Route refactors to Opus 4.7 and terminal work to GPT-5.5 on Bedrock

GPT-5.5 vs Claude Opus 4.7 is a frontier-model routing decision defined less by vendor preference than by whether your agent spends most of its time in a terminal or inside a repo-wide refactor. On April 23, 2026, OpenAI published the cleanest public split in this matchup: GPT-5.5 leads Terminal-Bench 2.0 at 82.7%, while Claude Opus 4.7 leads SWE-Bench Pro at 64.3%.[1]

Most comparison posts stop at "both are frontier models." That is the wrong abstraction. AWS made both vendors easier to buy through one control plane on April 28, 2026, but Bedrock does not make their request contracts or workload fit interchangeable.[3][4][6]

Key facts

1. OpenAI's own launch table shows Claude Opus 4.7 ahead on SWE-Bench Pro, 64.3% to 58.6%, while GPT-5.5 leads on Terminal-Bench 2.0, 82.7% to 69.4%.[1]
2. AWS added OpenAI models, Codex, and Managed Agents to Bedrock in limited preview on April 28, 2026, making dual-vendor deployment normal for AWS-first teams.[4]
3. Bedrock still separates Anthropic Messages from OpenAI-compatible ChatCompletions and Responses, so model routing remains an application concern.[5][6]
4. Anthropic says Opus 4.7 adds xhigh effort, adaptive thinking, and tokenizer changes that can raise text-token usage versus Opus 4.6, which matters for quality-first refactor workloads.[7]

Pick Opus 4.7 when the agent must hold a repo in working memory

Claude Opus 4.7 is the safer default when the job is a multi-file code change that rewards patience, instruction fidelity, and long-horizon verification. Anthropic positions the release as a direct upgrade for advanced software engineering, and OpenAI's comparison table supports that claim with a 64.3% SWE-Bench Pro score versus GPT-5.5's 58.6%.[1][2]

That benchmark gap is not abstract leaderboard noise. It maps to the kind of coder-agent work where the model must preserve constraints across files, notice hidden coupling, and avoid declaring victory too early. If you are asking an agent to rename interfaces across a large codebase, update tests, and explain the migration path, Opus 4.7 is the more defensible first route.[1][2]

There is a cost to that choice. Anthropic's model notes say Opus 4.7 introduces a new xhigh effort level for coding and agentic work, replaces the old extended-thinking mode with adaptive thinking, and may use roughly 1.0x to 1.35x as many text tokens as Opus 4.6 because of tokenizer changes.[7] That makes Opus a quality-first pick for agentic-loop workloads, not a universal default.

Pick GPT-5.5 when the loop lives in the terminal or browser

GPT-5.5 is the stronger default when the agent spends its time inspecting command output, recovering from tool friction, and continuing autonomously. OpenAI's launch post shows the biggest gap in the matchup on Terminal-Bench 2.0, where GPT-5.5 scores 82.7% and Opus 4.7 scores 69.4%.[1]

The same table gives GPT-5.5 smaller but still useful edges on OSWorld-Verified, 78.7% to 78.0%, and BrowseComp, 84.4% to 79.3%.[1] For teams building agents that mix shell work, browser investigation, and tool-use, those numbers matter more than generic claims about "reasoning."

This does not mean Opus is weak on long context or vision. Anthropic's docs say Opus 4.7 supports a 1M-token context window, 128K max output, and high-resolution image input up to 2576px or 3.75MP.[7] The practical split is narrower: GPT-5.5 looks better for fast-moving terminal loops, while Opus 4.7 looks better for repo reasoning that ends in a patch.

Use Bedrock as the control plane, not as an excuse to collapse routing

Bedrock changes procurement and governance more than it changes model behavior. AWS says OpenAI models on Bedrock inherit enterprise controls teams already use there, including IAM, PrivateLink, guardrails, encryption, and CloudTrail logging, while OpenAI frames the launch as bringing frontier models, Codex, and Managed Agents onto AWS infrastructure.[3][4]

The implementation detail most teams miss is that Bedrock still exposes different API families by vendor. AWS documents OPENAI_BASE_URL=https://bedrock-mantle.<region>.api.aws/v1 for OpenAI-compatible APIs and ANTHROPIC_BASE_URL=https://bedrock-mantle.<region>.api.aws/anthropic for Claude's Messages API.[5] The compatibility matrix makes the same point in a different form: OpenAI models sit behind ChatCompletions and Responses, while Claude sits behind Messages.[6]

That means Bedrock solves the governance half of agent-orchestration but not the routing half. One AWS account can host both vendors. Your application still needs to decide which request shape, benchmark profile, and failure mode belongs to which workload.

Route by benchmark split, then normalize your app layer

The useful routing table is already strong enough to act on. Send repo issue resolution and refactors to Opus 4.7 because it leads SWE-Bench Pro. Send shell-heavy debugging and CI loops to GPT-5.5 because it leads Terminal-Bench 2.0 by 13.3 points. Send browser-heavy operator tasks to GPT-5.5 unless you specifically need the visual reasoning or long-horizon code review shape where Opus earns its cost.[1][7]

That policy is simple enough to encode in a small router and precise enough to defend to an engineering team. It also fits the broader evaluation framework in Picking a Frontier Model: Opus 4.7 vs GPT-5.5 vs Gemini 3.1 Pro — A Builder's Benchmark Guide, where the right question is not "which frontier model is best?" but "which benchmark dimension predicts my production workload?"

Runnable example: one Bedrock account, two model families

```bash export OPENAI_BASE_URL="https://bedrock-mantle.us-east-1.api.aws/v1" export ANTHROPIC_BASE_URL="https://bedrock-mantle.us-east-1.api.aws/anthropic"

export OPENAI_MODEL_ID="openai-model-id" export ANTHROPIC_MODEL_ID="anthropic-model-id"

curl -X POST "$OPENAI_BASE_URL/chat/completions" \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -d '{ "model": "'"$OPENAI_MODEL_ID"'", "messages": [ {"role": "developer", "content": "You are a terminal-heavy debugging agent."}, {"role": "user", "content": "Investigate why the staging pod is CrashLoopBackOff and list the first three commands you would run."} ] }'

curl -X POST "$ANTHROPIC_BASE_URL/v1/messages" \ -H "Content-Type: application/json" \ -H "x-api-key: $ANTHROPIC_API_KEY" \ -d '{ "model": "'"$ANTHROPIC_MODEL_ID"'", "max_tokens": 1024, "messages": [ {"role": "user", "content": "Review a multi-file Python refactor and propose the patch plan before writing code."} ] }' ```

Expected output: the GPT-5.5 call should return an action-first debugging plan centered on shell inspection, while the Opus 4.7 call should return a structured refactor plan that foregrounds file relationships, constraints, and verification.[5][6]

The practical answer to "GPT-5.5 vs Claude Opus 4.7?" is not to crown a winner. Keep one AWS control plane, route repo-scale refactors to Opus 4.7, and send terminal-heavy loops to GPT-5.5. For the full evaluation framework behind that router, start with Picking a Frontier Model: Opus 4.7 vs GPT-5.5 vs Gemini 3.1 Pro — A Builder's Benchmark Guide.

See also the original research grounding in research · openai/2026-05-01 and research · anthropic/2026-05-01.