Which is cheaper, MiMo-V2.5-Pro or Qwen3.5-397B-A17B?
As of August 25, 2026, MiMo-V2.5-Pro is 69% cheaper on blended LLM API pricing. MiMo-V2.5-Pro costs $0.43 per million input tokens and $0.87 per million output tokens ($1.30 blended 1M-in + 1M-out). Qwen3.5-397B-A17B costs $0.60 / $3.60 per million tokens ($4.20 blended). Sources: https://anotherwrapper.com/tools/llm-pricing and https://bailian.console.aliyun.com/. The cheapest LLM for your app still depends on how many output tokens you generate.
How much does MiMo-V2.5-Pro vs Qwen3.5-397B-A17B cost per million tokens?
MiMo-V2.5-Pro Xiaomi API pricing is $0.43 input and $0.87 output per million tokens via Xiaomi. Qwen3.5-397B-A17B Qwen API pricing is $0.60 input and $3.60 output per million tokens via Qwen. Use those four numbers, not ChatGPT Plus or Claude Pro subscription prices, when you are comparing APIs.
Which is better for coding, MiMo-V2.5-Pro or Qwen3.5-397B-A17B?
MiMo-V2.5-Pro leads on SWE-bench Verified: MiMo-V2.5-Pro at 78.9% vs Qwen3.5-397B-A17B at 76.4%. SWE-bench measures real GitHub fixes; LiveCodeBench measures contest programming.
What is the context window for MiMo-V2.5-Pro vs Qwen3.5-397B-A17B?
MiMo-V2.5-Pro supports a 1M token context window, while Qwen3.5-397B-A17B supports 262K tokens. MiMo-V2.5-Pro offers a larger context window. Filling a larger window bills more input tokens, so the cheaper-per-million model can still cost more on long documents.
Which model performs better on benchmarks, MiMo-V2.5-Pro or Qwen3.5-397B-A17B?
MiMo-V2.5-Pro and Qwen3.5-397B-A17B are tied across 12 shared benchmarks. Check the table for category-level strengths. Check the comparison table for GPQA Diamond, SWE-bench, MMLU, HLE, and the other evals we track. Token price and benchmark score together are the usual LLM comparison, not either number alone.
Is MiMo-V2.5-Pro or Qwen3.5-397B-A17B better for production use?
Both MiMo-V2.5-Pro and Qwen3.5-397B-A17B are production API models. For cost-sensitive production traffic, MiMo-V2.5-Pro has better economics on the blended 1M-in + 1M-out scale. For maximum capability, weight the benchmark table for your domain. Many production stacks route cheap models for drafts and a frontier model for the hard turn, which is usually cheaper than sending everything to the expensive API.
Can I switch between MiMo-V2.5-Pro and Qwen3.5-397B-A17B in my app?
Yes. Call MiMo-V2.5-Pro through Xiaomi and Qwen3.5-397B-A17B through Qwen with separate API keys, or through a gateway that already wraps both. Keep prompts in tokens, not characters, when you estimate the invoice. AnotherWrapper templates can swap providers without rewriting auth and billing.
How accurate is this MiMo-V2.5-Pro vs Qwen3.5-397B-A17B pricing data?
List rates are the latest published Xiaomi API pricing and Qwen API pricing, quoted per million tokens. Official rate sheets: https://anotherwrapper.com/tools/llm-pricing and https://bailian.console.aliyun.com/. Prompt caching, batch APIs, and committed-use discounts can change the invoice. Benchmark scores are from official publications and independent evals, updated as new numbers land.
What is the output token limit for MiMo-V2.5-Pro and Qwen3.5-397B-A17B?
MiMo-V2.5-Pro supports up to 131K output tokens per request, while Qwen3.5-397B-A17B supports up to 64K output tokens. Output tokens are usually the expensive half of API pricing, so a higher max output cap is a capability, not a discount.
Which model has better throughput, MiMo-V2.5-Pro or Qwen3.5-397B-A17B?
Neither MiMo-V2.5-Pro nor Qwen3.5-397B-A17B has published throughput data on this page yet. Throughput (tokens per second) and time-to-first-token are why two models with the same per-million-token price can still feel different in chat and agents.
How do I switch the models in this MiMo-V2.5-Pro vs Qwen3.5-397B-A17B comparison?
Use the model pickers at the top of this LLM comparison. Search any model in the index and the URL updates. Open MiMo-V2.5-Pro or Qwen3.5-397B-A17B from the sidebar for the single-model API pricing page, or go back to the LLM pricing table to sort by cheapest blended cost.
