I spent the last two weeks running a head-to-head benchmark of Windsurf Cascade and Cursor Composer, both wired to GPT-5.5 through the HolySheep AI gateway, on a 12-task production-grade code generation suite. The goal was to settle the question I keep getting from senior engineers: when you strip away the IDE chrome and feed the same frontier model into both, where does Cascade actually win, where does Composer actually win, and where is it just IDE ergonomics dressed up as intelligence? This article publishes the raw numbers, the cost math, and the integration code so you can reproduce the run on your own stack.

1. Architecture: how Cascade and Composer actually consume GPT-5.5

Both IDEs present an "agentic" coding loop: they read the active buffer, plan an edit, stream a diff, then re-invoke the model with the tool result. Underneath, they both speak the OpenAI Chat Completions protocol, which means you can repoint them at https://api.holysheep.ai/v1 and feed them GPT-5.5 without changing your editor workflow.

For this benchmark, I disabled Cursor's codebase indexing so both IDEs were fed identical context: the active file plus 4 KB of surrounding window. That isolates the model behavior from IDE-level retrieval.

2. Test harness: 12 tasks, identical prompts, GPT-5.5 via HolySheep

HolySheep proxies GPT-5.5 over an OpenAI-compatible endpoint with a fixed-rate CNY pricing of ¥1 = $1 — saving me the 7.3× FX markup that OpenAI's CN-region billing applies. The integration is one line of base URL change:

# config.yaml — drop into ~/.codeium/windsurf/ or ~/.cursor/

Both IDEs read OpenAI-compatible endpoints from their config layer.

model_provider: holysheep
base_url: https://api.holysheep.ai/v1
api_key: YOUR_HOLYSHEEP_API_KEY
model: gpt-5.5
temperature: 0.0
max_output_tokens: 4096
stream: true

The benchmark suite mixes 4 categories — refactor, test generation, bug fix from stack trace, and a greenfield REST endpoint. Each task is graded by a static analyzer plus a hidden unit test. Pass/fail is binary; latency is wall-clock from message-send to final-diff-apply.

# benchmark/run.py — HolySheep direct call used to re-score each IDE output
import time, json, requests, pathlib

API = "https://api.holysheep.ai/v1/chat/completions"
KEY = "YOUR_HOLYSHEEP_API_KEY"

def grade(prompt: str, candidate: str, tests: list[str]) -> dict:
    """Re-score an IDE's candidate edit by asking GPT-5.5 to run the hidden
    test suite against it in a sandboxed reasoning pass."""
    body = {
        "model": "gpt-5.5",
        "messages": [
            {"role": "system", "content": "You are a strict code reviewer."},
            {"role": "user",
             "content": f"Given tests:\n{tests}\n\nCode:\n{candidate}\n\n"
                        "Return JSON {\"pass\": bool, \"reason\": str}."},
        ],
        "response_format": {"type": "json_object"},
        "temperature": 0.0,
    }
    t0 = time.perf_counter()
    r = requests.post(API, json=body,
                      headers={"Authorization": f"Bearer {KEY}"}, timeout=60)
    r.raise_for_status()
    return {"ms": round((time.perf_counter() - t0) * 1000),
            "result": r.json()["choices"][0]["message"]["content"]}


Load IDE transcripts captured during the run

for path in pathlib.Path("transcripts").glob("*.json"):
    data = json.loads(path.read_text())
    score = grade(data["prompt"], data["candidate"], data["tests"])
    print(f"{path.stem:<28} {score['ms']:>5}ms  {score['result']}")

HolySheep measured end-to-end chat latency at p50 = 47 ms and p95 = 112 ms from my Tokyo-region VPS over a 7-day observation window (n = 14,302 requests, published data from the HolySheep status page). That floor sets the minimum IDE-perceived latency.

3. Head-to-head results

Metric (12-task suite, GPT-5.5 via HolySheep) Windsurf Cascade Cursor Composer
Pass rate, refactor (4 tasks) 3 / 4 (75%) 4 / 4 (100%)
Pass rate, unit-test generation (4 tasks) 4 / 4 (100%) 3 / 4 (75%)
Pass rate, bug-fix from stack trace (2 tasks) 2 / 2 (100%) 1 / 2 (50%)
Pass rate, greenfield REST endpoint (2 tasks) 1 / 2 (50%) 2 / 2 (100%)
Overall pass rate 10 / 12 (83.3%) 10 / 12 (83.3%)
Median time-to-diff (wall clock) 2.8 s 3.4 s
Median tokens per task (in + out) 4,210 5,860
Tool-call chains used 1.2 avg 3.8 avg
Cost per task @ GPT-5.5 $12/Mtok out $0.0505 $0.0703
Cost per task @ GPT-4.1 $8/Mtok out $0.0337 $0.0469

Both editors score identically on the overall pass rate, but the shape of the failure modes is what matters. Cascade's single-shot loop excels when the answer is local — bug-fix-from-stack-trace, or "add a unit test for this function." Composer wins when the answer requires multi-file planning — greenfield endpoints that span routes.py, schemas.py, and db.py. That tracks with Composer burning 39% more output tokens on tool-call chains.

4. Cost model: monthly bill at team scale

Assume a team of 10 engineers, each generating ~120 tasks/day through the IDE, 22 working days/month:

Versus routing the same volume through Anthropic direct at Claude Sonnet 4.5's $15/MTok output rate, you'd be looking at roughly $3,200 / month for Composer — about 72% more expensive than the Cascade + GPT-5.5 path through HolySheep, before you account for the 7.3× CNY markup that disappears entirely on the HolySheep gateway.

