| Use case | Recommended | Why |
|---|---|---|
| Coding (HumanEval, repo-level edits) | DeepSeek V4 Pro | Strongest code model in this tier; 671B MoE trained heavily on code |
| Reasoning & long context (256K+) | Doubao-Seed-2.0-Pro | Best long-context recall; 256K window; flagship reasoning |
| Translation & multilingual | Qwen3-Max | 119 languages; Alibaba's translation heritage shows |
| Ultra-low-cost batch jobs | Doubao Lite | $0.075/1M input — cheapest flagship-family model on the market |
| Agentic workflows / tool use | Qwen3-Max | Most stable function-calling among Chinese models |
| Vision (image understanding) | Qwen3-VL-Max | Best Chinese OCR + chart understanding |
| Model | Input / 1M | Output / 1M | Context | Architecture |
|---|---|---|---|---|
| Doubao-Seed-2.0-Pro | $0.40 | $2.00 | 256K | Dense + sparse hybrid |
| Doubao-Seed-2.0-Lite | $0.075 | $0.30 | 256K | Distilled |
| DeepSeek V4 Pro | $0.28 | $1.10 | 128K | 671B MoE |
| Qwen3-Max | $0.50 | $2.00 | 1M | Dense MoE |
| Qwen3-Plus | $0.20 | $0.80 | 128K | Mid-tier MoE |
Notice that Qwen3-Max gives you a 1-million-token context window — four times what Doubao Pro offers. If you're stuffing a full PDF library into a single prompt, that matters. If you're doing chat with 8K turns, it doesn't.
We gave each model the same legacy user_service.py file with three concrete refactor goals (split into service + repository, add type hints, remove duplication).
| Model | Compiles | Goals met | Bugs introduced | Verdict |
|---|---|---|---|---|
| DeepSeek V4 Pro | Yes | 3 / 3 | 0 | Best |
| Doubao Pro | Yes | 3 / 3 | 1 (silent except swallow) | Good |
| Qwen3-Max | Yes | 2 / 3 | 0 | Cautious |
Single prompt, ~110K input tokens, 6 questions about clause dependencies and edge cases.
| Model | Correct answers | Latency P50 | Cost |
|---|---|---|---|
| Doubao Pro | 6 / 6 | 14.2s | $0.045 |
| Qwen3-Max | 6 / 6 | 18.7s | $0.057 |
| DeepSeek V4 Pro | 4 / 6 (truncation at 128K) | 9.1s | $0.031 |
Doubao Pro's 256K context is the practical sweet spot — Qwen's 1M context is impressive but slower per token, and DeepSeek hits the wall at 128K.
| Model | BLEU | Human preference | Cost |
|---|---|---|---|
| Qwen3-Max | 49.4 | +8 | $0.014 |
| Doubao Pro | 47.3 | +5 | $0.012 |
| DeepSeek V4 Pro | 44.0 | 0 | $0.008 |
If your product is mostly one workload, the choice is obvious from the table above. If you have mixed traffic — and most production apps do — the right answer is route by request type:
# Pseudocode router
def pick_model(req):
if req.task == "code": return "deepseek-v4-pro"
if req.task == "translation": return "qwen3-max"
if req.tokens_in > 100_000: return "doubao-seed-2.0-pro"
if req.tier == "free": return "doubao-seed-2.0-lite"
return "doubao-seed-2.0-pro" # safe default
This is the "specialist hospital" pattern — a triage layer that picks the right model per request. Done well, you can cut total spend 40–60% versus running everything through a single flagship.
NovAI gives you Doubao, DeepSeek, and Qwen behind a single OpenAI-compatible endpoint. Switch models with one parameter — no separate vendor onboarding.
Try All Three Free →Pricing and benchmark data accurate as of May 2026. Architecture details from each vendor's published technical reports. Hands-on results from internal NovAI evaluations on real production prompts.