2026 is the first year in which developers can plausibly pick from three ecosystems of frontier models without feeling they are making a compromise. This post summarizes the tradeoffs across reasoning quality, coding, long context, latency, and price — tested on NovAI's OpenAI-compatible gateway.
One API key. One endpoint. Drop-in OpenAI-SDK. Compare them yourself in Playground.
Get API key → Open Playground| Claude Opus 4.7 | GPT-5 | DeepSeek-V4-Pro | |
|---|---|---|---|
| Reasoning | Best in class | Best in class | Very good |
| Coding | Excellent | Excellent | Excellent (MoE) |
| Long context | 1M tokens | 128K–1M | 128K |
| Output speed | ~60 tok/s | ~120 tok/s | ~180 tok/s |
| Input price /1M | $8.00 | $1.25 | $0.28 |
| Output price /1M | $40.00 | $10.00 | $0.40 |
| Multimodal | Vision | Vision + audio | Text only |
| Tool calling | 99.5% valid JSON | 99.6% valid JSON | 99.1% valid JSON |
Pure price is misleading — a cheap model that gets the answer wrong costs more. We normalized by our 6-scenario pass rate:
| Model | Pass rate | Avg cost /call (1K in, 500 out) | Effective $/correct |
|---|---|---|---|
| Claude Haiku 4.5 | 89% | $0.0019 | $0.0021 |
| DeepSeek-V4-Pro | 91% | $0.0005 | $0.0005 |
| Claude Sonnet 4.6 | 96% | $0.0070 | $0.0073 |
| GPT-5 | 96% | $0.0063 | $0.0066 |
| Claude Opus 4.7 | 98% | $0.0280 | $0.0286 |
DeepSeek-V4-Pro wins raw $/correct. Claude Sonnet 4.6 narrowly beats GPT-5 on pass rate × ecosystem fit. Opus 4.7 is the premium choice when 98% correctness matters enough to pay 50× over DeepSeek.
Time-to-first-token, measured from our Hong Kong gateway:
| Model | TTFT p50 | TTFT p95 | Throughput |
|---|---|---|---|
| Claude Haiku 4.5 | 180 ms | 340 ms | ~110 tok/s |
| Claude Sonnet 4.6 | 420 ms | 680 ms | ~80 tok/s |
| Claude Opus 4.7 | 720 ms | 1.3 s | ~60 tok/s |
| GPT-5 | 240 ms | 480 ms | ~120 tok/s |
| DeepSeek-V4-Pro | 160 ms | 310 ms | ~180 tok/s |
from openai import OpenAI
client = OpenAI(
api_key="YOUR_NOVAI_KEY",
base_url="https://aiapi-pro.com/v1",
)
def ask(model, prompt):
return client.chat.completions.create(
model=model,
messages=[{"role":"user","content":prompt}],
).choices[0].message.content
# Compare all three on the same prompt
for m in ["claude-opus-4-7", "gpt-5", "deepseek-v4-pro"]:
print(f"--- {m} ---")
print(ask(m, "Explain RLHF in 2 sentences."))Because we source Claude through a China-market upstream at a 65% discount, we run stricter validation than most aggregators. Before shipping any Claude model we audit: connectivity, token-overhead injection, One-API billing formula, and a 6-scenario quality regression. Full writeup: How We Validated Claude at 65% Off.
Sign up, get a $5 trial credit, run the code block above. If any model disappoints, your credit is still there.
Create account → Full pricing