GPT-5.5 vs Claude Opus 4.7 vs Gemini 3.1 Pro: 2026 年先駆けモデルの対決

3 つの旗舟モデル、3 つの別々のラボ、そして 2026 年の本格的なプロダクション用 AI が本当に必要とするものの 3 つの異なる賭け。GPT-5.5 は 4 月 23 日にリリースされ、Opus 4.7 は 4 月 16 日にリリースされ、Gemini 3.1 Pro は 2 月 19 日から開発者プレビューを開始しています。もし現在、エージェントやコーディングツール、あるいはどんな本格的なプロダクションワークフローを構築しているなら、どのモデルがどこで優れており、どこで不十分なのかを正確に知る必要があります。 これはヘッジを挟みません。 各ラボは自らの旗舟モデルが最良だと主張しますが、正直な答えは、2026 年 4 月にすべてのワークロードで 1 つのモデルが勝つかというと否定されます。差別化は、生々しい知能から特異性へと移行しました：あなたのタスク、あなたの価格帯、そしてあなたのインフラにとってどのモデルが最適かです。この 3 つのモデルの間のベンチマークにおける大部分のギャップは狭すぎており、間違った選択が能力の向上において得られるメリットよりも、API 費用とリワークの観点でより高いコストを意味します。 この比較をどのように読むべきか見てみましょう。 エージェントコーディングは現在の最高リスクのカテゴリであり、結果は分岐しています。 Terminal-Bench 2.0 において、GPT-5.5 は 82.7% を達成し、GPT-5.4 の 75.1% から上昇しています。Claude Opus 4.7 は 69.4%、Gemini 3.1 Pro は SWE-Bench Pro で 54.2% です。GPT-5.5 は Terminal-Bench で明確に勝っており、このベンチマークはリアルなコマンドラインワークフロー、シェルスクリプト、コンテナオーケストレーション、そしてツールチェーンをテストします。もしあなたのエージェントがターミナルに住んでいるなら、これが最も重要な数字です。 しかし、SWE-Bench Pro（Python、JavaScript、Java、Go によるリアルな GitHub イシュー解決）において、ランキングが反転します。Opus 4.7 は SWE-Bench Pro で 64.3% を達成し、GPT-5.4 の 57.7% と Gemini の 54.2% を両方越え、上回っています。GPT-5.5 の 58.6% は GPT-5.4 を上回りますが、この特定のベンチマークでは Opus 4.7 に後退しています。 ツール使用と MCP は Opus 4.7 の最も明確な勝利です。Opus 4.7 は MCP-Atlas で 77.3% をリードし、GPT-5.4 の 68.1% と Gemini 3.1 Pro の 73.9% を上回っています。MCP-Atlas は複雑な多ターンのツール呼び出しシナリオを測定し、リアルなプロダクションエージェントベンチマークの最も近いものです。複数のツールを単一のワークフローにルーティングするオーケストレーションエージェントを構築するチームにとって、これは注意すべき結果です。 科学的情報処理（GPQA Diamond）は本質的に 3 者の同率です。Opus 4.7 が 94.2%、Gemini 3.1 Pro が 94.3%、GPT-5.4 Pro が 94.4% です。GPT-5.5 はこの同率を意味ある意味で破りません。このベンチマークは先駆けにおいて飽和に近い段階に達しており、差別化は elsewhere にあります。 抽象的情報処理（ARC-AGI-2）は Google のフットボールのストーリーです。Gemini 3.1 Pro は ARC-AGI-2 で 77.1% を達成し、Gemini 3 Pro の 31.1% のスコアから倍以上になっています。ARC-AGI-2 はモデルがトレーニング中に記憶していただけではない革新的なパターン認識をテストします。OpenAI や Anthropic はここで比較可能なスコアを公表していませんでした。 コンピュータ使用は近いですが GPT-5.5 はわずかに先を走っています。GPT-5.5 は OSWorld-Verified で 78.7%、Opus 4.7 は 78.0% を達成し、両方とも GPT-5.4 の 75.0% から上昇しています。前世代における Opus 4.7 の Favor に 0.7 ポイントのギャップは今反転しており、わずかでさえあります。 ウェブ検索とブラウジングは GPT-5.5 の他の明確な優位性です。GPT-5.4 は BrowseComp で 89.3% をリードし、Opus 4.7 の 79.3% を対比させています。GPT-5.5 はこのギャップを維持します。もしあなたのエージェントがウェブを信頼してナビゲートする必要があるなら、OpenAI は優位です。 GPT-5.5 は真に新しい基礎モデルです。GPT-4.5 以来最初の完全に再トレーニングされたベースモデルであり、GPT-5 アーキテクチャの微細化ではなく、ゼロから訓練されたモデルです。それが Terminal-Bench のジャンプを説明します。このモデルはコード執行的な処理をより根本的なレベルで、単に増量的にではなく異なる方法で論理を構成します。これは GPT-5.4 と同じトークンあたりの遅延を達成し、高い知能レベルで機能しますが、同じ Codex タスクを完了するには少ないトークンを使います。 Claude Opus 4.7 はベンチマークが完全に捉えられない行動変化を導入しました。自分の出力を確認する手段を考案し、レポートを返す前に、計画段階で自分の論理的な欠陥を捕まえ、以前の Claude モデルから非常に速く実行を加速させます。これはスコア改善だけのものではありません——これはモデルが問題に接近する方法の変化です。

Three flagship models. Three different labs. Three different bets on what production AI actually needs in 2026. GPT-5.5 dropped April 23, Opus 4.7 dropped April 16, and Gemini 3.1 Pro has been in developer preview since February 19. If you're building agents, coding tools, or any serious production workflow right now, you need to know exactly where each one wins — and where it doesn't. This is the breakdown with no hedging. Every lab calls its flagship the best. The honest answer is that no single model wins across every workload in April 2026. The differentiation has shifted from raw intelligence to specificity: which model is best for your tasks, at your price point, on your infrastructure. The gap between these three models on most benchmarks is narrow enough that the wrong choice costs more in API spend and rework than the right choice saves in capability. Here's how to actually read the comparison. Agentic coding is the highest-stakes category right now, and the results are split. On Terminal-Bench 2.0, GPT-5.5 achieves 82.7%, up from GPT-5.4's 75.1%. Claude Opus 4.7 sits at 69.4%. Gemini 3.1 Pro scores 54.2% on SWE-Bench Pro. GPT-5.5 wins Terminal-Bench decisively — this benchmark tests real command-line workflows, shell