MiMo V2.5 Pro is not a new architecture. It is the same 1T+ parameter MoE with 42B active parameters and a 1M token context window. What changed is everything behind the weights: training data, reinforcement learning, tool-use optimization, and a new harness-awareness system that makes the model significantly better at agent tasks.

If you are already running V2 Pro in production, the upgrade question is straightforward. Here is what actually changed and whether it matters for your workload.

For a deeper look at V2.5 Pro on its own, see the MiMo V2.5 Pro complete guide.

Same architecture, different training

Both models share the same foundation:

• 1 trillion+ total parameters, 42B active (Mixture of Experts)
• 1 million token context window
• Closed-source, API-only access
• Available on Xiaomi’s API and OpenRouter

V2.5 Pro’s improvements are entirely in training. Xiaomi rebuilt the reinforcement learning pipeline with a focus on multi-step tool use, long-horizon planning, and token efficiency. The architecture did not change. The behavior did.

This matters because it means V2.5 Pro runs on the same infrastructure. No new hardware requirements, no API changes, no breaking differences in how you call it.

Benchmark comparison

The numbers tell a clear story. V2.5 Pro is a meaningful step up on every agent and coding benchmark.

The 57.2% on SWE-bench Pro is the headline number. This is the harder variant of SWE-bench that filters for more complex, multi-file issues. V2 Pro was never officially scored on it. The 64% Pass^3 on ClawEval (which measures consistency across three independent runs) shows V2.5 Pro is not just capable but reliable.

For context on how these numbers compare to Claude and GPT, see MiMo V2 Pro vs Claude vs GPT.

Token efficiency: the real upgrade

This is where V2.5 Pro saves you money even at the same price per token.

V2.5 Pro uses 40 to 60% fewer tokens to accomplish the same tasks compared to V2 Pro. Xiaomi achieved this through better planning before acting. The model generates fewer wasted tool calls, writes more targeted code edits, and avoids the “try everything” pattern that V2 Pro sometimes fell into on complex tasks.

In practice, this means:

• A task that cost $0.50 in V2 Pro tokens might cost $0.20 to $0.30 with V2.5 Pro
• Agent loops complete faster because there are fewer round trips
• Context windows fill up slower, so you hit the 1M limit less often

For high-volume production workloads, the token efficiency improvement alone justifies the upgrade.

Long-horizon tasks: 1000+ tool calls

V2 Pro could handle multi-step agent tasks, but it started to lose coherence after a few hundred tool calls. V2.5 Pro pushes this boundary dramatically.

Xiaomi demonstrated V2.5 Pro completing:

• A working compiler built from scratch in 4.3 hours
• A video editor application in 11.5 hours
• Both tasks involved 1000+ sequential tool calls

These are not cherry-picked demos. The model maintains goal coherence, tracks state across hundreds of intermediate steps, and recovers from errors without losing the thread of what it was building. V2 Pro could not reliably do this. It would drift off-task or repeat failed approaches after extended sessions.

If you are building autonomous agents that need to run for hours, V2.5 Pro is a generational improvement over V2 Pro.

Harness awareness (new in V2.5)

V2.5 Pro introduces harness awareness, a capability V2 Pro did not have. The model can detect and adapt to the evaluation or execution environment it is running in.

What this means in practice:

• The model recognizes whether it is running inside a specific agent framework, CI pipeline, or testing harness
• It adjusts its output format, tool-calling patterns, and error handling to match the environment
• It avoids common failure modes like producing output that technically works but breaks the harness parser

This is particularly useful for SWE-bench style tasks where the evaluation harness has specific expectations. But it also helps in production agent setups where the model needs to work within the constraints of your orchestration layer.

V2 Pro had no concept of this. It produced the same output regardless of execution context, which sometimes caused unnecessary failures in structured environments.

Pricing: same tier, updated Token Plan

The per-token API pricing has not changed between V2 Pro and V2.5 Pro. Both sit in the same pricing tier:

However, Xiaomi updated the Token Plan alongside the V2.5 launch with two changes:

1. No context multiplier. V2 Pro’s Token Plan applied a multiplier when you used large context windows (above 128K). V2.5 Pro’s Token Plan charges the same rate regardless of context length. If you regularly use long context, this is a meaningful price drop.
2. Night discounts. The updated Token Plan includes reduced rates during off-peak hours (typically overnight in UTC+8). If your workloads are flexible on timing, you can save an additional percentage by scheduling batch jobs during discount windows.

Combined with the 40 to 60% token efficiency improvement, V2.5 Pro is substantially cheaper to run in practice, even though the sticker price per token is identical.

Migration: swap the model tag

Migrating from V2 Pro to V2.5 Pro is trivial. Change the model identifier in your API calls:

# Before
model: mimo-v2-pro

# After
model: mimo-v2.5-pro

That is it. The API interface, request format, response format, and tool-calling schema are all identical. No code changes beyond the model string.

If you are using OpenRouter, the model ID follows the same pattern. If you are using a framework like Aider or Continue, update the model name in your config file.

There is no reason to run both models in parallel unless you want to A/B test specific tasks. V2.5 Pro is a strict upgrade.

V2.5 Standard now beats V2 Pro on some benchmarks

Here is the part that might surprise you. MiMo V2.5 Standard (the smaller, cheaper model in the V2.5 family) now outperforms V2 Pro on several agent benchmarks.

This is a direct result of the training improvements. The reinforcement learning pipeline that powers V2.5 Pro was also applied to V2.5 Standard, and the gains were large enough to push it past the previous-generation flagship on tasks like:

• Multi-step code editing
• Tool-use planning efficiency
• Error recovery in agent loops

If you are currently using V2 Pro for moderate-complexity tasks and cost is a concern, V2.5 Standard might be the better option. You get V2 Pro-level (or better) agent performance at a lower price point.

For a broader comparison of where all these models fit, check the AI model comparison.

Who should upgrade

Upgrade immediately if you:

• Run autonomous agents with 100+ tool calls per session
• Use long context windows regularly (above 128K tokens)
• Care about token costs at scale
• Need reliable multi-file code editing

You can wait if you:

• Only use MiMo for simple completions or chat
• Have a working V2 Pro setup with no performance complaints
• Are locked into a prepaid Token Plan that has not expired

Consider V2.5 Standard instead if you:

• Currently use V2 Pro for tasks that do not require the full 1M context
• Want to cut costs without losing agent capability
• Need faster inference for interactive use cases

FAQ

Is V2.5 Pro backward compatible with V2 Pro API calls? Yes. The API is identical. Change the model tag and everything else stays the same. System prompts, tool definitions, and response parsing all work without modification.

Will V2 Pro be deprecated? Xiaomi has not announced a deprecation date. V2 Pro remains available, but it is unlikely to receive further updates. New features and optimizations will go into the V2.5 line.

Can I use V2.5 Pro with MiMo V2 Flash in a routing setup? Yes, and this is a strong pattern. Route simple tasks to Flash (cheap and fast) and complex agent tasks to V2.5 Pro. The token efficiency improvements in V2.5 Pro make the cost gap between the two tiers smaller, but Flash is still 10x cheaper per token for tasks that do not need Pro-level reasoning.