GPT-5.5 vs Claude 4.7 in OpenClaw: A Model-Per-Agent Routing Guide
GPT-5.5 landed less than 48 hours ago and r/openclaw already has 90+ comments across three threads arguing whether it beats Claude 4.7 Opus. The honest answer is that neither wins outright — they win at different jobs. Here is the routing table for OpenClaw teams, with the SOUL.md diffs and cost math to back it up.
Stop Asking Which Model Is Best
Walk through r/openclaw any day this week and the same three threads dominate the feed: a 36-comment benchmark post titled "I spent 24 hours benchmarking GPT-5.5 against Opus 4.7 in OpenClaw," a 34-comment setup thread titled "Anyone actually got GPT-5.5 working through Codex OAuth in OpenClaw?", and a 20-comment first-impressions thread titled "GPT-5.5 Released — seems to handle multi-step prompts better."
Every one of those threads eventually arrives at the same argument: one side says GPT-5.5 is a step up, the other side says Claude 4.7 is still better for their workload, and the mods pin a comment reminding everyone that benchmarks without task specification are useless. The reason the argument never resolves is that the question is wrong. "Which model is best" is not answerable in the abstract. The correct question is "which of my agents gets which model."
An OpenClaw team is not one model running one loop. It is three to seven agents, each doing a specific job, each talking to the others through plain text. The model powering each agent is a per-agent decision, not a team-wide one. The moment you accept that, the GPT-5.5 vs Claude 4.7 question stops being a debate and starts being a routing problem.
Where Each Model Actually Wins
Pulling signal from the three main r/openclaw threads and the benchmarks shared in comments, the picture is clearer than the top-level takes suggest. Users agree on more than they disagree — they just describe it in different words.
| Dimension | GPT-5.5 | Claude 4.7 Opus |
|---|---|---|
| Multi-step tool calling | Excellent (fewer dropped args) | Very good |
| Long-context reasoning | Good | Excellent |
| Editorial / long-form writing | Good | Excellent |
| Codebase-wide refactoring | Good (better than 5.4) | Excellent |
| Single-file code tasks | Excellent | Excellent |
| Structured JSON / tool-use output | Excellent | Very good |
| Latency (p50) | Faster | Slower (with thinking) |
| Prompt-caching savings | Modest | Large (5-min TTL) |
The pattern is simple. GPT-5.5 wins where the work is a chain of tool calls with structured inputs and outputs. Claude 4.7 wins where the work is long, reason-heavy, or prose-heavy. Most of the disagreement on Reddit comes from people comparing the two on mixed workloads, where one model's strength compensates for another's weakness and everything averages out.
The OpenClaw Routing Table
Here is the default routing we recommend for a typical 5-8 agent team, based on the strength map above and 242+ agent templates we maintain in the CrewClaw library.
| Agent Role | Recommended Model | Why |
|---|---|---|
| Project Manager / Orchestrator | Claude 4.7 Opus | Long context, stable delegations, big cache savings on the system prompt |
| Code Reviewer | GPT-5.5 | Tool-heavy, many file reads and diffs per turn |
| Integration / DevOps Engineer | GPT-5.5 | Shell and API chains, structured JSON, reliable tool calls |
| QA / Test Runner | GPT-5.5 | Multi-step execution, precise failure reporting |
| Long-form Writer / Editor | Claude 4.7 Opus | Prose quality, voice consistency, fewer formulaic tells |
| Researcher / Deep-reader | Claude 4.7 Opus | Long context window, reasoning over full documents |
| Data Analyst | GPT-5.5 | Query generation + tool calls against BI APIs |
| Customer Support Triage | Haiku 4.5 | Fast, cheap, narrow classification / routing task |
| Social / Repurpose Agent | Haiku 4.5 | Short transforms, high volume, cost-sensitive |
| Formatter / Router | Haiku 4.5 or Gemini Flash | Pure structural transformation, no reasoning needed |
A team built this way typically puts 2-3 agents on GPT-5.5, 2-3 on Claude 4.7 Opus, and 1-2 on Haiku 4.5 or Gemini Flash. Your PM stays on Opus because it is the only agent that sees the entire conversation, and the 5-minute cache TTL makes its token bill collapse. Your tool-heavy workers move to GPT-5.5 because they are the ones paying the penalty when a model fumbles a tool call. Your cheap agents stay cheap.
The SOUL.md Diffs
In OpenClaw, the model assigned to each agent is set in that agent's SOUL.md file. Switching an agent from Claude 4.7 to GPT-5.5 is a one-line change and a gateway restart. No code changes, no prompt rewrites — the rules in the SOUL.md stay identical.
# Agent: Project Manager
# Model: claude-opus-4-7
# Provider: anthropic
You coordinate the team. You receive requests, break them down,
delegate via @mentions, and compile status reports.
## Rules
- Always acknowledge a request within 1 turn
- Delegate to the most specialized agent available
- Never attempt the work yourself
- Summarize status every 5 turns# Agent: Code Reviewer
# Model: gpt-5.5
# Provider: openai
You review pull requests. Read the diff, load referenced files,
check for regressions, and leave inline comments.
## Rules
- Always check for test coverage on new functions
- Flag unhandled error paths
- Ignore formatting-only diffs
- Keep comments under 3 sentences# Agent: Support Triage
# Model: claude-haiku-4-5-20251001
# Provider: anthropic
You classify and route incoming support messages.
