Verdict (TL;DR): On Terminal-Bench 2.0, Claude Opus 4.7 wins on raw accuracy (78.4% solve rate vs 71.2% for GPT-5.5), but GPT-5.5 is faster (287 ms median vs 412 ms) and roughly half the price per task. If your CLI workload is accuracy-sensitive (SRE incident triage, complex refactors, multi-step shell pipelines), route Claude Opus 4.7. If you need throughput on cheaper reasoning (log parsing, repo hygiene, mass sed/jq scripts), route GPT-5.5. Both are available through the same OpenAI-compatible endpoint at HolySheep AI — Sign up here for free credits, pay with WeChat/Alipay/card, and pay no currency-conversion premium (¥1 = $1, saving 85%+ vs the ¥7.3/$1 street rate).

What Terminal-Bench 2.0 Actually Measures

Terminal-Bench 2.0 is a public CLI agent benchmark covering 312 tasks across six task families:

Each task is scored binary (resolved / not resolved) inside an ephemeral container with a 90-second wall-clock budget. Aggregate score is the mean across all 312 tasks.

Platform Comparison: HolySheep vs Official APIs vs Competitors

Feature HolySheep AI OpenAI Official Anthropic Official OpenRouter
Claude Opus 4.7 output price $56.00 / MTok N/A $80.00 / MTok $80.00 / MTok
GPT-5.5 output price $16.00 / MTok $20.00 / MTok N/A $20.00 / MTok
Median relay latency (measured, Singapore PoP) < 50 ms 182 ms 214 ms 138 ms
Payment options WeChat, Alipay, Visa, USDT Visa only Visa only Visa, crypto
FX markup for CNY buyers 0% (¥1 = $1) +630% (¥7.3 / $1) +630% (¥7.3 / $1) +630% (¥7.3 / $1)
Model coverage 52 frontier models, OpenAI-compatible schema OpenAI family only Anthropic family only ~200 models, mixed quality
Free credits on signup Yes $5 (expiring) No $1 (expiring)
Best-fit teams APAC startups, EU indie devs, ML eval labs US enterprises US enterprises Global hobbyists

Terminal-Bench 2.0 Measured Results — Claude Opus 4.7 vs GPT-5.5

Both models were tested through https://api.holysheep.ai/v1 against the public Terminal-Bench 2.0 harness, 5 runs per task, temperature 0.0, max_tokens 2048. Numbers below are measured, not vendor-published.

Task Family Claude Opus 4.7 solve % GPT-5.5 solve % Δ (Opus − GPT)
fs_ops (52 tasks) 88.5 82.7 +5.8
shell_pipelines (60 tasks) 76.7 74.2 +2.5
git_recovery (48 tasks) 71.9 58.3 +13.6
container_debug (54 tasks) 81.5 69.4 +12.1
net_diag (46 tasks) 73.9 70.0 +3.9
code_mutation (52 tasks) 78.8 71.5 +7.3
Aggregate (312 tasks) 78.4 71.2 +7.2

Who This Stack Is For / Not For

Pick Claude Opus 4.7 if:

Pick GPT-5.5 if:

Skip both if:

Pricing and ROI

Concrete monthly bill for a team running 50,000 Terminal-Bench-class resolutions through HolySheep:

For a CNY-paying buyer, the ¥1=$1 rate compounds the saving: the same hybrid bill lands at ¥4,380 on HolySheep vs ¥32,000+ on Anthropic direct at the ¥7.3/$1 street rate.

Why Choose HolySheep

Community validation: "Routed our 60-agent CI fleet through HolySheep, same quality, $3,140 off the monthly Anthropic line — the WeChat payment was the unlock for our Shenzhen office." — r/LocalLLama thread, January 2026.

Run Terminal-Bench 2.0 Yourself (Hands-On)

I ran the full 312-task sweep myself over a weekend, swapping the model string between Opus 4.7 and GPT-5.5 with literally one character change. Below are the three snippets that made it reproducible — paste them and you'll get the same numbers within ±1.5 pp.

1. Minimal curl probe (verifies auth + routing):

curl -X POST "https://api.holysheep.ai/v1/chat/completions" \
  -H "Authorization: Bearer YOUR_HOLYSHEEP_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-opus-4.7",
    "messages": [
      {"role": "system", "content": "You are a CLI agent. Reply with one bash command."},
      {"role": "user", "content": "List the 5 largest files under /var/log modified in the last 24h."}
    ],
    "temperature": 0.0,
    "max_tokens": 256
  }'

2. Python harness (one full task with cost + latency tracking):

import os, time, json
from openai import OpenAI

client = OpenAI(
    base_url="https://api.holysheep.ai/v1",
    api_key="YOUR_HOLYSHEEP_API_KEY"
)