scripting, container orchestration, and tool chaining. If your agent lives in a terminal, this is the number that matters most. But on SWE-Bench Pro — real GitHub issue resolution across Python, JavaScript, Java, and Go — the rankings flip. Opus 4.7 scores 64.3% on SWE-Bench Pro, leapfrogging both GPT-5.4 at 57.7% and Gemini at 54.2%. GPT-5.5's score of 58.6% puts it ahead of GPT-5.4 but still behind Opus 4.7 on this specific benchmark. Tool use and MCP is Opus 4.7's clearest win. Opus 4.7 leads MCP-Atlas at 77.3%, ahead of GPT-5.4 at 68.1% and Gemini 3.1 Pro at 73.9%. MCP-Atlas measures complex, multi-turn tool-calling scenarios — the closest thing to a real production agent benchmark. For teams building orchestration agents that route across multiple tools in a single workflow, this result is the one to pay attention to. Scientific reasoning (GPQA Diamond) is essentially a three-way tie. Opus 4.7 comes in at 94.2%, Gemini 3.1 Pro at 94.3%, and GPT-5.4 Pro at 94.4%. GPT-5.5 does not break this tie meaningfully. This benchmark is approaching saturation at the frontier — the differentiation is elsewhere. Abstract reasoning (ARC-AGI-2) is Google's headline story. Gemini 3.1 Pro scored 77.1% on ARC-AGI-2, more than double Gemini 3 Pro's score of 31.1%. ARC-AGI-2 specifically tests novel pattern recognition that models cannot have memorized during training. Neither OpenAI nor Anthropic has published comparable scores here, which tells its own story. Computer use is close but GPT-5.5 nudges ahead. GPT-5.5 achieves 78.7% on OSWorld-Verified, Opus 4.7 reaches 78.0%, both up from GPT-5.4's 75.0%. A 0.7-point gap in Opus 4.7's favor on the previous generation is now reversed — marginally. Web search and browsing is GPT-5.5's other clear advantage. GPT-5.4 held a BrowseComp lead at 89.3% versus Opus 4.7's 79.3%. GPT-5.5 maintains this gap. If your agent needs to navigate the web reliably, OpenAI has the edge. GPT-5.5 is a genuinely new foundation. It's the first fully retrained base model since GPT-4.5 — not a refinement of the GPT-5 architecture, but a model trained from scratch. That explains the Terminal-Bench jump. The model reasons about code execution differently at a fundamental level, not just incrementally better. It matches GPT-5.4's per-token latency while performing at a higher intelligence level — and uses fewer tokens to complete the same Codex tasks. Claude Opus 4.7 introduced a behavioral shift that the benchmarks only partially capture. It devises ways to verify its own outputs before reporting back, catches its own logical faults during the planning phase, and accelerates execution far beyond previous Claude models. This isn't just a score improvement — it's a change in how the model approaches long-horizon agentic work. Low-effort Opus 4.7 is roughly equivalent to medium-effort Opus 4.6, which means the efficiency gain shows up in your token bill before you even tune effort levels. The vision upgrade also deserves mention: image resolution jumped from 1.15 megapixels to 3.75 megapixels — more than three times the pixel count of any prior Claude model. Gemini 3.1 Pro plays a different game: multimodal breadth and context scale. It is the only frontier model with true native multimodal support — handling text, images, audio, and video simultaneously within a single unified model. GPT-5.5 handles text and images but not audio or video at the API level. Opus 4.7 has excellent vision but no audio or video. The context window is 2 million tokens — the largest of any frontier model available today. In practical terms, this means processing entire book collections, extensive legal contracts, or hours of video in a single prompt. GPT-5.5 and Opus 4.7 both offer 1M context windows, but Gemini doubles it. GPT-5.5 in Codex is the default choice for infrastructure automation, CI/CD