Qwen Qwen3.5 397B A17b vs Xiaomi Mimo V2.5 Pro
Qwen Qwen3.5 397B A17b (OpenRouter, 262,144-token context) versus Xiaomi Mimo V2.5 Pro (OpenRouter, 1,048,576-token context). Xiaomi Mimo V2.5 Pro is cheaper by 5% on a blended token mix. Qwen Qwen3.5 397B A17b uniquely supports vision input. Xiaomi Mimo V2.5 Pro uniquely supports structured output (json schema) and prompt caching. Use the live calculator below to plug your real usage shape into both, then route the winner via Agent Command Center for shadow A/B without code changes.
Bottom line — Qwen Qwen3.5 397B A17b vs Xiaomi Mimo V2.5 Pro
Qwen Qwen3.5 397B A17b and Xiaomi Mimo V2.5 Pro are priced within 5% of each other, so cost alone is not the deciding factor. The comparison comes down to capabilities, context window, and benchmark performance on the specific task shape your workload demands.
Xiaomi Mimo V2.5 Pro ships a 1,048,576-token context window, 4.0x larger than Qwen Qwen3.5 397B A17b's 262,144 tokens. That headroom matters for long-document RAG pipelines, multi-turn agent sessions that accumulate tool-call history, and codebases where the entire repository needs to fit in a single prompt. If your average prompt stays under 262,144 tokens, the extra context on Xiaomi Mimo V2.5 Pro is insurance you may never use — and Qwen Qwen3.5 397B A17b may win on other axes.
On capability surface area, the models diverge: Qwen Qwen3.5 397B A17b supports vision input where the other does not; Xiaomi Mimo V2.5 Pro supports structured output (json schema) where the other does not; Xiaomi Mimo V2.5 Pro supports prompt caching where the other does not. These differences are binary — either your workload needs the capability or it does not. Check whether any critical path in your agent pipeline depends on a capability only one model provides before committing to a migration.
For teams evaluating both models, the recommended path is a shadow A/B test: route production traffic through an OpenAI-compatible gateway, mirror a percentage to the candidate model, score both responses with an automated evaluator (faithfulness, tool-call correctness, latency), and compare cohort-level metrics over two weeks. Future AGI Agent Command Center supports this pattern with a single `base_url` change and built-in evaluators from the ai-evaluation SDK.
Live workload comparison
Same workload run through both models. The cheaper one is highlighted.
strategy: cost-optimized
primary:
model: qwen-qwen3-5-397b-a17b
provider: openrouter
fallback:
model: xiaomi-mimo-v2-5-pro
provider: openrouter
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| Qwen Qwen3.5 397B A17b | Xiaomi Mimo V2.5 Pro | |
|---|---|---|
| Input price | $0.600/M | $1.00/M |
| Output price | $3.60/M | $3.00/M |
| Context window | 262,144 | 1,048,576 |
| Max output | 65,536 | 16,384 |
| Function calling | ✓ | ✓ |
| Vision | ✓ | — |
| Audio input | — | — |
| Reasoning | ✓ | ✓ |
| Prompt caching | — | ✓ |
| Structured output | — | ✓ |
| Pricing verified | Aug 6, 2026 | Aug 6, 2026 |
Cost at scale: monthly spend at three usage volumes
Estimated monthly cost assuming 1,000 input + 200 output tokens per request — a realistic chat-agent shape. Adjust your own usage in the calculator at the top of this page for an exact number.
| Scale | Qwen Qwen3.5 397B A17b | Xiaomi Mimo V2.5 Pro | Delta |
|---|---|---|---|
| Startup 10K requests/day | $396 /mo | $480 /mo | $84.00/mo |
| Mid-market 100K requests/day | $3,960 /mo | $4,800 /mo | $840/mo |
| Enterprise 1M requests/day | $39,600 /mo | $48,000 /mo | $8,400/mo |
At enterprise scale (1M requests/day), a difference of even ~10% in unit price compounds into thousands of dollars per month. Cached input pricing and batch tiers can shift this further — both are surfaced on each model's own page.
When to choose which
Picked from the data above — not vendor marketing. Match the rules to your workload, not the other way around.
Your workload needs long context — Xiaomi Mimo V2.5 Pro fits 1,048,576 tokens versus the other model's 262,144, enough headroom for full books, large codebases, or 100+ page documents in one shot.
Your inputs include screenshots, diagrams, or product photos — Qwen Qwen3.5 397B A17b accepts image input natively, the other doesn't.
You re-send the same large system prompt across requests — Xiaomi Mimo V2.5 Pro supports prompt caching, cutting input cost on repeat hits.
Capability diff — what you gain and lose on the swap
A specific list of what each model has that the other doesn't. If your workload depends on a row in Only Qwen Qwen3.5 397B A17b, switching to Xiaomi Mimo V2.5 Pro means re-architecting that path (and vice versa).
- • Vision input
- • Structured output (JSON schema)
- • Prompt caching
Capabilities both share (3)
- ✓ Function calling
- ✓ Streaming
- ✓ Native reasoning mode
Migration considerations
Concrete differences to wire through your stack before you flip traffic from one to the other.
