The community broadly views GPT-5.4 as a strong candidate for execution, review, and context-heavy work, but long-sequence memory, excessive tool calls, Codex/MCP integration, and the cost of xhigh remain disputed. The safest strategy is to have models write and review each other's work according to the task.
Suitable tasks: Code review, targeted fixes, architecture planning, long-document/large-repository analysis, and Agent workflows with cross-review by Claude.
Unsuitable tasks: Automatic modifications without rollback or permission isolation; community members have reported excessively broad changes, deletion of sensitive files, and incorrect completion claims.
Applicable model versions: GPT-5.4/Codex; comparisons were mostly against Claude Sonnet/Opus 4.6 and Gemini 3.1.
Applicable clients, Agents, or APIs: Codex CLI/app, Claude Code + GPT review, OpenCode, and MCP; configurations were not standardized.
Recommended reasoning level and parameters: Multiple comments said high involved less overthinking than xhigh in their sequence tasks, but this is personal experience; your own evals should decide.
Workflows: Data enrichment pipelines, multi-step API chaining, whole-repository coding, MCP/agentic workflows, code review, and architecture planning.
Comparisons: GPT-5.4 vs. Claude Sonnet/Opus 4.6; some users also used Gemini/GLM.
Controls: There was no standardized prompt, codebase, model snapshot, tool schema, or number of repetitions; this was a post-release experience roundup.
One user said GPT-5.4 was impressive on the first task in data enrichment and multi-step API chaining, but would forget constraints from about ten steps earlier in long sequences; Sonnet was more “boring” but better at completing the original task.
Multiple users said GPT-5.4 was suitable for whole-repository work, code review, and finding edge cases missed by Opus; one recommendation was “Opus/Sonnet writes, GPT-5.4 tightens and tests, and CodeRabbit reviews.”
Other users reported that xhigh increased unnecessary tool calls, contradictions, or overthinking, and considered high more suitable for sequences longer than 3–4 steps; there were no objective statistics.
Users reported that a 1M long context felt more like automatic compression to around 200K, or that it would hang in Codex/MCP integrations; others said 5.4 clearly improved productivity across 3–4 parallel tasks.
Negative cases included excessively rewriting multiple stored procedures, incorrectly modifying SSH keys, and stopping after completing about 70% to ask whether it should continue; these are personal incident descriptions without logs.
The Reddit evidence supports putting GPT-5.4 on an “execution, review, and targeted fixes” track, while using Claude for exploration, architecture, and first drafts, followed by cross-review. Model behavior, the Codex/MCP tool layer, context assembly, and permission isolation must be diagnosed separately.
The reports are anonymous self-reports with substantial sample-selection bias, and positive and negative feedback clearly conflict.
Specific impressions may come from the client, server-side rollout, reasoning level, MCP tools, or context management rather than the model alone.
Claims such as “forgetting constraints,” “making excessive changes,” and “xhigh being worse” provide no original traces or automated evaluations, so their incidence cannot be calculated.
Choose the same repository task and API workflow, fix none/low/medium/high/xhigh in turn, and record tokens, tool calls, latency, and completion.
Run GPT-5.4 with minimum permissions, establish diff, test, and sensitive-file protections, and prohibit unconfirmed key or deployment changes.
Have GPT-5.4 review Claude's output, then have Claude review GPT's output; compare issues found, rework volume, and total cost.
Test inputs of 200K, 272K, 512K, and 1M for constraint recall, post-compression usability, and cost.
Record model failures separately from Codex/MCP control-flow hangs to avoid misclassifying integration failures as reasoning failures.
The post includes experiences with multi-step APIs/pipelines, whole-repository and parallel-Agent feedback, subjective differences between xhigh and high, the felt compression of a 1M context, and failure modes such as excessive changes and tool hangs, but provides no standardized evaluation numbers.
One comment called GPT-5.4 suitable for “rigorous peer-review,” while another warned that it would “go off script”; together, these observations show that permission controls, diffs, and test gates should be used alongside it.
GPT-5.4