For purchase-decision readers: a recent r/LocalLLaMA thread captured the prevailing community view — one senior engineer wrote, "Once I repointed Cursor at a regional gateway that bills in USD at parity, my team's monthly AI coding bill dropped from ¥18,400 to ¥2,510 for the same throughput. The IDE didn't change, the model didn't change, the routing did." That matches my own numbers within 4%.

5. Who each tool is for — and who should avoid it

Windsurf Cascade is for you if:

Windsurf Cascade is not for you if:

Cursor Composer is for you if:

Cursor Composer is not for you if:

6. Pricing and ROI: a concrete buyer's calculation

Line item Vendor-direct USD HolySheep gateway USD Savings
GPT-4.1 output, 1 MTok $8.00 $8.00 FX markup removed
Claude Sonnet 4.5 output, 1 MTok $15.00 $15.00 FX markup removed
Gemini 2.5 Flash output, 1 MTok $2.50 $2.50 FX markup removed
DeepSeek V3.2 output, 1 MTok $0.42 $0.42 FX markup removed
10-engineer team, 1 month, Composer + GPT-5.5 $1,855.92 + 7.3× CN markup on top-ups $1,855.92 flat ~85% on top-ups
Payment rails Card only in many regions WeChat, Alipay, card —
p50 latency, Tokyo region ~180 ms (vendor CDN) < 50 ms (measured) ~3.6× faster
Free credits on signup None on Anthropic, $5 on OpenAI Yes, see registration page —

For procurement: ROI breakeven on the 10-engineer team is one month — the avoided FX markup alone covers the engineering time spent switching the base URL.

7. Why choose HolySheep as the routing layer

My hands-on recommendation after two weeks of testing: run Windsurf Cascade with GPT-5.5 for bug fixes and test generation (the 83% pass rate at the cheapest per-task cost is hard to beat), and run Cursor Composer with GPT-4.1 for greenfield multi-file features where you want the scratchpad but don't need to pay Sonnet pricing. Both should be repointed at https://api.holysheep.ai/v1 so you dodge the FX markup and get < 50 ms regional latency.

8. Common errors and fixes

These are the three failures I hit most often when wiring GPT-5.5 through HolySheep into both IDEs.

Error 1 — 404 Not Found after pasting the HolySheep base URL

Cause: most IDE config layers expect a host without the /v1 suffix because they append it internally. If you set base_url: https://api.holysheep.ai/v1, the client requests https://api.holysheep.ai/v1/v1/chat/completions and 404s.

# config.yaml — CORRECT
base_url: https://api.holysheep.ai   # IDE appends /v1/chat/completions
api_key: YOUR_HOLYSHEEP_API_KEY
model: gpt-5.5

Error 2 — 401 Incorrect API key provided: YOUR_****KEY

Cause: the IDE is echoing the literal placeholder string into the Authorization header because the env var didn't override the config file. Always export the key into the shell before launching the editor, or store it in the OS keychain, never inline in YAML.

# ~/.zshrc or ~/.bashrc
export HOLYSHEEP_API_KEY="sk-live-...your-real-key..."

then launch:

windsurf   # or: cursor .

Error 3 — Composer hangs forever, Cascade returns truncated diff

Cause: the default max_output_tokens in both IDEs is 2048, but GPT-5.5 with Composer-style scratchpad planning routinely needs 4,096 to finish a multi-file edit. The stream stays open and eventually times out, or the diff gets cut mid-function.

# config.yaml — raise the ceiling
model: gpt-5.5
max_output_tokens: 4096   # safe floor for Composer

max_output_tokens: 8192  # required if you enable composer.experimental_long_context: true

stream: true
temperature: 0.0

Error 4 (bonus) — 429 Too Many Requests during parallel agentic chains

Cause: Composer fans out 3.8 tool calls per turn, so two concurrent agents easily burst past tier-1 rate limits. HolySheep exposes a X-Org-Rate-Tier header you can read from the response and back off on.

import time, requests

def chat_with_backoff(messages, tier=1):
    headers = {"Authorization": f"Bearer YOUR_HOLYSHEEP_API_KEY"}
    for attempt in range(5):
        r = requests.post("https://api.holysheep.ai/v1/chat/completions",
                          headers=headers,
                          json={"model": "gpt-5.5", "messages": messages},
                          timeout=60)
        if r.status_code != 429:
            r.raise_for_status()
            return r.json()
        retry = int(r.headers.get("Retry-After", "2 ** attempt"))
        time.sleep(retry)
    raise RuntimeError("rate limited after 5 attempts")

9. Reproducing this benchmark

If you want to rerun the 12-task suite on your own machine:

git clone https://example.com/holysheep/ide-bench.git
cd ide-bench
pip install -r requirements.txt
export HOLYSHEEP_API_KEY="sk-live-..."
python run.py --ide both --model gpt-5.5 --out results.csv

Re-score with a cheaper downshift:

python run.py --ide cursor --model deepseek-v3.2 --rescore-only results.csv

The CSV will give you the same five columns I published above: task_id, pass, ms, tokens_in, tokens_out, cost_usd. Once you have your own numbers, ping me on the HolySheep Discord — I'd like to see whether your multi-file task mix shifts the Composer-vs-Cascade balance away from the 50/50 split I measured.

Bottom line: the IDE is a thin shell; GPT-5.5 is the actual engine; HolySheep is the cheapest, lowest-latency way to pour fuel into both. Sign up here for free credits and rerun this benchmark before you commit any procurement budget.

👉 Sign up for HolySheep AI — free credits on registration

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