MiMo V2.5 Pro and GPT-5.4 land within half a percentage point of each other on SWE-bench Pro, but they get there in very different ways. Xiaomi built a token-efficient reasoning model that punches way above its weight class. OpenAI built a raw-power flagship that throws compute at every problem. This comparison breaks down where each model wins and when you should pick one over the other.
For a deeper look at the Xiaomi model on its own, see our MiMo V2.5 Pro complete guide.
Architecture Comparison
MiMo V2.5 Pro is a Mixture-of-Experts (MoE) model with roughly 70B total parameters and ~12B active per forward pass. Xiaomi designed it from the ground up for long-chain reasoning with minimal token waste. The model uses a custom reinforcement learning pipeline that rewards concise, correct reasoning traces rather than verbose chain-of-thought.
GPT-5.4 is OpenAI’s dense transformer flagship. The exact parameter count remains undisclosed, but it sits at the top of OpenAI’s model lineup. It uses a traditional dense architecture where all parameters activate on every token, giving it brute-force capability across every task category.
The key architectural difference: MiMo V2.5 Pro activates a fraction of its parameters per token, which translates directly into lower inference cost and faster response times. GPT-5.4 activates everything, which gives it a slight edge on the hardest problems but at significantly higher cost per token.
Benchmarks
The headline numbers are remarkably close, but the story underneath them is about efficiency.
| Benchmark | MiMo V2.5 Pro | GPT-5.4 | Notes |
|---|---|---|---|
| SWE-bench Pro | 57.2% | 57.7% | Near-identical accuracy |
| AIME 2025 | 83.6% | 86.1% | GPT-5.4 leads on math |
| LiveCodeBench | 72.4% | 74.0% | Slight GPT-5.4 edge |
| GPQA Diamond | 68.5% | 71.2% | GPT-5.4 stronger on science |
| Avg. tokens per SWE-bench solve | ~3,200 | ~5,800 | V2.5 uses 45% fewer tokens |
| Avg. response latency | ~8s | ~14s | V2.5 significantly faster |
GPT-5.4 wins on raw accuracy across most benchmarks, but the margins are small (1 to 3 points). MiMo V2.5 Pro closes that gap when you factor in token efficiency. It solves SWE-bench problems using roughly 45% fewer tokens than GPT-5.4, which means faster responses and lower cost per task.
For a broader view of how these models stack up against the field, check our AI model comparison.
Pricing
This is where the models diverge sharply.
| MiMo V2.5 Pro | GPT-5.4 | |
|---|---|---|
| Input tokens (per 1M) | ~$1.00 | $2.50 |
| Output tokens (per 1M) | ~$3.00 | $15.00 |
| Cost per SWE-bench solve (avg) | ~$0.013 | ~$0.095 |
| Free tier | Limited via Xiaomi API | ChatGPT Plus ($20/mo) |
| API access | Xiaomi Cloud, third-party hosts | OpenAI API |
MiMo V2.5 Pro costs roughly 5 to 7x less per completed task than GPT-5.4. The input token price is 2.5x lower, and the output token price is 5x lower. Combined with V2.5’s shorter reasoning traces, the per-task cost difference is dramatic.
For teams running thousands of API calls daily, this pricing gap compounds fast. A workload that costs $1,000/day on GPT-5.4 might run for $140 to $200/day on MiMo V2.5 Pro with nearly identical results.
Agent Capabilities
Both models are built for agentic workflows, but they approach it differently.
MiMo V2.5 Pro supports 1,000+ sequential tool calls in a single session. Xiaomi optimized the model specifically for long agentic chains where the model needs to plan, execute, observe, and iterate across many steps. It handles file editing, terminal commands, and API calls without losing context over extended sessions. The model’s token efficiency means these long chains stay affordable.
GPT-5.4 powers OpenAI’s Codex agent, which runs autonomously in sandboxed environments. Codex can spin up a full development environment, clone repos, run tests, and submit pull requests. The integration with OpenAI’s ecosystem (ChatGPT, API, Codex) is seamless. GPT-5.4 also supports parallel tool use, calling multiple tools simultaneously rather than sequentially.
| Agent Feature | MiMo V2.5 Pro | GPT-5.4 |
|---|---|---|
| Max sequential tool calls | 1,000+ | ~200 (Codex) |
| Parallel tool use | Limited | Yes |
| Sandboxed execution | No (relies on host) | Yes (Codex) |
| Context window | 128K | 128K+ |
| Long-session stability | Excellent | Good |
| Ecosystem integration | Xiaomi Cloud, open API | OpenAI suite, Codex |
If you need a model that can grind through a 500-step debugging session without ballooning your bill, MiMo V2.5 Pro is the better pick. If you want a turnkey agent that spins up its own environment and handles the full CI/CD loop, GPT-5.4 via Codex is more polished.
When to Use Which
Pick MiMo V2.5 Pro when:
- Budget matters and you are running high-volume API workloads
- You need long agentic chains (100+ tool calls per session)
- Token efficiency is a priority for latency-sensitive applications
- You want near-frontier performance at a fraction of the cost
- Your tasks are primarily code generation, debugging, or software engineering
Pick GPT-5.4 when:
- You need the absolute highest accuracy on math, science, or complex reasoning
- You want Codex’s sandboxed autonomous agent environment
- Your team already uses the OpenAI ecosystem
- You need parallel tool calling for faster agent execution
- The task requires broad multimodal capabilities (vision, audio, text)
For most software engineering workflows, MiMo V2.5 Pro offers the better value. The 0.5% accuracy gap on SWE-bench Pro is negligible compared to the 5 to 7x cost savings. GPT-5.4 justifies its premium when you need peak performance on the hardest reasoning tasks or when the Codex integration saves engineering time.
See also how GPT-5.4 compares against other frontier models: Claude Opus 4.7 vs GPT-5.4 and Kimi K2.6 vs GPT-5.4.
FAQ
Is MiMo V2.5 Pro really as good as GPT-5.4 for coding? On SWE-bench Pro, yes. MiMo V2.5 Pro scores 57.2% versus GPT-5.4’s 57.7%, a gap small enough to be within run-to-run variance. For day-to-day software engineering tasks like bug fixes, feature implementation, and code review, you will not notice a meaningful difference in quality. Where GPT-5.4 pulls ahead is on the most complex multi-file refactors and novel algorithmic problems.
Can MiMo V2.5 Pro replace GPT-5.4 in production? For many workloads, yes. If your pipeline runs thousands of coding or reasoning tasks per day, switching to MiMo V2.5 Pro can cut costs by 80% or more with minimal accuracy loss. The main blockers are ecosystem lock-in (if you depend on Codex or OpenAI-specific features) and tasks where the 1 to 3 point accuracy gap on non-coding benchmarks matters.
Which model is better for building AI agents? It depends on the agent design. MiMo V2.5 Pro excels at long sequential chains with 1,000+ tool calls, making it ideal for agents that need to iterate many times within a single task. GPT-5.4 is better for agents that need parallel execution and sandboxed environments via Codex. For cost-sensitive agent deployments at scale, MiMo V2.5 Pro is the stronger choice.
Bottom Line
MiMo V2.5 Pro and GPT-5.4 represent two philosophies in frontier AI. Xiaomi proved that a well-optimized MoE model can match a dense flagship on real-world coding benchmarks while costing a fraction of the price. OpenAI’s GPT-5.4 remains the safe pick when you need peak accuracy and a mature ecosystem.
For most teams shipping software, MiMo V2.5 Pro is the smarter default. Switch to GPT-5.4 when the task demands it, not as a blanket choice. The 2026 model landscape rewards picking the right tool for each job rather than committing to a single provider.