-5.5, the current OpenAI flagship, remains the strongest competitor on terminal-centric coding benchmarks and for workloads that stay under 272K input tokens. This guide is a head-to-head decision matrix: where each model leads, where the margins are narrow, and how the flat-versus-surcharge pricing structure changes the economics at scale.

The stakes are concrete. Both models are positioned as the production choice for agentic coding, long-context reasoning, and complex tool use — the highest-value commercial AI workloads of mid-2026. Teams routing incorrectly by benchmark headline rather than workload shape can leave meaningful performance and cost on the table. For context on the Opus 4.8 launch itself, see our Opus 4.8 release and dynamic workflows guide; for the prior-generation matchup, see GPT-5.5 vs Claude Opus 4.7.

All benchmark numbers in this post are sourced directly from the Opus 4.8 system card and published third-party leaderboards. Where a head-to-head number was not available (e.g., GPT-5.5 on SWE-bench Verified), this guide notes the absence rather than fabricating a figure. This is a decision guide, not a marketing sheet.

Before the benchmarks, the structural profile. Both models carry 1M-token context windows and are marketed as the best option for agentic coding from their respective labs. The meaningful structural differences sit in three places: the effort/reasoning model (Opus 4.8 defaults to high, with extra/xhigh/max selectable; GPT-5.5 uses a Thinking default plus a Pro tier), the pricing model (Opus 4.8 flat; GPT-5.5 has a surcharge above 272K input), and the cloud distribution (Opus 4.8 ships GA on Anthropic API, Bedrock, Vertex, and Foundry on day one; GPT-5.5 is live in ChatGPT and Codex, with the API rolling out).

Coding and agentic evaluation is the most contested category — and the one where the harness choice most visibly affects the results. Opus 4.8 leads SWE-bench Pro (69.2% vs 58.6%), the benchmark that tests resolving real GitHub issues. It also leads OSWorld-Verified computer use (83.4% vs 78.7%) and MCP-Atlas tool use (82.2% vs 75.3%). GPT-5.5 leads Terminal-Bench 2.1 under its native Codex CLI harness (83.4%); when both models are run on the Terminus-2 public harness, GPT-5.5 scores 78.2% and Opus 4.8 scores 74.6% — a smaller but still real gap that reflects genuine strength on terminal-centric, latency-sensitive pipelines.

The harness caveat matters here in a way that affects procurement decisions. GPT-5.5's 83.4% Terminal-Bench figure is on the Codex CLI harness — Anthropic's own harness for Opus 4.8 may produce different numbers on the same tasks, just as GPT-5.5's Terminus-2 figure (78.2%) differs from its Codex CLI result. The gap is real; its absolute magnitude depends on your evaluation environment. Teams considering Terminal-Bench performance as a primary signal should test both models on their own actual pipelines before committing.

On SWE-bench Verified, Opus 4.8 scores 88.6% per the system card — a strong result. The card did not publish a GPT-5.5 figure on this harness. AutomationBench (Zapier integrations) shows both models at modest levels — Opus 4.8 at 15.5%, GPT-5.5 at 12.9% — indicating that complex cross-tool automation remains a frontier challenge for either model.

A partner assessment cited on the Opus 4.8 announcement page noted that on their Super-Agent benchmark, Opus 4.8 was reportedly the only model to complete every case end-to-end, described as beating prior Opus models and GPT-5.5 at parity on cost — though this is a proprietary, non-public benchmark and should be treated as an indicative signal, not a third-party verifiable score.

On reasoning and knowledge-work evals, Opus 4.8 leads on most benchmarks but the margins vary considerably. Humanity's Last Exam without tools: 49.8% vs 41.4% — an 8.4-point lead that is the largest gap in this category. With tools: 57.9% vs 52.2% — 5.7 points. On ArXivMath, with GPT-5.5 running at xhigh effort, the scores are 71.82% vs 71.48% — effectively tied, within any reasonable noise threshold.

GDPval-AA, the knowledge-work ELO benchmark covering diverse professional tasks, shows Opus 4.8 leading GPT-5.5's xhigh setting by approximately 121 ELO points, corresponding to roughly a 66.7% pairwise win rate per the system card. The bar chart below uses relative ELO percentages for visual comparison; the raw ELO scores are 1890 (Opus 4.8) vs 1769 (GPT-5.5 xhigh).

Finance Agent v2, a Vals AI benchmark for financial analysis tasks, is one of the closer calls: Opus 4.8 at 53.9% vs GPT-5.5 at 51.8%. The v2 harness is deliberately harder than v1, and both models sit in the low-50s — a reminder that many professional knowledge-work benchmarks remain genuinely difficult for current frontier models. For teams evaluating either model for financial workflow automation, the agent coding cost and multi-model composition guide covers production cost modelling for stacks that combine both models.