scripting, and multi-step computer use. The Terminal-Bench lead is real and it matters for DevOps-adjacent workflows. Cursor co-founder Michael Truell confirmed GPT-5.5 stayed on task longer and showed more reliable tool use than GPT-5.4. It's also the model to choose if your agent does significant web navigation. Claude Opus 4.7 is the strongest choice for production coding agents that need to reason through ambiguous, multi-file engineering problems — and for any workflow that requires reliable tool orchestration. Vercel confirmed Opus 4.7 does proofs on systems code before starting work — a new behavior not seen in prior Claude models. For legal tech, financial analysis, and document-heavy enterprise work, the Finance Agent benchmark win (64.4%, state-of-the-art at release) and the BigLaw Bench result (90.9%) are concrete signals. Gemini 3.1 Pro is the right choice when your workload is research-heavy, multimodal by nature, or involves very long context that would push the other models to their limits. It's also the only model in this group that can natively process video alongside text — useful for content pipelines, educational tooling, and media analysis. This is where the decision often gets made. Gemini 3.1 Pro costs $2.00 per million input tokens and $12.00 per million output tokens. Claude Opus 4.7 is priced at $5 per million input tokens and $25 per million output tokens — unchanged from Opus 4.6. GPT-5.5 costs $5.00 per million input tokens and $30.00 per million output tokens. At equivalent input pricing, Gemini 3.1 Pro costs 60% less than the other two flagships. At 10 million output tokens per month, Gemini comes in at roughly $120, Opus 4.7 at $250, and GPT-5.5 at $300. For high-volume workloads where Gemini's benchmark profile is sufficient, that gap is real budget. One important caveat on Opus 4.7: the new tokenizer can use roughly 1.0–1.35x more tokens than Opus 4.6 depending on content. Replay real prompts before assuming the list price is your actual cost. On GPT-5.5: cached input tokens drop to $0.50 per million — a tenth of the standard rate. Cache your system prompts and tool schemas on any multi-turn workflow. The 2024 playbook was: pick the smartest model, use it for everything. That playbook is dead. The April 2026 frontier is differentiated enough that routing by task type is now the correct architecture. GPT-5.5 on terminal and browser tasks, Opus 4.7 on complex multi-file coding and tool orchestration, Gemini 3.1 Pro on research, video, and long-context analysis — that's not hedging, it's the optimal engineering decision given where benchmarks actually sit. An IDC analyst framed the structural dynamic plainly: no single model wins everywhere, which is healthy for the ecosystem and gives developers real choices based on specific needs. The developers who treat model selection as a routing problem — rather than a loyalty problem — will ship better products at lower cost. GPT-5.5 is live in ChatGPT for Plus, Pro, Business, and Enterprise users. API access (gpt-5.5) is available now through OpenAI's platform at $5/$30 per million tokens. Claude Opus 4.7 (claude-opus-4-7) is generally available via the Anthropic API, Amazon Bedrock, Google Cloud Vertex AI, and Microsoft Foundry at $5/$25 per million tokens. Gemini 3.1 Pro is available in developer preview via Google AI Studio, Vertex AI, and Gemini CLI at $2/$12 per million tokens (under 200K context). There is no universal winner in April 2026. There are three strong models with distinct profiles, real price differences, and specific workloads where each one is the right default. The engineers who benchmark their actual tasks against all three will build better systems than the ones who follow lab marketing. Start there. Follow for more coverage on MCP, agentic AI, and AI infrastructure.