## Rules
- Output one of: billing / bug / feature / other
- If bug, extract reproduction steps
- Never reply to the user directly, only routeAfter editing, run openclaw gateway restart. The gateway picks up the new model directive, reroutes to the new provider, and keeps the agent's rules, identity, and conversation history untouched.
Cost Math: All-Opus vs Routed Team
Here is the monthly bill comparison for a 5-agent team running 500 tasks per week, assuming average task length of 4,000 input tokens and 1,500 output tokens with prompt caching enabled where the model supports it.
| Setup | PM | Code Rev. | DevOps | Writer | Support | Monthly |
|---|---|---|---|---|---|---|
| All Opus 4.7 | Opus | Opus | Opus | Opus | Opus | ~$200 |
| All GPT-5.5 | GPT-5.5 | GPT-5.5 | GPT-5.5 | GPT-5.5 | GPT-5.5 | ~$110 |
| Routed (recommended) | Opus | GPT-5.5 | GPT-5.5 | Opus | Haiku 4.5 | ~$70 |
The routed setup costs roughly one-third of all-Opus while delivering the same quality where quality matters (the PM, the writer) and better quality where tool reliability matters (the reviewer, the DevOps engineer). The all-GPT-5.5 setup is cheaper than all-Opus but loses noticeably on the PM and writer roles in community-reported quality tests.
Four Gotchas That Will Bite You
Codex OAuth drops after ~60 minutes
The most-reported GPT-5.5 issue in OpenClaw right now. If you use Codex OAuth instead of a direct API key, plan for silent session expiry. Use direct API keys for production agent teams until the upstream fix ships.
Tool-use schema drift between providers
GPT-5.5 and Claude 4.7 have slightly different tolerance for tool-call JSON. A tool that works on Opus but throws 'arguments must be a string' on GPT-5.5 usually has a nested object that needs to be serialized. Check the gateway logs, not the agent output.
Cache invalidation on model swap
Switching an agent's model does not invalidate its conversation cache on the old provider — you lose that cache, and the next turn rebuilds it on the new provider. Batch model swaps at the start of a new session, not mid-conversation.
GPT-5.5's shorter effective context
GPT-5.5 accepts a large context window on paper, but in practice agents start degrading past ~60-80K tokens. Your PM agent with a long conversation history is the one most likely to hit this, which is another reason to keep PMs on Opus.
Frequently Asked Questions
Is GPT-5.5 better than Claude 4.7 Opus for OpenClaw agents?
Neither wins outright. In the r/openclaw benchmark threads from April 23-24, 2026, the community consensus is that GPT-5.5 handles multi-step tool-calling more predictably (fewer dropped JSON arguments, fewer repeated calls) while Claude 4.7 Opus still leads on long-context reasoning, editorial writing, and codebase-wide refactoring. The right answer is not to pick one for your whole team — it is to split agents by role and put each on the model that matches the work.
My GPT-5.5 + Codex OAuth setup in OpenClaw is failing. What is going on?
This is the most-reported GPT-5.5 issue on r/openclaw right now (the Codex OAuth thread has 34 comments as of April 24, 2026). The common cause is that Codex OAuth tokens do not carry the tool-use scopes OpenClaw expects by default. The current workaround most users report: set the provider endpoint explicitly, regenerate the token with the full scope set, and avoid long-lived tokens — GPT-5.5 + Codex OAuth sessions drop silently after ~60 minutes for some users. Until the official OpenClaw docs ship an updated guide, direct API keys remain more reliable than OAuth for production agent teams.
Which OpenClaw agent roles should I switch to GPT-5.5 first?
Switch the agents whose work is multi-step and tool-heavy: code reviewer, data analyst, QA/test runner, integration engineer, DevOps. These roles chain 5-15 tool calls per turn and benefit most from GPT-5.5's improved tool-call reliability. Keep your PM/coordinator, long-form writer, and editorial review agents on Claude 4.7 Opus — they rely more on reasoning depth and prose quality than on tool-call accuracy.
Can I really mix GPT-5.5 and Claude 4.7 in the same OpenClaw team?
Yes, and it is the recommended setup for any team with more than three agents. Each agent's SOUL.md file specifies its own model directive. The OpenClaw gateway handles routing to the correct provider transparently. A PM agent on Claude 4.7 Opus can delegate to a code-review agent on GPT-5.5 and a formatter on Haiku 4.5 in the same conversation — the handoff is just text, so the model underneath each agent is irrelevant to the other agents.
What is the cost difference between an all-Claude 4.7 team and a mixed team?
At current pricing (Opus 4.7 at roughly $15/M input and $75/M output, GPT-5.5 at roughly $5/M input and $15/M output, Haiku 4.5 at $0.80/M input and $4/M output), a 5-agent team running 500 tasks/week costs approximately $180-220/month on Opus 4.7 only. The same team using GPT-5.5 for 3 tool-heavy agents and Haiku 4.5 for 1 formatter, with Opus 4.7 only on the PM, runs roughly $60-80/month — a 60-65% reduction with no measurable quality drop on community-reported workloads.
Does GPT-5.5 support OpenClaw's prompt caching?
Partially. GPT-5.5 has its own automatic prefix caching, which OpenClaw detects and takes advantage of, but the cache TTL is shorter than Anthropic's 5-minute window and the invalidation rules are different. The practical impact: Claude 4.7 Opus still delivers more cost savings per cached session for agents with long stable system prompts (like PMs with big rulebooks), while GPT-5.5 is close to break-even whether you cache or not.
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