GPT 5.5 vs Claude Opus 4.8: Frontier Coding and Reasoning Tested (2026)

By mid 2026 the frontier has two clear leaders for engineering work, and they are not optimized for the same thing. Anthropic's Claude Opus 4.8, released May 28, 2026, leads the company's own launch table on real world issue resolution (SWE-Bench Pro), multidisciplinary reasoning (Humanity's Last Exam), agentic computer use (OSWorld-Verified), knowledge work (GDPval-AA), and financial analysis (Finance Agent v2). It is the most reliable option we have tested for agentic coding: multi step tool use, surgical patches, and structured output that survives contact with a real pipeline. OpenAI's GPT 5.5 takes one clear crown in that same table: agentic terminal coding (Terminal-Bench 2.1), where it edges Opus 4.8. It also costs less on input and adds native audio that Opus does not have.

That split means the usual question (which model is "better") is the wrong one. The right question is which axis dominates your workload. If you are running coding agents that resolve real issues across module boundaries and feed structured results to downstream tools, Opus 4.8 is hard to beat. If your work centers on terminal driven agentic coding, high volume reasoning at a lower input price, or audio in the loop, GPT 5.5 makes a strong case. Below is the head to head, including a worked cost example and a routing pattern for teams that genuinely need both.

Comparison Table: GPT 5.5 vs Opus 4.8

Coding: Two Benchmarks, Two Winners

Coding is where these models genuinely split, and Anthropic's own launch table shows it. On SWE-Bench Pro, which measures full issue resolution against real repositories, Opus 4.8 leads 69.2% to 58.6%. On Terminal-Bench 2.1, which measures agentic coding driven through a terminal (running commands, inspecting output, iterating in a shell loop), GPT 5.5 leads 78.2% to 74.6%. So the right answer depends on what "coding" means for your team.

The distinction matters more than a single headline number. SWE-Bench Pro rewards end to end issue resolution: read the bug, locate the right files, produce a patch that passes the hidden tests without human cleanup. That is the workload most coding agents actually run in CI, and Opus 4.8's ten point lead there is decisive. Terminal-Bench 2.1 rewards a different skill: driving an interactive shell, chaining commands, and recovering from intermediate failures inside a terminal session. GPT 5.5's edge on that axis points to strong terminal driven agent loops.

The score gap tracks a difference in failure modes. GPT 5.5 is very fast to a first candidate patch and shines when the loop is command driven, but it sometimes commits to the wrong file before fully exploring the repository, and it is weaker on multi file edits that span module boundaries. Opus 4.8 is more deliberate: it tends to produce minimal correct patches (surgical edits rather than sprawling rewrites), and its multi step planning holds up better when a fix requires touching three files in the right order. For full issue resolution, that reliability compounds across a long task.

One caveat worth stating plainly: scaffolding matters as much as the raw score. GPT 5.5 does its best work inside strong harnesses like OpenAI Codex and OpenHands, and a well tuned scaffold can narrow the gap on many real repositories. If you are evaluating, hold the scaffold constant before you attribute a difference to the model.

Reasoning and General Intelligence

Outside of coding, Opus 4.8 pulls ahead on Anthropic's hardest reasoning benchmark. On Humanity's Last Exam, a multidisciplinary test at the edge of what frontier models can answer, Opus 4.8 scores 49.8% with no tools against 41.4% for GPT 5.5, and 57.9% with tools against 52.2%. That is a clear lead in both settings, not a rounding margin. The model also leads on agentic computer use (OSWorld-Verified, 83.4% vs 78.7%), knowledge work (GDPval-AA, 1890 ELO vs 1769), and financial analysis (Finance Agent v2, 53.9% vs 51.8%).

The practical takeaway: for non coding reasoning and agentic knowledge work, Opus 4.8 is the stronger model on the published table. Where GPT 5.5 makes its case is modality and price. If you need native audio in the loop, GPT 5.5 is the only one of the two that offers it, and its lower input price favors high volume read heavy workloads. If you need the strongest reasoning, computer use, and structured output, Opus 4.8 is the safer default.

Cost at Scale

List price favors GPT 5.5, and it is not subtle. Take a representative workload: 50K input tokens plus 5K output tokens per request, 10,000 requests per day. These figures use Opus 4.8 standard pricing ($5 / 1M input, $25 / 1M output); the Opus fast mode tier runs $10 / 1M input and $50 / 1M output for about 2.5x the throughput at the same quality, so adjust upward if you route to it.

Opus 4.8:

• Daily input: 50K x 10,000 = 500M tokens x $5 / 1M = $2,500
• Daily output: 5K x 10,000 = 50M tokens x $25 / 1M = $1,250
• Daily total: $3,750. Monthly (30 days): about $112,500

GPT 5.5:

• Daily input: 500M tokens x $1.25 / 1M = $625
• Daily output: 50M tokens x $10 / 1M = $500
• Daily total: $1,125. Monthly (30 days): about $33,750

On raw list price, GPT 5.5 runs roughly a third of the Opus bill for the same token volume. That is a real number and for many general reasoning workloads it settles the question.

