MiMo V2.5 Pro solves the same coding tasks as Claude Opus 4.6, GPT-5.4, and Gemini 3.1 Pro while burning 40-60% fewer tokens per trajectory. That is not a minor optimization. It changes the economics of AI-assisted development.
This post breaks down the numbers, explains why token efficiency matters more than raw benchmark scores, and shows what it means for your monthly bill.
The headline number: 40-60% fewer tokens
On ClawEval, the most rigorous agentic coding benchmark available in 2026, MiMo V2.5 Pro averages roughly 70K tokens per task trajectory. Opus 4.6 uses 120K+. GPT-5.4 lands around 130K. Gemini 3.1 Pro sits in a similar range.
Same tasks. Same pass criteria. Dramatically fewer tokens.
This is not about cutting corners. MiMo V2.5 Pro hits 64% Pass^3 on ClawEval, which puts it in the same tier as models that consume nearly twice the tokens. The model simply finds shorter paths to correct solutions.
What this means in practice
Token count is not an abstract metric. Every token costs money, takes time to generate, and eats into your context window. When a model uses 70K tokens instead of 130K, you get:
- Faster completions. Fewer tokens to generate means faster wall-clock time for each task.
- Lower costs. You pay per token. 40-60% fewer tokens translates directly to 40-60% lower per-task costs.
- More context headroom. Shorter trajectories leave room for longer conversations, bigger codebases, and more complex multi-step workflows without hitting context limits.
For a detailed comparison of how this plays out against Opus 4.6 specifically, see our MiMo V2.5 Pro vs Opus 4.6 breakdown.
ClawEval results in detail
ClawEval measures agentic coding ability across real-world software engineering tasks. Pass^3 means the model must pass the same task three consecutive times, filtering out lucky one-off successes.
Here is where MiMo V2.5 Pro lands:
- Pass^3 score: 64%
- Average tokens per trajectory: ~70K
- Tool calls per trajectory: varies by task complexity (see real-world examples below)
The 64% Pass^3 is competitive with the top tier. The token count is not. It is in a class of its own.
Token comparison across models
| Model | ClawEval Pass^3 | Avg Tokens per Trajectory | Relative Token Usage |
|---|---|---|---|
| MiMo V2.5 Pro | 64% | ~70K | 1.0x (baseline) |
| Claude Opus 4.6 | 66% | ~120K | 1.7x |
| GPT-5.4 | 63% | ~130K | 1.9x |
| Gemini 3.1 Pro | 61% | ~125K | 1.8x |
Look at the Pass^3 column. The scores are within a few percentage points of each other. Now look at the token column. MiMo V2.5 Pro does comparable work with roughly half the tokens.
For a broader look at how these models compare across all dimensions, check the MiMo V2.5 Pro complete guide.
Real-world impact: two case studies
Abstract benchmarks tell part of the story. Here is what token efficiency looks like on actual tasks from ClawEval.
Compiler task (672 tool calls)
This task involves building and debugging a small compiler. MiMo V2.5 Pro completed it in 672 tool calls with a tight token budget. Opus 4.6 needed over 1,100 tool calls for the same task. GPT-5.4 exceeded 1,200.
Fewer tool calls means fewer round trips, fewer redundant explorations, and less time spent backtracking. The model identifies the right approach faster and commits to it.
Video editor task (1,868 tool calls)
A more complex task: building a functional video editor component. MiMo V2.5 Pro used 1,868 tool calls. That sounds like a lot until you see that competing models needed 2,800-3,200 calls for the same task.
The pattern holds across task complexity levels. Whether the task is small or large, MiMo V2.5 Pro consistently uses fewer tokens and fewer tool calls to reach the same outcome.
Cost implications
Let’s put dollar amounts on this. Assume you are running 100 agentic coding tasks per day through an API.
| Model | Tokens per Task | Daily Token Usage (100 tasks) | Relative Daily Cost |
|---|---|---|---|
| MiMo V2.5 Pro | ~70K | 7M | 1.0x |
| Claude Opus 4.6 | ~120K | 12M | 1.7x |
| GPT-5.4 | ~130K | 13M | 1.9x |
| Gemini 3.1 Pro | ~125K | 12.5M | 1.8x |
At scale, the savings compound fast. A team running thousands of tasks per month could save 40-60% on their AI API bill by switching to MiMo V2.5 Pro without sacrificing capability.
For strategies on tracking and controlling these costs, see our guide on monitoring AI API spending.
Token Plan pricing amplifies the savings
MiMo V2.5 Pro is available through Xiaomi’s Token Plan pricing, which offers volume discounts on top of the already lower per-task token usage. The combination is powerful:
- Lower base token consumption. 40-60% fewer tokens per task.
- Volume pricing tiers. Bulk token purchases reduce the per-token rate further.
- No capability tradeoff. You are not downgrading to a weaker model to save money.
This makes MiMo V2.5 Pro the clear cost leader for teams that run high volumes of agentic coding tasks. The per-task savings from token efficiency stack with the per-token savings from volume pricing.
For a full comparison of pricing across all major AI coding tools, see our AI coding tools pricing guide for 2026.
Why token efficiency will matter more over time
Context windows are getting larger, but they are not infinite. As agentic workflows get more complex (multi-file refactors, full-repo analysis, long debugging sessions), token budgets become the bottleneck.
A model that solves problems in 70K tokens instead of 130K gives you nearly double the effective workspace within the same context window. That is the difference between a model that can handle a 10-file refactor in one session and one that runs out of context halfway through.
Token efficiency is not just a cost metric. It is a capability multiplier.
FAQ
Does fewer tokens mean less thorough reasoning?
No. MiMo V2.5 Pro’s ClawEval Pass^3 score (64%) is within 2 percentage points of Opus 4.6 (66%). The model is not skipping steps or producing lower-quality output. It reaches correct solutions through more direct reasoning paths, avoiding the redundant exploration that inflates token counts in other models.
Can I use MiMo V2.5 Pro as a drop-in replacement for Opus 4.6?
For most agentic coding workflows, yes. The capability overlap is substantial, and the API interface follows standard conventions. The main consideration is prompt formatting. Some prompts optimized for Opus-style verbose reasoning may need minor adjustments to get the best results from MiMo V2.5 Pro’s more concise approach. See our complete guide for migration tips.
How do token savings scale with task complexity?
The 40-60% savings hold across task complexity levels. On simple tasks, MiMo V2.5 Pro might use 20K tokens where Opus uses 40K. On complex tasks like the video editor example (1,868 tool calls), the absolute savings are even larger. The ratio stays consistent because the efficiency comes from the model’s reasoning approach, not from task-specific shortcuts.