Claude Opus 4.8 vs GPT-5.5 vs Gemini 3.1 Ultra: The Benchmark-by-Benchmark Comparison

A BitsMinds analysis. “Which model is best?” is the wrong question in 2026 — but “which model is best at what?” has never been more answerable. We lined up each lab’s strongest model — Anthropic’s Claude Opus 4.8, OpenAI’s GPT-5.5 and Google’s Gemini 3.1 Ultra — and ran them through the benchmark portfolio that still separates frontier systems, then weighed the things teams actually pay for: price, context and speed.

How we compared them

Following the consensus that has formed around independent trackers like Artificial Analysis and llm-stats, we avoid any single “winner” number. Saturated benchmarks like MMLU and HumanEval — where every frontier model now clusters above 88% — are out. In are the evals that still discriminate: GPQA Diamond (PhD-level science), ARC-AGI-2 (logic the model has never seen), AIME 2025 (competition math), Humanity’s Last Exam (the hardest questions we have), SWE-bench Verified/Pro (real GitHub fixes), BFCL (tool calling) and OSWorld (computer use).

Two caveats up front. First, these are vendor-reported and public-leaderboard figures, and run conditions differ — Anthropic, for instance, quotes Terminal-Bench 2.1 (74.6%) while OpenAI’s headline 82.7% is on Terminal-Bench 2.0, so we lean on SWE-bench as the common coding yardstick instead. Second, benchmark contamination is a real risk: a high score can mean a model memorized the test, not that it can reason. Treat the table as a map, not a verdict — and always validate against your own workload.

Three models, three philosophies

Anthropic is selling autonomy and trust. Opus 4.8 launched with a feature, not a number: Dynamic Workflows, which let Claude plan a job, spin up hundreds of parallel subagents in one session and verify their work before reporting back. Add a user-facing effort dial and what Anthropic calls its “most honest” self-review yet, and the pitch is the model you trust with a long, messy, multi-hour task.

OpenAI is selling execution and ubiquity. GPT-5.5 (internally “Spud”) is built to plan and finish multi-step work, and it is the default for everyone in ChatGPT, tuned to cut hallucinations in law and medicine. Its edge is reach: the capable generalist hundreds of millions of people already use without choosing it.

Google is selling raw reasoning and scale. Gemini 3.1 Ultra is the brain of the group on the hardest intelligence tests, and it pairs that with a 2-million-token context window, strong grounding and the lowest flagship price of the three. When the job is dense reasoning over enormous inputs, it is built to win.

Reasoning & knowledge

On the benchmarks built to measure raw intelligence, Gemini 3.1 Ultra is the front-runner. Its verified 94.3% on GPQA Diamond and 77.1% on ARC-AGI-2 — a test specifically designed so it cannot be memorized — are the best of the three, and it narrowly tops AIME 2025 as well. This is the model you reach for when the bottleneck is the difficulty of the thinking itself.

The gaps, though, are small — a point or two on most rows — and Opus 4.8 takes the single hardest eval, Humanity’s Last Exam, where every model still scores under 50% without tools. The takeaway isn’t a blowout; it’s that Google has quietly reclaimed the “smartest on paper” crown by a nose.

Coding & agents

Flip to work that has to actually run, and the order flips too. Claude Opus 4.8 is the clear coding-and-agents leader, resolving 88.6% of SWE-bench Verified issues — nearly seven points clear of Gemini and nine clear of GPT-5.5 — and holding a similar lead on the harder SWE-bench Pro split. It is also the only one of the three with a published OSWorld computer-use score (83.4%). That, plus Dynamic Workflows, is why Anthropic keeps winning developer mindshare even when it loses the reasoning leaderboard.

GPT-5.5 stays competitive here and remains exceptional at agentic execution in its own ChatGPT and Codex surfaces; Gemini edges ahead only on BFCL function calling. For autonomous coding and long-horizon agent runs, though, Opus is the one to beat.

Context, price & speed

For practical deployment, Gemini presses two more advantages. Its 2-million-token context window is twice what Opus and GPT-5.5 offer at the API, which matters enormously for whole-repo analysis, long legal or financial documents and RAG pipelines. And at roughly $4 in / $20 out per million tokens, it is the cheapest flagship of the three — undercutting Opus 4.8 ($5 / $25) and GPT-5.5 ($5 / $30).

Speed is the one axis where none of these flagships shines: all three are “quality” tiers that trade latency for depth. Teams serving high-volume, latency-sensitive traffic will usually route the easy 90% of requests to a fast tier — Google’s own Gemini 3.5 Flash, GPT-5.5’s lighter variants or Claude’s fast mode — and reserve these flagships for the hard 10%.

Safety, honesty & trust

Benchmarks miss the dimension that matters most once a model is running unattended: can you trust what it tells you it did? This is where Opus 4.8 stakes its claim, shipping what Anthropic describes as its most honest self-review behavior yet — the model is less likely to quietly paper over a failed step or overstate its confidence. For agentic workflows that touch code, money or compliance, that reliability can outweigh a point of GPQA. GPT-5.5, for its part, was explicitly tuned to cut hallucinations in high-stakes domains, and Gemini 3.1 Ultra leans on improved grounding to reduce factual errors — but Anthropic is the only one making trustworthiness its headline.

The full scorecard

Highlighted cell = leader on that row. Figures are lab-reported or drawn from public leaderboards (llm-stats, Vellum, Artificial Analysis) as of late May 2026; benchmark versions and run conditions differ between vendors.

Read across the rows and the pattern is unmistakable: Gemini 3.1 Ultra owns reasoning, context, price and human preference; Claude Opus 4.8 owns coding, agents and honesty; GPT-5.5 wins no single row outright yet trails on almost none — the all-rounder advantage that, combined with ChatGPT’s reach, keeps it the default for most people.

The verdict: which one should you use?

Pick Gemini 3.1 Ultra when the work is hard reasoning over large inputs — scientific analysis, novel problem-solving, million-token codebases or document sets — or when you simply want the most intelligence per dollar. It is the sharpest and the best value of the three.

Pick Claude Opus 4.8 when the work has to execute and be trusted — autonomous coding, large refactors, multi-hour agent runs, anything where you need to audit how the answer was reached. Its SWE-bench lead and honesty posture make it the safest pick for real engineering.

Pick GPT-5.5 when you want one capable default for the widest audience with the least friction. It is strong almost everywhere, already embedded in the tools your team uses, and rarely the wrong answer — even when it is not the single best one.

The deeper point is that the “frontier” has split into specialties. The right move for most teams isn’t to crown one model — it’s to route across all three, sending each job to the lab that optimized for it.