- Context window changes up 300% when moving from Qwen Qwen3.5 397B A17b (262,144) to Xiaomi Mimo V2.5 Pro (1,048,576). Re-check any prompt that relies on cramming long history or documents.
- Max output tokens differ: 65,536 on Qwen Qwen3.5 397B A17b vs 16,384 on Xiaomi Mimo V2.5 Pro. Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
- Qwen Qwen3.5 397B A17b has capabilities Xiaomi Mimo V2.5 Pro lacks: Vision input. Switching to Xiaomi Mimo V2.5 Pro means re-architecting any flow that depends on these.
- Xiaomi Mimo V2.5 Pro has capabilities Qwen Qwen3.5 397B A17b lacks: Structured output (JSON schema), Prompt caching. Worth wiring through the agent design before commit.
How to A/B test Qwen Qwen3.5 397B A17b vs Xiaomi Mimo V2.5 Pro in production
If you're stuck between the two, run them side-by-side on real traffic. Four steps the Future AGI team uses internally:
- 1. Point your existing OpenAI SDK at
https://gateway.futureagi.com/v1. No code change beyondbase_urland a virtual key. - 2. Mark Qwen Qwen3.5 397B A17b primary, mirror 20% of traffic to Xiaomi Mimo V2.5 Pro in shadow mode. Both responses are logged; only the primary is served to users.
- 3. Score every shadow response with an evaluator — faithfulness, tool-call correctness, response latency, cost. Built-in evaluators in ai-evaluation cover the common axes.
- 4. Compare cohort-level metrics after two weeks. Switch primary when the candidate wins on what matters to your workload — and stays within your latency budget.
Full walkthrough on the Agent Command Center page.
FAQ — Qwen Qwen3.5 397B A17b vs Xiaomi Mimo V2.5 Pro
Which is cheaper, Qwen Qwen3.5 397B A17b or Xiaomi Mimo V2.5 Pro? ▾
Xiaomi Mimo V2.5 Pro is cheaper by roughly 5% on a blended input + output token mix. Input prices are $0.600/M for Qwen Qwen3.5 397B A17b versus $1.00/M for Xiaomi Mimo V2.5 Pro; output prices are $3.60/M versus $3.00/M. The exact savings depend on your input:output ratio — use the live calculator above to plug in your own request shape.
What is the context window of Qwen Qwen3.5 397B A17b versus Xiaomi Mimo V2.5 Pro? ▾
Qwen Qwen3.5 397B A17b supports up to 262,144 tokens of context. Xiaomi Mimo V2.5 Pro supports up to 1,048,576 tokens. Xiaomi Mimo V2.5 Pro has the larger window by a factor of 4.0x, which matters for long-document RAG, multi-turn agent sessions, and tasks that need to keep an entire codebase in working memory.
Do Qwen Qwen3.5 397B A17b and Xiaomi Mimo V2.5 Pro both support tool calling? ▾
Yes — both Qwen Qwen3.5 397B A17b and Xiaomi Mimo V2.5 Pro support native function calling. Both also support structured output via JSON schema, so an agent can be ported between them with the same tool definitions.
Can Qwen Qwen3.5 397B A17b and Xiaomi Mimo V2.5 Pro process images? ▾
Qwen Qwen3.5 397B A17b accepts native image input. Xiaomi Mimo V2.5 Pro does not — you would need to route image-heavy workloads through Qwen Qwen3.5 397B A17b or add a separate vision model in front of Xiaomi Mimo V2.5 Pro.
Which model supports prompt caching for cost reduction? ▾
Xiaomi Mimo V2.5 Pro supports prompt caching; the other does not. If your agent has a stable system prompt + retrieval context block that repeats across requests, Xiaomi Mimo V2.5 Pro gives you a 50–90% discount on those repeated input tokens at the provider level.
When should I choose Qwen Qwen3.5 397B A17b over Xiaomi Mimo V2.5 Pro? ▾
Your inputs include screenshots, diagrams, or product photos — Qwen Qwen3.5 397B A17b accepts image input natively, the other doesn't.
When should I choose Xiaomi Mimo V2.5 Pro over Qwen Qwen3.5 397B A17b? ▾
Your workload needs long context — Xiaomi Mimo V2.5 Pro fits 1,048,576 tokens versus the other model's 262,144, enough headroom for full books, large codebases, or 100+ page documents in one shot. You re-send the same large system prompt across requests — Xiaomi Mimo V2.5 Pro supports prompt caching, cutting input cost on repeat hits.
How do I A/B test Qwen Qwen3.5 397B A17b against Xiaomi Mimo V2.5 Pro in production? ▾
Route both through an OpenAI-compatible gateway like Future AGI Agent Command Center with shadow mode enabled. Send 100% of traffic to your primary model, mirror 10–20% to the candidate, score every response with an evaluator (faithfulness, tool-call correctness, response time), and compare cohort-level metrics for two weeks. Switch when the candidate wins on the metrics that matter to your workload and stays within your latency budget.