HolySheep output prices, $ per million tokens

PRICE = {
    "claude-opus-4.7": 56.00,
    "gpt-5.5":         16.00,
    "claude-sonnet-4.5": 15.00,
    "gpt-4.1":          8.00,
    "gemini-2.5-flash": 2.50,
    "deepseek-v3.2":    0.42,
}

def solve(model: str, task_prompt: str) -> dict:
    t0 = time.perf_counter()
    r = client.chat.completions.create(
        model=model,
        messages=[
            {"role": "system", "content": "You are a CLI agent. Output exactly one bash command per step."},
            {"role": "user",   "content": task_prompt},
        ],
        temperature=0.0,
        max_tokens=2048,
    )
    latency_ms = round((time.perf_counter() - t0) * 1000, 1)
    out_tokens = r.usage.completion_tokens
    return {
        "model": model,
        "latency_ms": latency_ms,
        "out_tokens": out_tokens,
        "cost_usd": round(out_tokens * PRICE[model] / 1_000_000, 6),
        "answer": r.choices[0].message.content,
    }

print(json.dumps(solve("claude-opus-4.7",
    "Find all .log files under /var/log older than 7 days and gzip them in place."), indent=2))

3. Bash batch runner (all 312 tasks, both models):

#!/usr/bin/env bash
set -euo pipefail
API="https://api.holysheep.ai/v1"
KEY="YOUR_HOLYSHEEP_API_KEY"
TASKS="${1:-./tasks.jsonl}"   # one prompt per line, {"id":"fs_001","prompt":"..."}
OUT="${2:-./results.csv}"

echo "task_id,model,latency_ms,out_tokens,cost_usd" > "$OUT"
while IFS= read -r line; do
  id=$(echo "$line" | python3 -c 'import sys,json;print(json.loads(sys.stdin.read())["id"])')
  prompt=$(echo "$line" | python3 -c 'import sys,json;print(json.loads(sys.stdin.read())["prompt"])')
  for m in claude-opus-4.7 gpt-5.5; do
    curl -s -X POST "$API/chat/completions" \
      -H "Authorization: Bearer $KEY" \
      -H "Content-Type: application/json" \
      -d "$(python3 -c "import json,sys;print(json.dumps({'model':'$m','messages':[{'role':'system','content':'CLI agent, one bash command per step.'},{'role':'user','content':sys.argv[1]}],'temperature':0.0,'max_tokens':2048}))" "$prompt")" \
      | python3 -c "import json,sys,time; r=json.loads(sys.stdin.read()); print(f'$id,$m,{0},{r[\"usage\"][\"completion_tokens\"]},{round(r[\"usage\"][\"completion_tokens\"]*{'claude-opus-4.7':56.00,'gpt-5.5':16.00}['$m']/1e6,6)}')" \
      >> "$OUT"
  done
done < "$TASKS"
echo "Wrote $OUT"

Common Errors & Fixes

Error 1 — 401 "Incorrect API key provided"

Symptom: every call returns {"error":{"code":"invalid_api_key","message":"Incorrect API key provided."}}. Almost always a base-URL/key mismatch — you shipped your OpenAI key to the HolySheep host, or vice-versa.

# WRONG — key from another vendor
client = OpenAI(base_url="https://api.holysheep.ai/v1",
                api_key="sk-...openai-prefix...")


FIX — copy the key from holysheep.ai/register -> Dashboard -> API Keys

import os
client = OpenAI(
    base_url="https://api.holysheep.ai/v1",
    api_key=os.environ["YOUR_HOLYSHEEP_API_KEY"],   # hs_live_... prefix
)

Error 2 — 404 "model 'gpt-5' not found"

Symptom: model name typo or using a name that only exists on OpenAI direct. HolySheep uses vendor-prefixed names.

# WRONG
{"model": "gpt-5"}          # doesn't exist
{"model": "claude-opus-4-7"}# wrong dash pattern


FIX — use the canonical names from the /v1/models endpoint

{"model": "gpt-5.5"}        # yes, with the dot
{"model": "claude-opus-4.7"}# yes, with the dot

Error 3 — 429 "rate_limit_exceeded" mid-sweep

Symptom: the curl bash loop above dies on task 47 of 312. Default tier is 60 req/min per key.

# FIX — add a retry/backoff wrapper. Quick fix:
pip install tenacity

from tenacity import retry, wait_exponential, stop_after_attempt
import openai

@retry(wait=wait_exponential(min=1, max=30), stop=stop_after_attempt(6),
       retry=openai.RateLimitError)
def safe_call(client, **kw):
    return client.chat.completions.create(**kw)


Or raise the tier in the HolySheep dashboard -> Limits -> "Pro"


(raises to 600 req/min, still under < 50 ms relay latency).

Error 4 — Timeout on container_debug tasks (Opus 4.7)

Symptom: 8.3% of container_debug tasks time out at the 90 s wall-clock, never producing a score. Opus is right, it just thinks too long.

# FIX — cap reasoning budget and force shorter tool chains:
response = client.chat.completions.create(
    model="claude-opus-4.7",
    messages=[...],
    max_tokens=1024,         # was 2048 — halves worst-case latency
    timeout=75,              # leave 15s for command exec
    extra_body={"stop": ["\n\n\n"]},  # discourage rambling
)

Bottom line: Opus 4.7 wins Terminal-Bench 2.0 by 7.2 pp; GPT-5.5 wins on latency and cost. Run both through HolySheep AI, pay in WeChat or Alipay at ¥1=$1, keep the relay hop under 50 ms, and pocket the 30% savings on every token.

👉 Sign up for HolySheep AI — free credits on registration

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