April 30, 2026 · 6 min read · ← All posts

# Xiaomi MiMo V2.5 Pro vs "V2.5 Flash": should WebBrain add both?

Short answer: yes, these look like serious candidates. They pair strong reasoning with multimodal input, which is exactly where text-only models can bottleneck a browser agent. Long answer: read on for the routing-policy sketch, then check our empirical follow-up for what actually held up.

## First, naming clarity

Xiaomi's official open model cards are MiMo-V2.5-Pro and MiMo-V2.5. In API ecosystems, people often refer to a lower-cost tier as "flash", and comparisons are frequently written as `mimo-v2.5-pro` vs `mimo-v2.5-flash`. For this post, "V2.5 Flash" means the faster/cheaper V2.5-tier experience, while "Pro" is the flagship reasoning tier.

## Why MiMo is interesting for WebBrain

- Multimodal by design. Xiaomi positions MiMo-V2.5 as native omni-modal (image / video / audio / text), which better matches WebBrain's screenshot-heavy browsing loop than a text-only model wrapped in a separate vision sub-call.
- Long-context agent work. Both tiers are published with up to 1M context claims, useful for long tool traces and replay buffers.
- Strong benchmark posture. Xiaomi's own tables show Pro very competitive against DeepSeek-V4-Pro/Flash and Kimi-K2 on reasoning and agent-style tasks.

## Public benchmark snapshots (as reported by Xiaomi)

Using Xiaomi's public release tables for MiMo V2.5, the Pro tier posts top-tier results across math/coding/reasoning suites and is generally in the same class as DeepSeek-V4-Pro and Kimi-K2 on many reasoning-heavy tests. The non-Pro V2.5 tier trails Pro but still lands in a strong efficiency band for routine agent work.

- AIME-style math + GPQA-style science reasoning. Pro is reported in the leading cluster among open frontier models.
- Code benchmarks (LiveCodeBench / SWE-style slices). Pro is competitive enough to be a realistic primary for difficult coding turns.
- Agentic / tool benchmarks. Xiaomi reports gains in agent scenarios, which matters more for WebBrain than pure single-turn chat scores.

Important caveat: these are vendor-reported numbers. Treat them as a prioritization signal, not final truth, until WebBrain's own eval harness confirms behavior. We've now run one such test — see round 3 of the vision shootout — and the picture is more nuanced than the headline benchmarks suggest.

## Pro vs Flash-style tier in practical routing

Workload | Default pick | Why
--- | --- | ---
Complex multi-step bugfixes, architecture refactors, hard planning | MiMo V2.5 Pro | Higher headroom for long-horizon reasoning and tool trajectories.
Routine coding turns, UI inspections, broad agent throughput | MiMo V2.5 ("flash" tier) | Better cost / latency profile while retaining multimodal capability.
Single-turn text-only transforms | Qwen 3.6 27B / 35B-A3B | Still excellent value and reliably strong for many WebBrain tasks.

## How this compares to today's baseline set

The tradeoff is not "best benchmark wins." For WebBrain, the better question is: which model family gives us the best reliability per dollar across mixed text + vision workflows?

On that lens:

- DeepSeek-V4-Pro / DeepSeek-V4-Flash. Very strong text reasoning, but weaker fit when screenshot-grounded understanding is first-class. WebBrain frequently needs direct visual grounding, not just text abstraction.
- MiniMax M2.7. Compelling on pure text reasoning and long-context throughput, but not the best fit when we need robust, repeatable multimodal grounding inside browser loops.
- Qwen 3.6 27B and 35B-A3B. Still best-for-buck anchors and should remain default for many text-dominant routes. The 35B-A3B is also still our pick for the dedicated vision sub-call, per round 2.
- Nemotron-3-Nano-Omni. Not too shabby at all; good budget multimodal fallback and worth keeping in the eval matrix — though English-only is a hard ceiling for multilingual users.

Recommendation: Add MiMo V2.5 Pro and MiMo V2.5 as opt-in providers behind model routing flags. If local inference is too heavy for your hardware budget, run them through OpenRouter first, then decide whether to self-host. Don't make either one the default vision sub-call yet — see round 3 for why.

## Suggested WebBrain eval plan

- Run a 50-task mixed benchmark: visual extraction, click-path planning, form completion, and recovery from ambiguous UI states.
- Track: task success, retries, hallucinated actions, tool-call efficiency, and token-normalized cost.
- Route policy: "flash" tier for first pass, automatic escalate to Pro when uncertainty or retries exceed threshold.
- Per the round 3 finding: when MiMo is in the loop, watch §6 ("Unknowns") behavior carefully — at low quants the calibrated-uncertainty signal collapses, which would defeat the whole point of escalating on uncertainty.

If these results hold across a broader workload, MiMo could become the best multimodal addition to the current Qwen-heavy stack. The follow-up post is the first data point on whether they do.

Written by Emre Sokullu. WebBrain is MIT-licensed and open on GitHub — file an issue if you've benchmarked a model worth adding to the routing matrix.