Now add the nuance that list price hides. Opus 4.8 resolves a higher share of issues on SWE-Bench Pro (69.2% vs 58.6%) and tends to use fewer reasoning tokens to get there, so cost per resolved task is far closer than the per token gap suggests. As an editorial estimate (not an official figure), once you account for the higher resolution rate and fewer retries and less human cleanup, the effective cost per closed issue narrows substantially against Opus. And if your system prompt is stable, Opus prompt caching applies a discount on cached input, which can pull effective input cost down further on repetitive agent traffic. The honest summary: GPT 5.5 wins on list price; on cost per outcome for full issue resolution, the two are much closer than the sticker suggests.

Where Opus 4.8 Wins

• Issue resolution: leads SWE-Bench Pro (69.2% vs 58.6%) and is the most reliable choice for full, end to end issue resolution.
• Reasoning: leads Humanity's Last Exam both with tools (57.9% vs 52.2%) and without (49.8% vs 41.4%).
• Computer use: leads OSWorld-Verified (83.4% vs 78.7%) for agentic, screen driven tasks.
• Knowledge work: leads GDPval-AA (1890 vs 1769 ELO).
• Finance: leads Finance Agent v2 (53.9% vs 51.8%).
• Structured output reliability: schema conformant output that downstream tools can consume without defensive parsing.

Where GPT 5.5 Wins

• Terminal coding: leads Terminal-Bench 2.1 (78.2% vs 74.6%) for agentic, shell driven coding loops.
• Lower input cost: about $1.25 / 1M input is roughly a quarter of Opus, which dominates high volume read heavy workloads.
• Native audio: text, image, and audio in one model, which Opus 4.8 does not offer.
• Fast first candidate: very quick to a usable initial patch in interactive use.

When You Cannot Pick Just One

Plenty of teams do not have a single workload, and the clean answer there is to route rather than standardize. A sensible split: send full issue resolution, reasoning, computer use, and anything that feeds structured output to downstream systems to Opus 4.8, and send terminal driven coding loops plus cheap high volume general reasoning to GPT 5.5. You get Opus reliability where correctness compounds and GPT 5.5 economics and terminal strength where they fit.

Routing is not free, though. You now maintain two prompt surfaces, and prompts tuned for one model rarely transfer cleanly to the other. You also risk losing caching benefit: prompt caching pays off when traffic is predictable enough to keep entries warm, and a router that scatters requests across providers can fragment that locality. The pattern is worth it when the workloads are genuinely distinct and high volume; it is overhead you do not need if one model already covers ninety percent of your traffic.

When This Applies to Your Stack

If you are running either model in production, the model choice is only half the work. The other half is the infrastructure around it: a gateway that gives you one interface across providers, caching that actually keeps entries warm, eval harnesses that measure resolved tasks rather than token counts, and routing with fallback so a provider incident does not take you down. That is the layer where the list price versus cost per outcome distinction becomes real money.

Contra Collective builds exactly this layer: provider gateways, prompt caching strategies, eval harnesses tied to your real tasks, and routing and fallback that survive an outage. If you are deciding between GPT 5.5 and Opus 4.8, or trying to run both behind one interface without losing caching, we can help you wire it up and measure it honestly. Reach out and we will scope it against your actual traffic.

FAQ

Is GPT 5.5 better than Opus 4.8? It depends on the workload. On Anthropic's official launch table, Opus 4.8 leads every benchmark except agentic terminal coding: it wins SWE-Bench Pro (69.2% vs 58.6%), Humanity's Last Exam, OSWorld-Verified, GDPval-AA, and Finance Agent v2. GPT 5.5 leads Terminal-Bench 2.1 (78.2% vs 74.6%), costs less on input, and adds native audio. Neither is universally better.

Which one is cheaper? GPT 5.5, on list price. Input runs about $1.25 / 1M against $5 / 1M for Opus standard, and output is about $10 / 1M against $25 / 1M. The gap narrows on cost per resolved issue because Opus resolves a higher share of SWE-Bench Pro tasks (69.2% vs 58.6%) with fewer reasoning tokens, and Opus prompt caching can cut effective input cost further on stable prompts. Note Opus also offers a fast mode tier at $10 / 1M input and $50 / 1M output for about 2.5x the speed.

Which is better for coding? It depends on the coding workflow. For full issue resolution against real repositories, Opus 4.8 leads SWE-Bench Pro (69.2% vs 58.6%), produces more minimal correct patches, and handles multi file edits across module boundaries more reliably. For terminal driven agentic coding, GPT 5.5 leads Terminal-Bench 2.1 (78.2% vs 74.6%). Scaffolding (OpenHands, OpenAI Codex) can narrow the gap, so hold it constant when you evaluate.

What are the context and output limits? Opus 4.8 has a 1M token context window (200K on Microsoft Foundry) and a 128K maximum output. GPT 5.5 has about a 400K token context window and the same 128K maximum output. If you need the largest context for whole repository or long document reasoning, Opus has the headroom.

Can I run both behind one gateway? Yes, and many teams do. A gateway gives you a single interface and lets you route agentic coding to Opus and high volume general reasoning to GPT 5.5. The tradeoffs are two prompt surfaces to maintain and potential loss of caching locality if traffic is unpredictable. Contra Collective builds gateways, caching, and routing with fallback for exactly this case.