The pattern across this category: Opus 4.8 has a meaningful lead on multi-domain professional reasoning (HLE, GDPval-AA), while math (ArXivMath) sits at parity. Neither model dominates the other on every axis, which means the decision should hinge on which workloads dominate your use case — not on any single headline number.

Long-context performance and pricing are the two axes most likely to drive the economics of agentic pipelines at scale — and they are deeply connected in this comparison. Both models have 1M-token context windows. The headline is parity. The retrieval reality and the pricing reality are not.

On GraphWalks long-context F1 — a benchmark that tests factual retrieval over very large context windows using graph traversal tasks — Opus 4.8 leads GPT-5.5 across every configuration tested. At BFS 256K, the lead is 12.2 points (85.9% vs 73.7%). At BFS 1M, it widens to 22.7 points (68.1% vs 45.4%). At Parents 1M, it is 24.8 points (83.3% vs 58.5%). These are large enough gaps to be architecturally decisive: for workloads that routinely reason over entire codebases, multi-document research corpora, or long agent execution traces, Opus 4.8 can be expected to retrieve more reliably at the upper end of the context window.

The pricing structure compounds this advantage for long-context workloads. Opus 4.8 charges a flat $5 per million input tokens and $25 per million output tokens regardless of where in the context window you are operating. GPT-5.5 charges $5/$30 for sessions under 272K input tokens — a competitive rate — but applies a long-context surcharge above that threshold, reportedly approximately doubling the input rate and increasing the output rate by 1.5× for the whole session.

The inflection point is roughly the 272K-token session boundary. For workloads that consistently stay below 272K input tokens, GPT-5.5's $5/$30 base rate makes it modestly more expensive on output than Opus 4.8's $5/$25, but the gap is manageable. Above 272K, GPT-5.5's effective per-session cost rises sharply while Opus 4.8's stays constant. For teams doing frequent full-codebase reasoning or long-document analysis, the Opus 4.8 flat-rate model can become materially cheaper even at equal or lower individual benchmark scores — though actual savings depend on your session composition.

The fast mode tier ($10/$50 per 1M) also deserves mention. Opus 4.8 offers a 2.5× speed increase at 2× the standard price — useful for latency-sensitive agentic loops where raw throughput matters more than cost per token. This tier sits between the GPT-5.5 base tier and the GPT-5.5 Pro tier ($30/$180) in both price and capability positioning.

Benchmark scores only capture part of the production decision. Ecosystem fit — how well a model integrates with your existing tooling, deployment targets, and developer workflows — often determines which model a team actually ships with, even when benchmark margins are marginal.

Anthropic ecosystem. Opus 4.8 launched alongside dynamic workflows in Claude Code (research preview) — a capability that allows Opus 4.8 to plan and execute multi-step agentic tasks while adapting its tool-use strategy mid-execution. This is a meaningful workflow-level advantage for teams already on Claude Code. Opus 4.8 is available on Anthropic API, Amazon Bedrock, Google Cloud Vertex AI, and Microsoft Foundry from day one, covering the major enterprise deployment paths. The Opus 4.8 launch guide covers the dynamic workflows capability in depth.

OpenAI ecosystem. GPT-5.5 is the default model in ChatGPT for Plus/Pro/Business/Enterprise subscribers and ships with deep Codex CLI integration — the same harness that produced its 83.4% Terminal-Bench result. For teams whose coding pipeline is built around Codex, that integration is not just a benchmark number; it is a toolchain compatibility advantage. The API rollout for GPT-5.5 was described as rolling out after launch, with additional safety and security work needed for API-scale serving.

Multi-model patterns. The emerging production pattern among engineering-led teams is not to pick one model but to route by task shape. The three-model agentic coding cost comparison (Gemini 3.5 Flash / GPT-5.5 / Opus 4.7) documented this routing approach at the previous model generation. With Opus 4.8, the router logic is updated: long-context and broad agentic work routes to Opus 4.8; terminal-centric coding routes to GPT-5.5 under Codex; bulk and cost-sensitive tasks route to smaller models. Our AI transformation service work with engineering teams increasingly centers on building these multi-model orchestration layers — the benchmark spread between labs has made single-vendor strategies genuinely suboptimal for most large workloads.

One area worth tracking as MCP adoption grows: Anthropic introduced the Model Context Protocol and Opus 4.8's MCP-Atlas lead (82.2% vs 75.3%) appears to reflect deeper native integration. Teams whose agent stacks are MCP-heavy should weight that benchmark accordingly; teams whose agents are primarily built around OpenAI function calling or the Assistants API may not see the same MCP-advantage in production.

Both models are frontier-tier. On the benchmarks where they diverge, the margins range from narrow (ArXivMath: a tie; Finance Agent v2: 2.1 points) to substantial (GraphWalks 1M Parents: 24.8 points; SWE-bench Pro: 10.6 points). The routing guidance below reflects where each model's strengths translate to production workload outcomes — not just benchmark leaderboard rankings.